Ordered by severity, then signals introduced in this release. Hidden characters are shown as ⟨U+…⟩.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
inputs = processor.apply_chat_template(
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
inputs = processor.apply_chat_template(
⟨U+00A0⟩ ⟨U+00A0⟩ messages,
⟨U+00A0⟩ ⟨U+00A0⟩ padding=True,
add_generation_prompt=True,
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ messages,
⟨U+00A0⟩ ⟨U+00A0⟩ padding=True,
add_generation_prompt=True,
tokenize=True,
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
return_dict=True,
⟨U+00A0⟩ ⟨U+00A0⟩ return_tensors="pt",
).to(model.device)
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
messages = [
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
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[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/dreamstime_golden_gate_flowers.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
-
[embedded python] base64-like blob (46 bytes, compression ratio 1.24) (raw pass, confidence 0.60)
requests.get(
"https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4=", stream=True
).raw
)
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line length 2171 characters (raw pass, confidence 0.65)
*We present Qwen2.5-Omni, an end-to-end multimodal model designed to perceive diverse modalities, including text, images, audio, and video, while simultaneously generating text and natural speech responses in a streaming manner. To enable the streaming of multimodal information inputs, both audio and visual encoders ut
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line length 2804 characters (raw pass, confidence 0.65)
*Recent advancements in automatic speech translation have dramatically expanded language coverage, improved multimodal capabilities, and enabled a wide range of tasks and functionalities. That said, large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel se
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line length 2804 characters (raw pass, confidence 0.65)
*Recent advancements in automatic speech translation have dramatically expanded language coverage, improved multimodal capabilities, and enabled a wide range of tasks and functionalities. That said, large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel se
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[embedded python] `eval` called on a non-literal expression (ast pass, confidence 0.70)
ax.imshow(mask_image)
x0, y0, x1, y1 = eval(train_ds["prompt"][0])["bbox"]
ax.add_patch(
plt.Rectangle((x0, y0), x1 - x0, y1 - y0,
-
[embedded python] `eval` called on a non-literal expression (ast pass, confidence 0.70)
image = item["image"]
prompt = eval(item["prompt"])["bbox"]
inputs = self.processor(image, input_boxes=[[prompt]], return_tensors="pt")
inputs["ground_truth_mask"] = (np.array(item["mask"]) > 0).astype(np.float32)
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[embedded python] string literal compression ratio 0.955 (134 bytes) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
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[embedded python] base64-like blob (68 bytes, compression ratio 1.16) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
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identifier `π_chosen` mixes scripts: Greek + Latin (raw pass, confidence 0.80)
- a reference model calculates its own log-probs
- the loss is a function of `π_chosen`, `π_rejected`, `π_ref_chosen`, `π_ref_rejected`
None of the above fits the standard [`Trainer.compute_loss`] method.
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identifier `π_rejected` mixes scripts: Greek + Latin (raw pass, confidence 0.80)
- a reference model calculates its own log-probs
- the loss is a function of `π_chosen`, `π_rejected`, `π_ref_chosen`, `π_ref_rejected`
None of the above fits the standard [`Trainer.compute_loss`] method.
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identifier `π_ref_chosen` mixes scripts: Greek + Latin (raw pass, confidence 0.80)
- a reference model calculates its own log-probs
- the loss is a function of `π_chosen`, `π_rejected`, `π_ref_chosen`, `π_ref_rejected`
None of the above fits the standard [`Trainer.compute_loss`] method.
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identifier `π_ref_rejected` mixes scripts: Greek + Latin (raw pass, confidence 0.80)
- a reference model calculates its own log-probs
- the loss is a function of `π_chosen`, `π_rejected`, `π_ref_chosen`, `π_ref_rejected`
None of the above fits the standard [`Trainer.compute_loss`] method.
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. **[XLNet](model_doc/xlnet)** (da Google/CMU) rilasciato con il paper [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) da Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
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[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))")
source $torch_ccl_path/env/setvars.sh
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identifier `コミュニティの多くの人々に積極的にモデルを追加する力を与えようと努力しており` mixes scripts: Katakana + Hiragana + Han + Other (raw pass, confidence 0.80)
しかし、これは難しいプロジェクトであり、🤗 Transformersライブラリと実装するモデルについての深い知識が必要です。
Hugging Faceでは、コミュニティの多くの人々に積極的にモデルを追加する力を与えようと努力しており、
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identifier `つまり個々のコンポーネントのみをポーティングする問題に分割するのに役立ち` mixes scripts: Hiragana + Han + Other + Katakana (raw pass, confidence 0.80)
- 大きな問題を小さな問題に分解する、つまり個々のコンポーネントのみをポーティングする問題に分割するのに役立ち、作業を構造化するのに役立ちます
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identifier `人々が内部表現にアクセスできるようにしました` mixes scripts: Han + Other + Hiragana + Katakana (raw pass, confidence 0.80)
�つかの追加機能を組み込み、人々が内部表現にアクセスできるようにしました。これらの機能は、主にPaul Michel氏の優れた研究（[論文リンク](https://huggingface.co/papers/1905.10650)）に基づいています。具体的には、以下の機能が含まれていま… (340 bytes total)
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identifier `これらのトークンが別々の単語ではなく` mixes scripts: Hiragana + Katakana + Han + Other (raw pass, confidence 0.80)
これらのトークンが別々の単語ではなく、同じ単語の一部であることを示すために、"RA" と "M" にはダブルハッシュのプレフィックスが追加されます。
```python
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identifier `各値が各個々のトークンの予測ラベルに対応します` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
- トークン分類モデル（[`BertForTokenClassification`]）の場合、モデルは次元が `(batch_size, seq_length)` のテンソルを期待し、各値が各個々のトークンの予測ラベルに対応します。
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identifier `各値が各個々のトークンの予測ラベルに対応します` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
_length)` のテンソルを期待し、各値が各個々のトークンの予測ラベルに対応します。ここでのラベルはマスクされたトークンのトークンIDであり、他のトークンには通常 -100 などの値が設定されます。
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identifier `バッチ内の各値が各個々の画像の予測ラベルに対応します` mixes scripts: Katakana + Han + Hiragana + Other (raw pass, confidence 0.80)
- 画像分類モデルの場合（[`ViTForImageClassification`]）、モデルは次元が `(batch_size)` のテンソルを期待し、バッチ内の各値が各個々の画像の予測ラベルに対応します。
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identifier `バッチ内の各値が各個々のピクセルの予測ラベルに対応します` mixes scripts: Katakana + Han + Hiragana + Other (raw pass, confidence 0.80)
- セマンティックセグメンテーションモデルの場合（[`SegformerForSemanticSegmentation`]）、モデルは次元が `(batch_size, height, width)` のテンソルを期待し、バッチ内の各値が各個々のピクセルの予測ラベルに対応します。
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identifier `モデルは各個々の画像の予測ラベルと境界ボックスの数に対応する` mixes scripts: Katakana + Hiragana + Han + Other (raw pass, confidence 0.80)
- 物体検出モデルの場合（[`DetrForObjectDetection`]）、モデルは各個々の画像の予測ラベルと境界ボックスの数に対応する `class_labels` と `boxes` キーを持つ辞書のリストを期待します。
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identifier `各値が各個々のトークンの予測ラベルに対応します` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
- 自動音声認識モデルの場合（[`Wav2Vec2ForCTC`]）、モデルは次元が `(batch_size, target_length)` のテンソルを期待し、各値が各個々のトークンの予測ラベルに対応します。
<Tip>
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identifier `個々の単語の分類など` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
テキスト内に何があるかを理解する関連するすべてのタスク（たとえば、テキスト全体の分類、個々の単語の分類など）。
## P
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
我々は、前述のように、2つのシーケンスを2つの引数として `tokenizer` に渡すことで、このような文を自動的に生成することができます（以前のようにリストではなく）。以下のように：
```python
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
Le から公開された研究論文: [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `scores` (オプション): 各生成ステップの言語モデリング ヘッドの予測スコア
- `hidden_⟨U+200B⟩⟨U+200B⟩states` (オプション): 生成ステップごとのモデルの隠れた状態
- `attentions` (オプション): 生成ステップごとのモデルのアテンションの重み
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `scores` (オプション): 各生成ステップの言語モデリング ヘッドの予測スコア
- `hidden_⟨U+200B⟩⟨U+200B⟩states` (オプション): 生成ステップごとのモデルの隠れた状態
- `attentions` (オプション): 生成ステップごとのモデルのアテンションの重み
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
ここでは、`output_scores=True`を渡したので `scores` がありますが、`hidden_⟨U+200B⟩⟨U+200B⟩states` はありません。
`attentions` は、`output_hidden_⟨U+200B⟩⟨U+200B⟩states=True`または`output_attentions=True`を渡さなかったためです。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
ここでは、`output_scores=True`を渡したので `scores` がありますが、`hidden_⟨U+200B⟩⟨U+200B⟩states` はありません。
`attentions` は、`output_hidden_⟨U+200B⟩⟨U+200B⟩states=True`または`output_attentions=True`を渡さなかったためです。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
ここでは、`output_scores=True`を渡したので `scores` がありますが、`hidden_⟨U+200B⟩⟨U+200B⟩states` はありません。
`attentions` は、`output_hidden_⟨U+200B⟩⟨U+200B⟩states=True`または`output_attentions=True`を渡さなかったためです。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
ここでは、`output_scores=True`を渡したので `scores` がありますが、`hidden_⟨U+200B⟩⟨U+200B⟩states` はありません。
`attentions` は、`output_hidden_⟨U+200B⟩⟨U+200B⟩states=True`または`output_attentions=True`を渡さなかったためです。
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identifier `結果の分布からサンプリングする前に数々の変換を適用するほど複雑な方法まで` mixes scripts: Han + Hiragana + Katakana + Other (raw pass, confidence 0.80)
�だけのシンプルな方法から、結果の分布からサンプリングする前に数々の変換を適用するほど複雑な方法まで、あらゆる方法が考えられます。
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
で 20 トークンまで返します。我々は `generate` コールで `max_new_tokens` を手動で設定することを強くお勧めします。これにより、返される新しいトークンの最大数を制御できます。LLM（正確には、[デコーダー専用モデル](https://huggingface.co/learn
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
各派生構成クラスはモデル固有の属性を実装します。すべての構成クラスに存在する共通の属性は次のとおりです。
`hidden_⟨U+200B⟩⟨U+200B⟩size`、`num_attention_heads`、および `num_hidden_⟨U+200B⟩⟨U+200B⟩layers`。テキスト モデルはさらに以下を実装します。
`vocab_size`。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
各派生構成クラスはモデル固有の属性を実装します。すべての構成クラスに存在する共通の属性は次のとおりです。
`hidden_⟨U+200B⟩⟨U+200B⟩size`、`num_attention_heads`、および `num_hidden_⟨U+200B⟩⟨U+200B⟩layers`。テキスト モデルはさらに以下を実装します。
`vocab_size`。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
各派生構成クラスはモデル固有の属性を実装します。すべての構成クラスに存在する共通の属性は次のとおりです。
`hidden_⟨U+200B⟩⟨U+200B⟩size`、`num_attention_heads`、および `num_hidden_⟨U+200B⟩⟨U+200B⟩layers`。テキスト モデルはさらに以下を実装します。
`vocab_size`。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
各派生構成クラスはモデル固有の属性を実装します。すべての構成クラスに存在する共通の属性は次のとおりです。
`hidden_⟨U+200B⟩⟨U+200B⟩size`、`num_attention_heads`、および `num_hidden_⟨U+200B⟩⟨U+200B⟩layers`。テキスト モデルはさらに以下を実装します。
`vocab_size`。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`]) ランダムなデータ拡張 (ランダム マスキングなど) も適用します
形成されたバッチ上で。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`]) ランダムなデータ拡張 (ランダム マスキングなど) も適用します
形成されたバッチ上で。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- `stage3_param_persistence_threshold` を非常に大きな数値に設定します。たとえば、`6 * hidden_⟨U+200B⟩⟨U+200B⟩size * hidden_⟨U+200B⟩⟨U+200B⟩size` のように、最大⟨U+200B⟩⟨U+200B⟩パラメータよりも大きくなります。これにより、パラメータが GPU に保持されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`offload_optimizer`を有効にしない限り、DeepSpeed スケジューラーと HuggingFace スケジューラーを組み合わせて使用⟨U+200B⟩⟨U+200B⟩できます。
オプティマイザー (HuggingFace スケジューラーと DeepSpeed オプティマイザーの組み合わせを除く):
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`offload_optimizer`を有効にしない限り、DeepSpeed スケジューラーと HuggingFace スケジューラーを組み合わせて使用⟨U+200B⟩⟨U+200B⟩できます。
オプティマイザー (HuggingFace スケジューラーと DeepSpeed オプティマイザーの組み合わせを除く):
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
Python には、よく組み合わせて使用⟨U+200B⟩⟨U+200B⟩される 2 つのロギング システムがあります。上で説明した `logging` と `warnings` です。
これにより、特定のバケット内の警告をさらに分類できます (例: 機能またはパスの`FutureWarning`)
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
Python には、よく組み合わせて使用⟨U+200B⟩⟨U+200B⟩される 2 つのロギング システムがあります。上で説明した `logging` と `warnings` です。
これにより、特定のバケット内の警告をさらに分類できます (例: 機能またはパスの`FutureWarning`)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`bigscience/bloom-1b7`モデルをロードする必要があり、`lm_head`を除くモデル全体に⟨U+200B⟩⟨U+200B⟩適合するのに十分な GPU RAM があるとします。したがって、次のようにカスタム device_map を作成します。
```python
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`bigscience/bloom-1b7`モデルをロードする必要があり、`lm_head`を除くモデル全体に⟨U+200B⟩⟨U+200B⟩適合するのに十分な GPU RAM があるとします。したがって、次のようにカスタム device_map を作成します。
```python
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identifier `別々に` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
別々に。
- **create_optimizer** -- init で渡されなかった場合にオプティマイザーをセットアップします。
- **create_scheduler** -- init で渡されなかった場合、学習率スケジューラを設定します。
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identifier `我々はBERTのメモリ消費を低減し` mixes scripts: Han + Other + Hiragana + Latin + Katakana (raw pass, confidence 0.80)
�らの問題に対処するために、我々はBERTのメモリ消費を低減し、訓練速度を高めるための2つのパラメータ削減技術を提案します。包括的な実証的証拠は、我々の提案方法が元のBERTに比べてはるかによくスケールするモデルを生み出すこ… (339 bytes total)
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identifier `我々の提案方法が元のBERTに比べてはるかによくスケールするモデルを生み出すことを示しています` mixes scripts: Han + Other + Hiragana + Latin + Katakana (raw pass, confidence 0.80)
�す。包括的な実証的証拠は、我々の提案方法が元のBERTに比べてはるかによくスケールするモデルを生み出すことを示しています。また、文間の一貫性をモデリングに焦点を当てた自己教師あり損失を使用し、複数の文が含まれる下流タ… (340 bytes total)
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identifier `我々の最良のモデルは` mixes scripts: Han + Other + Hiragana + Katakana (raw pass, confidence 0.80)
�ことを示します。その結果、我々の最良のモデルは、BERT-largeに比べてパラメータが少ないにもかかわらず、GLUE、RACE、SQuADベンチマークで新たな最先端の結果を確立します。*
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�合させることを学習します。我々は、コーパスの規模がそのノイズを補い、このような単純な学習スキームでも最先端の表現につながることを示します。我々の視覚表現は、ImageNetやVTABなどの分類タスクへの転移において強力な性能を… (339 bytes total)
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identifier `我々の視覚表現は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�につながることを示します。我々の視覚表現は、ImageNetやVTABなどの分類タスクへの転移において強力な性能を発揮します。整合した視覚的および言語的表現は、ゼロショット画像分類を可能にし、また、より洗練されたクロスアテンシ… (339 bytes total)
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identifier `様々な画像` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�語エンコーダーの代替）は、様々な画像-テキストペアおよびテキスト-テキストペアでトレーニングされたニューラルネットワークです。CLIPのテキストエンコーダーを事前学習済みの多言語テキストエンコーダーXLM-Rに置き換えることで… (341 bytes total)
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identifier `我々の方法を検証します` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�広いタスクの評価を通じて、我々の方法を検証します。ImageNet-CN、Flicker30k-CN、COCO-CNを含む多くのタスクで新たな最先端の性能を達成しました。さらに、ほぼすべてのタスクでCLIPに非常に近い性能を得ており、これはCLIPのテキストエン… (339 bytes total)
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identifier `我々はASTを様々なオーディオ分類ベンチマークで評価し` mixes scripts: Han + Other + Hiragana + Latin + Katakana (raw pass, confidence 0.80)
ransformer（AST）を紹介します。我々はASTを様々なオーディオ分類ベンチマークで評価し、AudioSetで0.485 mAP、ESC-50で95.6%の正解率、Speech Commands V2で98.1%の正解率という新たな最先端の結果を達成しました。*
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identifier `長距離依存関係を発見するために様々なセルフアテンション機構を採用しています` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
��Transformerベースのモデルは、長距離依存関係を発見するために様々なセルフアテンション機構を採用しています。しかし、長期未来の複雑な時間的パターンによってモデルが信頼できる依存関係を見つけることを妨げられます。また、… (341 bytes total)
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identifier `我々は自己相関機構を持つ新しい分解アーキテクチャとしてAutoformerを設計しました` mixes scripts: Han + Other + Hiragana + Katakana + Latin (raw pass, confidence 0.80)
��ります。Transformerを超えて、我々は自己相関機構を持つ新しい分解アーキテクチャとしてAutoformerを設計しました。系列分解の事前処理の慣行を破り、それを深層モデルの基本的な内部ブロックとして革新します。この設計は、複雑な… (339 bytes total)
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identifier `我々の知る限りに` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
（我々の知る限りに）。 BARThez は、過去の研究から得た非常に大規模な単一言語フランス語コーパスで事前トレーニングされました
BART の摂動スキームに合わせて調整しました。既存の BERT ベースのフランス語モデルとは異なり、
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- モデルを [`EncoderDecoderModel`] と組み合わせて使用⟨U+200B⟩⟨U+200B⟩して、2 つの事前トレーニングされたモデルを活用できます。
後続の微調整のための BERT チェックポイント。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
- モデルを [`EncoderDecoderModel`] と組み合わせて使用⟨U+200B⟩⟨U+200B⟩して、2 つの事前トレーニングされたモデルを活用できます。
後続の微調整のための BERT チェックポイント。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
任意の情報に基づいて画像セグメンテーションを生成できます。
テスト時にプロンプ⟨U+200B⟩⟨U+200B⟩トが表示されます。プロンプトはテキストまたは
画像。このアプローチにより、統一されたモデルを作成できます。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
任意の情報に基づいて画像セグメンテーションを生成できます。
テスト時にプロンプ⟨U+200B⟩⟨U+200B⟩トが表示されます。プロンプトはテキストまたは
画像。このアプローチにより、統一されたモデルを作成できます。
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�ることを目的としています。我々は、大規模な言語モデルを介した会話型プログラム合成アプローチを提案します。これは、従来のアプローチで直面した広大なプログラム空間とユーザーの意図の仕様を検索するという課題に対処しま… (339 bytes total)
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
これは、計算の冗長性が存在することを意味します。したがって、我々は、新しいスパンベースの動的畳み込みを提案します。
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identifier `を設計に向けて徐々に` mixes scripts: Hiragana + Han + Other (raw pass, confidence 0.80)
畳み込みのバイアス。この作業では、設計空間を再検討し、純粋な ConvNet が達成できる限界をテストします。標準 ResNet を設計に向けて徐々に「最新化」します。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、これがエンコーダを介して送信され、同じ形状の `encoder_hidden_⟨U+200B⟩⟨U+200B⟩states` が出力されます (次のように考えることができます)。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、これがエンコーダを介して送信され、同じ形状の `encoder_hidden_⟨U+200B⟩⟨U+200B⟩states` が出力されます (次のように考えることができます)。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
同じ形状の `decoder_hidden_⟨U+200B⟩⟨U+200B⟩states` を出力します: `(batch_size, num_queries, d_model)`。次に頭が２つ
オブジェクト検出のために上部に追加されます。各オブジェクト クエリをオブジェクトの 1 つに分類するための線形レイヤー、または「いいえ」
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
同じ形状の `decoder_hidden_⟨U+200B⟩⟨U+200B⟩states` を出力します: `(batch_size, num_queries, d_model)`。次に頭が２つ
オブジェクト検出のために上部に追加されます。各オブジェクト クエリをオブジェクトの 1 つに分類するための線形レイヤー、または「いいえ」
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
あるいは、画像をバッチ処理するためにカスタムの `collat⟨U+200B⟩⟨U+200B⟩e_fn` を定義することもできます。
[`~transformers.DetrImageProcessor.pad_and_create_pixel_mask`]。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
あるいは、画像をバッチ処理するためにカスタムの `collat⟨U+200B⟩⟨U+200B⟩e_fn` を定義することもできます。
[`~transformers.DetrImageProcessor.pad_and_create_pixel_mask`]。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
DiNAT は *バックボーン* として使用できます。 「output_hidden_⟨U+200B⟩⟨U+200B⟩states = True」の場合、
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
DiNAT は *バックボーン* として使用できます。 「output_hidden_⟨U+200B⟩⟨U+200B⟩states = True」の場合、
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
`hidden_⟨U+200B⟩⟨U+200B⟩states` と `reshaped_hidden_⟨U+200B⟩⟨U+200B⟩states` の両方を出力します。 `reshape_hidden_⟨U+200B⟩⟨U+200B⟩states` は、`(batch_size, height, width, num_channels)` ではなく、`(batch, num_channels, height, width)` の形状を持っています。
ノート：
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identifier `他の人々が時間とリソースを節約できるように` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
�供することを信じています。他の人々が時間とリソースを節約できるように、モデルをコミュニティと共有することを検討することをお勧めします。
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identifier `単語とその位置を2つのベクトルで別々にエンコードする解かれた注意機構を追加しました` mixes scripts: Han + Hiragana + Katakana + Other (raw pass, confidence 0.80)
��。[DeBERTa](model_doc/deberta) は、単語とその位置を2つのベクトルで別々にエンコードする解かれた注意機構を追加しました。注意はこれらの別々のベクトルから計算されます。単語と位置の埋め込みが含まれる単一のベクトルではなく、[L… (341 bytes total)
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identifier `注意はこれらの別々のベクトルから計算されます` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
�た注意機構を追加しました。注意はこれらの別々のベクトルから計算されます。単語と位置の埋め込みが含まれる単一のベクトルではなく、[Longformer](model_doc/longformer) は、特に長いシーケンス長のドキュメントを処理するために注意をよ… (341 bytes total)
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[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
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[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))")
source $torch_ccl_path/env/setvars.sh
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identifier `各シャードは別々に並行して処理され` mixes scripts: Han + Katakana + Hiragana + Other (raw pass, confidence 0.80)
�GPUに存在します。処理中に、各シャードは別々に並行して処理され、異なるGPUで同期され、ステップの最後に結果が同期されます。これは水平並列処理と呼ばれるもので、分割は水平レベルで行われます。
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identifier `我々は` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
我々は、モデルを垂直に2つに分割し、レイヤー0から3をGPU0に配置し、レイヤー4から7をGPU1に配置しました。
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identifier `異なるハードウェア構成用の別々のガイドが用意されています` mixes scripts: Han + Hiragana + Katakana + Other (raw pass, confidence 0.80)
各セクション内には、トレーニング用のシングルGPU対マルチGPU、推論用のCPU対GPUなど、異なるハードウェア構成用の別々のガイドが用意されています。
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identifier `様々な方法があります` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
�転、リサイズ、ズームなど、様々な方法があります。ただし、増強操作によって画像の意味が変わらないように注意する必要があります。
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identifier `各画像パッチが画像全体とどのように関連しているかを徐々に学習するためにTransformerを使用する` mixes scripts: Han + Katakana + Hiragana + Other + Latin (raw pass, confidence 0.80)
2. 画像をパッチに分割し、各画像パッチが画像全体とどのように関連しているかを徐々に学習するためにTransformerを使用する。CNNが好むボトムアップアプローチとは異なり、これはぼんやりとした画像から始めて徐々に焦点を合わせるよう
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identifier `これはぼんやりとした画像から始めて徐々に焦点を合わせるようなものです` mixes scripts: Hiragana + Han + Other (raw pass, confidence 0.80)
�ップアプローチとは異なり、これはぼんやりとした画像から始めて徐々に焦点を合わせるようなものです。
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identifier `テキストはテキストのシーケンスを個々の単語やサブワードに分割して前処理されます` mixes scripts: Katakana + Hiragana + Han + Other (raw pass, confidence 0.80)
どんなNLPタスクでも、テキストはテキストのシーケンスを個々の単語やサブワードに分割して前処理されます。これらは[トークン](/glossary#token)として知られています。トークン分類は、事前に定義されたクラスのセットから各トークンに… (340 bytes total)
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identifier `これは異なるバックグラウンドを持つ人々がコミュニケーションをとるのに役立ち` mixes scripts: Hiragana + Han + Katakana + Other (raw pass, confidence 0.80)
�の言語に変換する作業です。これは異なるバックグラウンドを持つ人々がコミュニケーションをとるのに役立ち、広範な観客にコンテンツを翻訳して伝えるのに役立ち、新しい言語を学ぶのを支援する学習ツールにもなります。要約と… (339 bytes total)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
�照合器がないため、[`DataCollat⟨U+200B⟩⟨U+200B⟩orWithPadding`] を調整してサンプルのバッチを作成する必要があります。また、テキストとラベルが (データセット全体ではなく) バッチ内の最も長い要素の長さに合わせて動的に埋め込まれ、均一な長さに… (341 bytes total)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
�合器がないため、[`DataCollat⟨U+200B⟩⟨U+200B⟩orWithPadding`] を調整してサンプルのバッチを作成する必要があります。また、テキストとラベルが (データセット全体ではなく) バッチ内の最も長い要素の長さに合わせて動的に埋め込まれ、均一な長さにな… (341 bytes total)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、`DataCollat⟨U+200B⟩⟨U+200B⟩orForCTCWithPadding` をインスタンス化します。
```py
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、`DataCollat⟨U+200B⟩⟨U+200B⟩orForCTCWithPadding` をインスタンス化します。
```py
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identifier `個々のフィールドが表す内容は次のとおりです` mixes scripts: Han + Other + Hiragana + Katakana (raw pass, confidence 0.80)
個々のフィールドが表す内容は次のとおりです。
* `id`: サンプルのID
* `image`: ドキュメント画像を含む PIL.Image.Image オブジェクト
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
* サンプルをバッチ処理する関数を定義します。ここでは [`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] が適切に機能します。
* モデル、データセット、データ照合器とともにトレーニング引数を [`Trainer`] に渡します。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
* サンプルをバッチ処理する関数を定義します。ここでは [`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] が適切に機能します。
* モデル、データセット、データ照合器とともにトレーニング引数を [`Trainer`] に渡します。
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identifier `人々はさまざまな状況をナビゲートします` mixes scripts: Han + Other + Hiragana + Katakana (raw pass, confidence 0.80)
画像のキャプション付けは、特定の画像のキャプションを予測するタスクです。一般的な用途は視覚障害者を支援することです
人々はさまざまな状況をナビゲートします。たとえば、オンラインで画像コンテンツを探索します。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
これらの特定のカテゴリからのラベル付きの例。カテゴリのリストを指定し、その画像とテキストを使用して理解する
機能を利用すると、モデルは画像がどのカテゴリに属する⟨U+200B⟩⟨U+200B⟩可能性が高いかを推測できます。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
これらの特定のカテゴリからのラベル付きの例。カテゴリのリストを指定し、その画像とテキストを使用して理解する
機能を利用すると、モデルは画像がどのカテゴリに属する⟨U+200B⟩⟨U+200B⟩可能性が高いかを推測できます。
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identifier `画像を説明することで人々のコンテンツへのアクセシビリティを向上させるのに役立ちます` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
視覚障害者がさまざまな状況を乗り越えられるよう支援します。したがって、画像のキャプション
画像を説明することで人々のコンテンツへのアクセシビリティを向上させるのに役立ちます。
このガイドでは、次の方法を説明します。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合器とは異なり、`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or` はパディングなどの追加の前処理を適用しません。
```py
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合器とは異なり、`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or` はパディングなどの追加の前処理を適用しません。
```py
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合器とは異なり、`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or` はパディングなどの追加の前処理を適用しません。
```py
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。 🤗 Transformers の他のデータ照合器とは異なり、`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or` はパディングなどの追加の前処理を適用しません。
```py
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identifier `我々は生徒モデル` mixes scripts: Han + Other + Hiragana + Katakana (raw pass, confidence 0.80)
基本的に、我々は生徒モデル（ランダムに初期化されたMobileNet）が教師モデル（微調整されたビジョン変換器）を模倣することを望む。これを実現するために、まず教師と生徒からロジット出力を得る。次に、それぞれのソフトターゲッ… (339 bytes total)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`] を使用してサンプルのバッチを作成します。 *動的にパディング*する方が効率的です。
データセット全体を最大長までパディングするのではなく、照合中にバッチ内の文を最長の長さにします。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`] を使用してサンプルのバッチを作成します。 *動的にパディング*する方が効率的です。
データセット全体を最大長までパディングするのではなく、照合中にバッチ内の文を最長の長さにします。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForLanguageModeling`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
[embedded python] base64-like blob (92 bytes, compression ratio 1.05) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image
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identifier `個々の画像を正常に拡張し` mixes scripts: Han + Other + Hiragana (raw pass, confidence 0.80)
個々の画像を正常に拡張し、それらの注釈を準備しました。ただし、前処理はそうではありません。
まだ完成しています。最後のステップでは、画像をバッチ処理するためのカスタム `collat⟨U+200B⟩⟨U+200B⟩e_fn` を作成します。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
まだ完成しています。最後のステップでは、画像をバッチ処理するためのカスタム `collat⟨U+200B⟩⟨U+200B⟩e_fn` を作成します。
画像 (現在は `pixel_values`) をバッチ内の最大の画像にパディングし、対応する `pixel_mask` を作成します
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
まだ完成しています。最後のステップでは、画像をバッチ処理するためのカスタム `collat⟨U+200B⟩⟨U+200B⟩e_fn` を作成します。
画像 (現在は `pixel_values`) をバッチ内の最大の画像にパディングし、対応する `pixel_mask` を作成します
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
プロンプトの指示を変更して、LLM にこのタスクを実行させましょう。ここでは`return_full_text = False`も設定しましょう
出力にプロンプ⟨U+200B⟩⟨U+200B⟩トが含⟨U+200B⟩⟨U+200B⟩まれないようにします。
```python
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
プロンプトの指示を変更して、LLM にこのタスクを実行させましょう。ここでは`return_full_text = False`も設定しましょう
出力にプロンプ⟨U+200B⟩⟨U+200B⟩トが含⟨U+200B⟩⟨U+200B⟩まれないようにします。
```python
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
プロンプトの指示を変更して、LLM にこのタスクを実行させましょう。ここでは`return_full_text = False`も設定しましょう
出力にプロンプ⟨U+200B⟩⟨U+200B⟩トが含⟨U+200B⟩⟨U+200B⟩まれないようにします。
```python
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
プロンプトの指示を変更して、LLM にこのタスクを実行させましょう。ここでは`return_full_text = False`も設定しましょう
出力にプロンプ⟨U+200B⟩⟨U+200B⟩トが含⟨U+200B⟩⟨U+200B⟩まれないようにします。
```python
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identifier `画像の個々のピクセルにラベルまたはクラスを割り当てます` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
ク セグメンテーションでは、画像の個々のピクセルにラベルまたはクラスを割り当てます。セグメンテーションにはいくつかのタイプがありますが、セマンティック セグメンテーションの場合、同じオブジェクトの一意のインスタンス間
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. T5 がこれが要約タスクであることを認識できるように、入力の前にプロンプ⟨U+200B⟩⟨U+200B⟩トを付けます。複数の NLP タスクが可能な一部のモデルでは、特定のタスクのプロンプトが必要です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. T5 がこれが要約タスクであることを認識できるように、入力の前にプロンプ⟨U+200B⟩⟨U+200B⟩トを付けます。複数の NLP タスクが可能な一部のモデルでは、特定のタスクのプロンプトが必要です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForSeq2Seq`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForSeq2Seq`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
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identifier `言語サブセット上の英語音声で元々トレーニングされた` mixes scripts: Han + Katakana + Hiragana + Other (raw pass, confidence 0.80)
1. [VoxPopuli](https://huggingface.co/datasets/facebook/voxpopuli) のオランダ語 (`nl`) 言語サブセット上の英語音声で元々トレーニングされた [SpeechT5](../model_doc/speecht5) を微調整します。 データセット。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
すべての例からの転写を 1 つの文字列にまとめ、それを文字セットに変換します。
すべての文字起こしが一度に利用できるように、`dataset.map()`で`b⟨U+200B⟩⟨U+200B⟩atched=True`と`batch_size=-1`を必ず設定してください。
マッピング機能。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
すべての例からの転写を 1 つの文字列にまとめ、それを文字セットに変換します。
すべての文字起こしが一度に利用できるように、`dataset.map()`で`b⟨U+200B⟩⟨U+200B⟩atched=True`と`batch_size=-1`を必ず設定してください。
マッピング機能。
-
identifier `文内の個々のトークンにラベルを割り当てます` mixes scripts: Han + Hiragana + Other + Katakana (raw pass, confidence 0.80)
トークン分類では、文内の個々のトークンにラベルを割り当てます。最も一般的なトークン分類タスクの 1 つは、固有表現認識 (NER) です。 NER は、人、場所、組織など、文内の各エンティティのラベルを見つけようとします。
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orWithPadding`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orWithPadding`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. T5 がこれが翻訳タスクであることを認識できるように、入力の前にプロンプ⟨U+200B⟩⟨U+200B⟩トを付けます。複数の NLP タスクが可能な一部のモデルでは、特定のタスクのプロンプトが必要です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. T5 がこれが翻訳タスクであることを認識できるように、入力の前にプロンプ⟨U+200B⟩⟨U+200B⟩トを付けます。複数の NLP タスクが可能な一部のモデルでは、特定のタスクのプロンプトが必要です。
-
identifier `を別々にトークン化します` mixes scripts: Hiragana + Han + Other + Katakana (raw pass, confidence 0.80)
2. 英語の語彙で事前トレーニングされたトークナイザーを使用してフランス語のテキストをトークン化することはできないため、入力 (英語) とターゲット (フランス語) を別々にトークン化します。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForSeq2Seq`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
次に、[`DataCollat⟨U+200B⟩⟨U+200B⟩orForSeq2Seq`] を使用してサンプルのバッチを作成します。データセット全体を最大長までパディングするのではなく、照合中にバッチ内の最長の長さまで文を *動的にパディング* する方が効率的です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
�水平反転を組み合わせて使用⟨U+200B⟩⟨U+200B⟩します。検証および評価のデータセット変換では、ランダムなトリミングと水平反転を除き、同じ変換チェーンを維持します。これらの変換の詳細については、[PyTorchVideo の公式ドキュメント](https://pytor… (341 bytes total)
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
�平反転を組み合わせて使用⟨U+200B⟩⟨U+200B⟩します。検証および評価のデータセット変換では、ランダムなトリミングと水平反転を除き、同じ変換チェーンを維持します。これらの変換の詳細については、[PyTorchVideo の公式ドキュメント](https://pytorchv… (341 bytes total)
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
また、サンプルをまとめてバッチ処理するために使用される `collat⟨U+200B⟩⟨U+200B⟩e_fn` を定義します。各バッチは、`pixel_values` と `labels` という 2 つのキーで構成されます。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
また、サンプルをまとめてバッチ処理するために使用される `collat⟨U+200B⟩⟨U+200B⟩e_fn` を定義します。各バッチは、`pixel_values` と `labels` という 2 つのキーで構成されます。
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identifier `一部の人々` mixes scripts: Han + Hiragana + Other (raw pass, confidence 0.80)
質問に対する答えは主観的なものになる可能性があるためです。この場合、問題は "彼はどこを見ているのか？"ということです。一部の人々
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
最後のステップとして、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
最後のステップとして、[`DefaultDataCollat⟨U+200B⟩⟨U+200B⟩or`] を使用してサンプルのバッチを作成します。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
事前にトレーニングされたビジョン エンコーダーと LLM を任意に組み合わせて使用⟨U+200B⟩⟨U+200B⟩できるパラダイム (詳細については、[BLIP-2 ブログ投稿](https://huggingface.co/blog/blip-2) を参照)。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
事前にトレーニングされたビジョン エンコーダーと LLM を任意に組み合わせて使用⟨U+200B⟩⟨U+200B⟩できるパラダイム (詳細については、[BLIP-2 ブログ投稿](https://huggingface.co/blog/blip-2) を参照)。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
特定の画像の特徴をラベルに「マッピング」します。分類タスクにそのようなモデルを使用する必要がある場合、
新しいラベルのセットでは、モデルを "再調整" するために微調整が必⟨U+200B⟩⟨U+200B⟩要です。
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
特定の画像の特徴をラベルに「マッピング」します。分類タスクにそのようなモデルを使用する必要がある場合、
新しいラベルのセットでは、モデルを "再調整" するために微調整が必⟨U+200B⟩⟨U+200B⟩要です。
-
[embedded python] string literal compression ratio 0.955 (134 bytes) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
-
[embedded python] base64-like blob (68 bytes, compression ratio 1.16) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
-
[embedded python] base64-like blob (68 bytes, compression ratio 1.16) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640"
>>> im = Image.open(requests.get(url, stream=True).raw)
>>> im
-
identifier `低解像度の特徴を高解像度のピクセル埋め込みに徐々にアップサンプリングします` mixes scripts: Han + Hiragana + Katakana + Other (raw pass, confidence 0.80)
クセルデコーダー*に渡され、低解像度の特徴を高解像度のピクセル埋め込みに徐々にアップサンプリングします。ピクセルデコーダーは実際には解像度1/32、1/16、および1/8のオリジナル画像のマルチスケール特徴（低解像度と高解像度の… (339 bytes total)
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identifier `時々` mixes scripts: Han + Other (raw pass, confidence 0.80)
時々、モデルに対して `accelerate` テストを実行する必要があります。たとえば、`OPT` 実行に対してこれらのテストを実行したい場合、コマンドに `-m accelerate_tests` を追加するだけで済みます：
```bash
-
identifier `時々` mixes scripts: Han + Other (raw pass, confidence 0.80)
時々、デバイスコードでエラーが発生したという一般的な CUDA エラーに遭遇することがあります。
```
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
-
line length 3096 characters (raw pass, confidence 0.65)
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT/////
-
base64-like blob (2992 bytes, compression ratio 0.76) (raw pass, confidence 0.60)
-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29v
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[embedded python] base64-like blob (46 bytes, compression ratio 1.24) (raw pass, confidence 0.60)
```py
url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
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[embedded python] base64-like blob (46 bytes, compression ratio 1.24) (raw pass, confidence 0.60)
```py
url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
-
line length 3096 characters (raw pass, confidence 0.65)
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT/////
-
base64-like blob (2992 bytes, compression ratio 0.76) (raw pass, confidence 0.60)
-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29v
-
[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
oneccl_bindings_for_pytorch_path=$(python -c "from oneccl_bindings_for_pytorch import cwd; print(cwd)")
source $oneccl_bindings_for_pytorch_path/env/setvars.sh
-
[embedded bash] `source` called with a dynamic path — sourcing a variable script path (ast pass, confidence 0.75)
torch_ccl_path=$(python -c "import torch; import torch_ccl; import os; print(os.path.abspath(os.path.dirname(torch_ccl.__file__)))")
source $torch_ccl_path/env/setvars.sh
-
[embedded python] base64-like blob (92 bytes, compression ratio 1.05) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/HwBAsSbPBDU/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MzR8fGNhciUyMGluJTIwdGhlJTIwc3RyZWV0fGVufDB8MHx8fDE2Nzg5MDEwODg&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> image
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[embedded python] string literal compression ratio 0.955 (134 bytes) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
-
[embedded python] base64-like blob (68 bytes, compression ratio 1.16) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/g8oS8-82DxI/download?ixid=MnwxMjA3fDB8MXx0b3BpY3x8SnBnNktpZGwtSGt8fHx8fDJ8fDE2NzgxMDYwODc&force=true&w=640"
>>> image = Image.open(requests.get(url, stream=True).raw)
-
[embedded python] base64-like blob (68 bytes, compression ratio 1.16) (raw pass, confidence 0.60)
>>> url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640"
>>> im = Image.open(requests.get(url, stream=True).raw)
>>> im
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identifier `같습니디ㅏ` mixes scripts: Hangul + Other (raw pass, confidence 0.80)
GPU 요구 사항을 표로 정리하면 아래와 같습니디ㅏ:
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U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
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line length 2213 characters (raw pass, confidence 0.65)
[ <a href="./deepspeed#deepspeed-trainer-integration">DeepSpeed</a><a id="deepspeed"></a> | <a href="./deepspeed#deepspeed-installation">Installation</a><a id="installation"></a> | <a href="./deepspeed#deepspeed-multi-gpu">Deployment with multiple GPUs</a><a id="deployment-with-multiple-gpus"></a> | <a href="./deepspee
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line length 3104 characters (raw pass, confidence 0.65)
<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp
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base64-like blob (2992 bytes, compression ratio 0.76) (raw pass, confidence 0.60)
-29a79b.svg?style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAC0AAAAtCAMAAAANxBKoAAAC7lBMVEUAAADg5vYHPVgAoJH+/v76+v39/f9JbLP///9+AIgAnY3///+mcqzt8fXy9fgkXa3Ax9709fr+///9/f8qXq49qp5AaLGMwrv8/P0eW60VWawxYq8yqJzG2dytt9Wyu9elzci519Lf3O3S2efY3OrY0+Xp7PT///////+dqNCexMc6Z7AGpJeGvbenstPZ5ejQ1OfJzOLa7ejh4+/r8fT29v
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identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
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identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
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identifier `وPyTorch` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
<h3 align="center">
<p>أحدث تقنيات التعلم الآلي لـ JAX وPyTorch وTensorFlow</p>
</h3>
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identifier `وTensorFlow` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
<h3 align="center">
<p>أحدث تقنيات التعلم الآلي لـ JAX وPyTorch وTensorFlow</p>
</h3>
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
-
identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
-
base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
-
identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
-
base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
<h3 align="center">
<p>مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده⟨U+200C⟩ی پیشرفته برای استنتاج (Inference) و آموزش (Training)</p>
</h3>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
<h3 align="center">
<p>مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده⟨U+200C⟩ی پیشرفته برای استنتاج (Inference) و آموزش (Training)</p>
</h3>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
<h3 align="center">
<p>مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده⟨U+200C⟩ی پیشرفته برای استنتاج (Inference) و آموزش (Training)</p>
</h3>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
صلی برای تعریف و پیاده⟨U+200C⟩سازی برترین مدل⟨U+200C⟩های یادگیری ماشین در حوزه⟨U+200C⟩های متن، بینایی ماشین، صوت، ویدئو و مدل⟨U+200C⟩های چندوجهی شناخته می⟨U+200C⟩شود. این کتابخانه ابزاری جامع است که تمامی مر
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�یاده⟨U+200C⟩سازی برترین مدل⟨U+200C⟩های یادگیری ماشین در حوزه⟨U+200C⟩های متن، بینایی ماشین، صوت، ویدئو و مدل⟨U+200C⟩های چندوجهی شناخته می⟨U+200C⟩شود. این کتابخانه ابزاری جامع است که تمامی مراحل، از آموزش (Tra… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
یادگیری ماشین در حوزه⟨U+200C⟩های متن، بینایی ماشین، صوت، ویدئو و مدل⟨U+200C⟩های چندوجهی شناخته می⟨U+200C⟩شود. این کتابخانه ابزاری جامع است که تمامی مراحل، از آموزش (Training) تا استنتاج (Inference) را به⟨U+200C⟩
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
اشین، صوت، ویدئو و مدل⟨U+200C⟩های چندوجهی شناخته می⟨U+200C⟩شود. این کتابخانه ابزاری جامع است که تمامی مراحل، از آموزش (Training) تا استنتاج (Inference) را به⟨U+200C⟩خوبی پوشش می⟨U+200C⟩دهد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�های چندوجهی شناخته می⟨U+200C⟩شود. این کتابخانه ابزاری جامع است که تمامی مراحل، از آموزش (Training) تا استنتاج (Inference) را به⟨U+200C⟩خوبی پوشش می⟨U+200C⟩دهد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ا استنتاج (Inference) را به⟨U+200C⟩خوبی پوشش می⟨U+200C⟩دهد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ence) را به⟨U+200C⟩خوبی پوشش می⟨U+200C⟩دهد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ن کتابخانه با یکپارچه⟨U+200C⟩سازی تعریف مدل⟨U+200C⟩ها، استانداردی واحد در سراسر اکوسیستم هوش مصنوعی ایجاد کرده است. Transformers نقش یک نقطهٔ اتصال مرکزی را ایفا می⟨U+200C⟩کند؛ به این معنا که اگر تعر… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�پارچه⟨U+200C⟩سازی تعریف مدل⟨U+200C⟩ها، استانداردی واحد در سراسر اکوسیستم هوش مصنوعی ایجاد کرده است. Transformers نقش یک نقطهٔ اتصال مرکزی را ایفا می⟨U+200C⟩کند؛ به این معنا که اگر تعریف مدلی در آن پشت… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
اتصال مرکزی را ایفا می⟨U+200C⟩کند؛ به این معنا که اگر تعریف مدلی در آن پشتیبانی شود، بلافاصله با اکثر چارچوب⟨U+200C⟩های آموزش (مانند Axolotl، Unsloth، DeepSpeed و PyTorch-Lightning)، موتورهای استنتاج (مانند vLLM… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�افاصله با اکثر چارچوب⟨U+200C⟩های آموزش (مانند Axolotl، Unsloth، DeepSpeed و PyTorch-Lightning)، موتورهای استنتاج (مانند vLLM، SGLang و TGI) و کتابخانه⟨U+200C⟩های مدل⟨U+200C⟩سازی مکمل (مانند llama.cpp و mlx) که همگی از استاندارده… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
LLM، SGLang و TGI) و کتابخانه⟨U+200C⟩های مدل⟨U+200C⟩سازی مکمل (مانند llama.cpp و mlx) که همگی از استانداردهای تعریف مدل در Transformers پیروی می⟨U+200C⟩کنند، سازگار خواهد بود.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
TGI) و کتابخانه⟨U+200C⟩های مدل⟨U+200C⟩سازی مکمل (مانند llama.cpp و mlx) که همگی از استانداردهای تعریف مدل در Transformers پیروی می⟨U+200C⟩کنند، سازگار خواهد بود.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
مدل در Transformers پیروی می⟨U+200C⟩کنند، سازگار خواهد بود.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ما متعهد می⟨U+200C⟩شویم که از مدل⟨U+200C⟩های جدید و پیشرفته پشتیبانی کنیم و استفاده از آن⟨U+200C⟩ها را همگانی⟨U+200C⟩تر کنیم؛ با این هدف که تعریف مدل⟨U+200C⟩هایشان ساده، قابل⟨U+200C⟩سفارشی⟨U+200C⟩سازی و کارآمد باشد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
بیش از 1M+ [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) مربوط به Transformers در [Hugging Face Hub](https://huggingface.com/models) وجود دارد که می⟨U+200C⟩توانید از آن⟨U+200C⟩ها استفاده کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
بیش از 1M+ [model checkpoints](https://huggingface.co/models?library=transformers&sort=trending) مربوط به Transformers در [Hugging Face Hub](https://huggingface.com/models) وجود دارد که می⟨U+200C⟩توانید از آن⟨U+200C⟩ها استفاده کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
```
برای نصب این کتابخانه، به **Python 3.10+** و **PyTorch 2.5+** نیاز دارید. نصب می⟨U+200C⟩تواند از طریق `pip` یا `uv` انجام شود:
```py
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
اگر می⟨U+200C⟩خواهید جدیدترین تغییرات کتابخانه را داشته باشید یا قصد مشارکت (contribute) در پروژه را دارید، می⟨U+200C⟩توانید Transformers را از سورس (source) نصب کنید. با این حال، جدیدترین نسخه ممکن است پا
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
) در پروژه را دارید، می⟨U+200C⟩توانید Transformers را از سورس (source) نصب کنید. با این حال، جدیدترین نسخه ممکن است پایدار (stable) نباشد. اگر با خطایی برخورد کردید، با خیال راحت یک [Issue](https://github.com/huggi
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ingface/transformers/issues) در گیت⟨U+200C⟩هاب باز کنید:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
با استفاده از [Pipeline](https://huggingface.co/docs/transformers/pipeline_tutorial) API می⟨U+200C⟩توانید خیلی سریع کار با Transformers را شروع کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
لاین (Pipeline) یک کلاس سطح⟨U+200C⟩بالا برای انجام استنتاج (Inference) است که از کار با متن، صدا، تصویر و وظایف چندوجهی (multimodal) پشتیبانی می⟨U+200C⟩کند. این ابزار به⟨U+200C⟩طور خودکار مراحل پیش⟨U+200C⟩پردازش ورودی
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�هی (multimodal) پشتیبانی می⟨U+200C⟩کند. این ابزار به⟨U+200C⟩طور خودکار مراحل پیش⟨U+200C⟩پردازش ورودی را انجام می⟨U+200C⟩دهد و خروجی مناسب را برمی⟨U+200C⟩گرداند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ی می⟨U+200C⟩کند. این ابزار به⟨U+200C⟩طور خودکار مراحل پیش⟨U+200C⟩پردازش ورودی را انجام می⟨U+200C⟩دهد و خروجی مناسب را برمی⟨U+200C⟩گرداند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
⟨U+200C⟩طور خودکار مراحل پیش⟨U+200C⟩پردازش ورودی را انجام می⟨U+200C⟩دهد و خروجی مناسب را برمی⟨U+200C⟩گرداند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
دازش ورودی را انجام می⟨U+200C⟩دهد و خروجی مناسب را برمی⟨U+200C⟩گرداند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
� و خروجی مناسب را برمی⟨U+200C⟩گرداند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�تن استفاده شود. مدل به⟨U+200C⟩صورت خودکار دانلود و در حافظهٔ کش (cache) ذخیره می⟨U+200C⟩شود تا در اجراهای بعدی بتوان به⟨U+200C⟩راحتی دوباره از آن استفاده کرد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�افظهٔ کش (cache) ذخیره می⟨U+200C⟩شود تا در اجراهای بعدی بتوان به⟨U+200C⟩راحتی دوباره از آن استفاده کرد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
اجراهای بعدی بتوان به⟨U+200C⟩راحتی دوباره از آن استفاده کرد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
> [!TIP]
همچنین می⟨U+200C⟩توانید مستقیماً از خط فرمان با یک مدل چت کنید، مادامی که [`transformers serve` در حال اجرا باشد](https://huggingface.co/docs/transformers/main/en/serving).
> ```shell
> transformers chat Qwen/Qwen2.5-0.5B-Instruct
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
مثال⟨U+200C⟩های زیر را بسط دهید تا ببینید `Pipeline` چگونه برای روش⟨U+200C⟩ها و وظایف مختلف کار می⟨U+200C⟩کند.
<details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
مثال⟨U+200C⟩های زیر را بسط دهید تا ببینید `Pipeline` چگونه برای روش⟨U+200C⟩ها و وظایف مختلف کار می⟨U+200C⟩کند.
<details>
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
مثال⟨U+200C⟩های زیر را بسط دهید تا ببینید `Pipeline` چگونه برای روش⟨U+200C⟩ها و وظایف مختلف کار می⟨U+200C⟩کند.
<details>
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
<summary>
طبقه⟨U+200C⟩بندی تصویر (Image Classification):
</summary>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. مدل⟨U+200C⟩های پیشرفته و آسان برای استفاده:
- عملکرد بالا در درک و تولید زبان طبیعی، بینایی ماشین، صوت، ویدیو و وظایف چندوجهی (Multimodal).
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- ورود آسان و بدون پیچیدگی برای پژوهشگران، مهندسان و توسعه⟨U+200C⟩دهندگان. (کاهش سد ورود برای متخصصان)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- مفاهیم انتزاعی (Abstractions) اندک برای کاربر، به گونه⟨U+200C⟩ای که تنها با یادگیری سه کلاس اصلی می⟨U+200C⟩توان از آن استفاده کرد.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- مفاهیم انتزاعی (Abstractions) اندک برای کاربر، به گونه⟨U+200C⟩ای که تنها با یادگیری سه کلاس اصلی می⟨U+200C⟩توان از آن استفاده کرد.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک رابط برنامه⟨U+200C⟩نویسی (API) واحد و یکپارچه برای بهره⟨U+200C⟩گیری از تمامی مدل⟨U+200C⟩های پیش⟨U+200C⟩آموزش⟨U+200C⟩دیده ما.
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک رابط برنامه⟨U+200C⟩نویسی (API) واحد و یکپارچه برای بهره⟨U+200C⟩گیری از تمامی مدل⟨U+200C⟩های پیش⟨U+200C⟩آموزش⟨U+200C⟩دیده ما.
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک رابط برنامه⟨U+200C⟩نویسی (API) واحد و یکپارچه برای بهره⟨U+200C⟩گیری از تمامی مدل⟨U+200C⟩های پیش⟨U+200C⟩آموزش⟨U+200C⟩دیده ما.
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک رابط برنامه⟨U+200C⟩نویسی (API) واحد و یکپارچه برای بهره⟨U+200C⟩گیری از تمامی مدل⟨U+200C⟩های پیش⟨U+200C⟩آموزش⟨U+200C⟩دیده ما.
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک رابط برنامه⟨U+200C⟩نویسی (API) واحد و یکپارچه برای بهره⟨U+200C⟩گیری از تمامی مدل⟨U+200C⟩های پیش⟨U+200C⟩آموزش⟨U+200C⟩دیده ما.
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. کاهش هزینه⟨U+200C⟩های محاسباتی و ردپای کربن:
- به⟨U+200C⟩جای آموزش مدل از ابتدا، از مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده استفاده و آن⟨U+200C⟩ها را به اشتراک بگذارید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- به⟨U+200C⟩جای آموزش مدل از ابتدا، از مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده استفاده و آن⟨U+200C⟩ها را به اشتراک بگذارید.
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- به⟨U+200C⟩جای آموزش مدل از ابتدا، از مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده استفاده و آن⟨U+200C⟩ها را به اشتراک بگذارید.
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- به⟨U+200C⟩جای آموزش مدل از ابتدا، از مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده استفاده و آن⟨U+200C⟩ها را به اشتراک بگذارید.
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- به⟨U+200C⟩جای آموزش مدل از ابتدا، از مدل⟨U+200C⟩های از پیش آموزش⟨U+200C⟩دیده استفاده و آن⟨U+200C⟩ها را به اشتراک بگذارید.
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
- ده⟨U+200C⟩ها معماری مختلف مدل به همراه بیش از یک میلیون چک⟨U+200C⟩پوینتِ از پیش آموزش⟨U+200C⟩دیده در تمام حوزه⟨U+200C⟩های چندوجهی(Multimodal).
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
- ده⟨U+200C⟩ها معماری مختلف مدل به همراه بیش از یک میلیون چک⟨U+200C⟩پوینتِ از پیش آموزش⟨U+200C⟩دیده در تمام حوزه⟨U+200C⟩های چندوجهی(Multimodal).
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
- ده⟨U+200C⟩ها معماری مختلف مدل به همراه بیش از یک میلیون چک⟨U+200C⟩پوینتِ از پیش آموزش⟨U+200C⟩دیده در تمام حوزه⟨U+200C⟩های چندوجهی(Multimodal).
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
- ده⟨U+200C⟩ها معماری مختلف مدل به همراه بیش از یک میلیون چک⟨U+200C⟩پوینتِ از پیش آموزش⟨U+200C⟩دیده در تمام حوزه⟨U+200C⟩های چندوجهی(Multimodal).
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- زمان محاسباتی و هزینه⟨U+200C⟩های تولید را کاهش دهید.
- ده⟨U+200C⟩ها معماری مختلف مدل به همراه بیش از یک میلیون چک⟨U+200C⟩پوینتِ از پیش آموزش⟨U+200C⟩دیده در تمام حوزه⟨U+200C⟩های چندوجهی(Multimodal).
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- مدل⟨U+200C⟩های پیشرفته (State‑of‑the‑Art) را تنها با سه خط کد آموزش دهید.
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- یک مدل واحد را به⟨U+200C⟩دلخواه بین فریم⟨U+200C⟩ورک⟨U+200C⟩های PyTorch، JAX و TensorFlow 2.0 جابه⟨U+200C⟩جا کنید.
- برای مراحل آموزش، ارزیابی و استقرار در محیط تولید، مناسب⟨U+200C⟩ترین فریم⟨U+200C⟩ورک را انتخاب کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. قابلیت شخصی⟨U+200C⟩سازی:
- برای هر معماری مدل، نمونه⟨U+200C⟩هایی ارائه می⟨U+200C⟩کنیم تا بتوانید نتایج منتشرشده توسط نویسندگان اصلی آن را بازتولید کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. قابلیت شخصی⟨U+200C⟩سازی:
- برای هر معماری مدل، نمونه⟨U+200C⟩هایی ارائه می⟨U+200C⟩کنیم تا بتوانید نتایج منتشرشده توسط نویسندگان اصلی آن را بازتولید کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. قابلیت شخصی⟨U+200C⟩سازی:
- برای هر معماری مدل، نمونه⟨U+200C⟩هایی ارائه می⟨U+200C⟩کنیم تا بتوانید نتایج منتشرشده توسط نویسندگان اصلی آن را بازتولید کنید.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- اجزای داخلی مدل⟨U+200C⟩ها تا حد امکان به⟨U+200C⟩صورت سازگار و یکسان در دسترس قرار داده شده⟨U+200C⟩اند.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- اجزای داخلی مدل⟨U+200C⟩ها تا حد امکان به⟨U+200C⟩صورت سازگار و یکسان در دسترس قرار داده شده⟨U+200C⟩اند.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- اجزای داخلی مدل⟨U+200C⟩ها تا حد امکان به⟨U+200C⟩صورت سازگار و یکسان در دسترس قرار داده شده⟨U+200C⟩اند.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- فایل⟨U+200C⟩های مدل را می⟨U+200C⟩توان برای انجام آزمایش⟨U+200C⟩های سریع، مستقل از خود کتابخانه نیز استفاده کرد.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- فایل⟨U+200C⟩های مدل را می⟨U+200C⟩توان برای انجام آزمایش⟨U+200C⟩های سریع، مستقل از خود کتابخانه نیز استفاده کرد.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- فایل⟨U+200C⟩های مدل را می⟨U+200C⟩توان برای انجام آزمایش⟨U+200C⟩های سریع، مستقل از خود کتابخانه نیز استفاده کرد.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- این کتابخانه یک جعبه⟨U+200C⟩ابزار ماژولار برای شبکه⟨U+200C⟩های عصبی عمومی نیست؛ کدهای مدل⟨U+200C⟩ها عمداً برای محققان کمتر انتزاعی شده⟨U+200C⟩اند.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- این کتابخانه یک جعبه⟨U+200C⟩ابزار ماژولار برای شبکه⟨U+200C⟩های عصبی عمومی نیست؛ کدهای مدل⟨U+200C⟩ها عمداً برای محققان کمتر انتزاعی شده⟨U+200C⟩اند.
-
U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- این کتابخانه یک جعبه⟨U+200C⟩ابزار ماژولار برای شبکه⟨U+200C⟩های عصبی عمومی نیست؛ کدهای مدل⟨U+200C⟩ها عمداً برای محققان کمتر انتزاعی شده⟨U+200C⟩اند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- این کتابخانه یک جعبه⟨U+200C⟩ابزار ماژولار برای شبکه⟨U+200C⟩های عصبی عمومی نیست؛ کدهای مدل⟨U+200C⟩ها عمداً برای محققان کمتر انتزاعی شده⟨U+200C⟩اند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- رابط آموزش (Training API) بهینه⟨U+200C⟩سازی شده برای مدل⟨U+200C⟩های Transformers است. برای حلقه⟨U+200C⟩های عمومی یادگیری ماشین، بهتر است از کتابخانه⟨U+200C⟩هایی مانند [Accelerate](https://huggingface.co/docs/accelerate) استفاده کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- رابط آموزش (Training API) بهینه⟨U+200C⟩سازی شده برای مدل⟨U+200C⟩های Transformers است. برای حلقه⟨U+200C⟩های عمومی یادگیری ماشین، بهتر است از کتابخانه⟨U+200C⟩هایی مانند [Accelerate](https://huggingface.co/docs/accelerate) استفاده کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- رابط آموزش (Training API) بهینه⟨U+200C⟩سازی شده برای مدل⟨U+200C⟩های Transformers است. برای حلقه⟨U+200C⟩های عمومی یادگیری ماشین، بهتر است از کتابخانه⟨U+200C⟩هایی مانند [Accelerate](https://huggingface.co/docs/accelerate) استفاده کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- رابط آموزش (Training API) بهینه⟨U+200C⟩سازی شده برای مدل⟨U+200C⟩های Transformers است. برای حلقه⟨U+200C⟩های عمومی یادگیری ماشین، بهتر است از کتابخانه⟨U+200C⟩هایی مانند [Accelerate](https://huggingface.co/docs/accelerate) استفاده کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
s تنها یک ابزار برای به⟨U+200C⟩کارگیری مدل⟨U+200C⟩های ازپیش⟨U+200C⟩آموزش⟨U+200C⟩دیده نیست، بلکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کت… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ر برای به⟨U+200C⟩کارگیری مدل⟨U+200C⟩های ازپیش⟨U+200C⟩آموزش⟨U+200C⟩دیده نیست، بلکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کتابخانه به توس… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ارگیری مدل⟨U+200C⟩های ازپیش⟨U+200C⟩آموزش⟨U+200C⟩دیده نیست، بلکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کتابخانه به توسعه⟨U+200C⟩دهندگا… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
مدل⟨U+200C⟩های ازپیش⟨U+200C⟩آموزش⟨U+200C⟩دیده نیست، بلکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کتابخانه به توسعه⟨U+200C⟩دهندگان، پژوهش
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�دیده نیست، بلکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کتابخانه به توسعه⟨U+200C⟩دهندگان، پژوهشگران، دانشجویان، اسات… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
لکه جامعه⟨U+200C⟩ای از پروژه⟨U+200C⟩هاست که پیرامون آن و همچنین پیرامون **Hugging Face Hub** شکل گرفته است. هدف ما این است که این کتابخانه به توسعه⟨U+200C⟩دهندگان، پژوهشگران، دانشجویان، اساتید، مهندسان و
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
این کتابخانه به توسعه⟨U+200C⟩دهندگان، پژوهشگران، دانشجویان، اساتید، مهندسان و هر فرد دیگری کمک کند تا بتوانند پروژه⟨U+200C⟩های رؤیایی خود را بسازند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ک کند تا بتوانند پروژه⟨U+200C⟩های رؤیایی خود را بسازند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
به⟨U+200C⟩مناسبت رسیدن تعداد ستاره⟨U+200C⟩های Transformers به **۱۰۰٬۰۰۰**، تصمیم گرفتیم با صفحهٔ
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
به⟨U+200C⟩مناسبت رسیدن تعداد ستاره⟨U+200C⟩های Transformers به **۱۰۰٬۰۰۰**، تصمیم گرفتیم با صفحهٔ
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�اربران جلب کنیم؛ صفحه⟨U+200C⟩ای که فهرستی از **۱۰۰ پروژهٔ شگفت⟨U+200C⟩انگیز** ساخته⟨U+200C⟩شده با این کتابخانه را معرفی می⟨U+200C⟩کند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
تی از **۱۰۰ پروژهٔ شگفت⟨U+200C⟩انگیز** ساخته⟨U+200C⟩شده با این کتابخانه را معرفی می⟨U+200C⟩کند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�هٔ شگفت⟨U+200C⟩انگیز** ساخته⟨U+200C⟩شده با این کتابخانه را معرفی می⟨U+200C⟩کند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ن کتابخانه را معرفی می⟨U+200C⟩کند.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
در صورتی که صاحب یک پروژه هستید یا از پروژه⟨U+200C⟩ای استفاده می⟨U+200C⟩کنید که فکر می⟨U+200C⟩کنید باید در این فهرست قرار بگیرد، لطفاً یک **Pull Request (PR)** باز کنید تا آن را به مجموعه اضافه کنیم.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
در صورتی که صاحب یک پروژه هستید یا از پروژه⟨U+200C⟩ای استفاده می⟨U+200C⟩کنید که فکر می⟨U+200C⟩کنید باید در این فهرست قرار بگیرد، لطفاً یک **Pull Request (PR)** باز کنید تا آن را به مجموعه اضافه کنیم.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
در صورتی که صاحب یک پروژه هستید یا از پروژه⟨U+200C⟩ای استفاده می⟨U+200C⟩کنید که فکر می⟨U+200C⟩کنید باید در این فهرست قرار بگیرد، لطفاً یک **Pull Request (PR)** باز کنید تا آن را به مجموعه اضافه کنیم.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## مدل⟨U+200C⟩های نمونه
شما می⟨U+200C⟩توانید اکثر مدل⟨U+200C⟩های ما را مستقیماً در [صفحات مخزن مدل](https://huggingface.co/models) آنها آزمایش کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## مدل⟨U+200C⟩های نمونه
شما می⟨U+200C⟩توانید اکثر مدل⟨U+200C⟩های ما را مستقیماً در [صفحات مخزن مدل](https://huggingface.co/models) آنها آزمایش کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## مدل⟨U+200C⟩های نمونه
شما می⟨U+200C⟩توانید اکثر مدل⟨U+200C⟩های ما را مستقیماً در [صفحات مخزن مدل](https://huggingface.co/models) آنها آزمایش کنید.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
با باز کردن هر یک از بخش⟨U+200C⟩های زیر، می⟨U+200C⟩توانید چند نمونه مدل برای کاربردهای مختلف را مشاهده کنید.
<details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
با باز کردن هر یک از بخش⟨U+200C⟩های زیر، می⟨U+200C⟩توانید چند نمونه مدل برای کاربردهای مختلف را مشاهده کنید.
<details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- دسته⟨U+200C⟩بندی صدا با [CLAP](https://huggingface.co/laion/clap-htsat-fused)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- تخمین عمق با [DepthPro](https://huggingface.co/apple/DepthPro-hf)
- دسته⟨U+200C⟩بندی تصویر با [DINO v2](https://huggingface.co/facebook/dinov2-base)
- تشخیص نقاط کلیدی با [SuperPoint](https://huggingface.co/magic-leap-community/superpoint)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- تخمین وضعیت بدن با [VitPose](https://huggingface.co/usyd-community/vitpose-base-simple)
- بخش⟨U+200C⟩بندی جامع با [OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_swin_large)
- دسته⟨U+200C⟩بندی ویدیو با [VideoMAE](https://huggingface.co/MCG-NJU/videomae-large)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- بخش⟨U+200C⟩بندی جامع با [OneFormer](https://huggingface.co/shi-labs/oneformer_ade20k_swin_large)
- دسته⟨U+200C⟩بندی ویدیو با [VideoMAE](https://huggingface.co/MCG-NJU/videomae-large)
</details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- پرسش و پاسخ از جدول⟨U+200C⟩ها (Table Question Answering) با [TAPAS](https://huggingface.co/google/tapas-base)
- درک و تولید چندوجهی یکپارچه با [Emu3](https://huggingface.co/BAAI/Emu3-Gen)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- پرسش و پاسخ تصویری (Visual Question Answering) با [Llava](https://huggingface.co/llava-hf/llava-1.5-7b-hf)
- بخش⟨U+200C⟩بندی ارجاعی در تصویر (Visual Referring Expression Segmentation) با [Kosmos-2](https://huggingface.co/microsoft/kosmos-2-patch14-224)
</details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- تکمیل واژه⟨U+200C⟩های حذف⟨U+200C⟩شده (Masked Word Completion) با [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)
- شناسایی موجودیت⟨U+200C⟩های نامدار (Named Entity Recognition) با [Gemma](https://huggingface.co/google/gemma-2-2b)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- تکمیل واژه⟨U+200C⟩های حذف⟨U+200C⟩شده (Masked Word Completion) با [ModernBERT](https://huggingface.co/answerdotai/ModernBERT-base)
- شناسایی موجودیت⟨U+200C⟩های نامدار (Named Entity Recognition) با [Gemma](https://huggingface.co/google/gemma-2-2b)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- شناسایی موجودیت⟨U+200C⟩های نامدار (Named Entity Recognition) با [Gemma](https://huggingface.co/google/gemma-2-2b)
- پرسش و پاسخ متنی (Question Answering) با [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- پرسش و پاسخ متنی (Question Answering) با [Mixtral](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
- خلاصه⟨U+200C⟩سازی متن (Summarization) با [BART](https://huggingface.co/facebook/bart-large-cnn)
- ترجمه خودکار (Translation) با [T5](https://huggingface.co/google-t5/t5-base)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- تولید متن (Text Generation) با [Llama](https://huggingface.co/meta-llama/Llama-3.2-1B)
- دسته⟨U+200C⟩بندی متن (Text Classification) با [Qwen](https://huggingface.co/Qwen/Qwen2.5-0.5B)
</details>
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
اکنون می⟨U+200C⟩توانید برای کتابخانهٔ 🤗 Transformers به [مقالهٔ](https://aclanthology.org/2020.emnlp-demos.6/) منتشرشدهٔ آن استناد کنید.
```bibtex
@inproceedings{wolf-etal-2020-transformers,
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identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<b>Français</b> |
-
identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<b>Français</b> |
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
-
base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
-
identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
-
base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
-
identifier `Рortuguês` contains homoglyph candidates: Р (0420)→P (raw pass, confidence 0.70)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
identifier `Рortuguês` mixes scripts: Cyrillic + Latin (raw pass, confidence 0.80)
<a href="README_ru.md">Русский</a> |
<a href="README_pt-br.md">Рortuguês</a> |
<a href="README_te.md">తెలుగు</a> |
<a href="README_fr.md">Français</a> |
-
base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్లు టెక్స్ట్, విజన్ మరియు ఆడియో వంటి విభిన్న పద్ధతులపై టాస్క్⟨U+200C⟩లను నిర్వహించడానికి వేలాది ముందుగా శ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
్ధతులపై టాస్క్⟨U+200C⟩లను నిర్వహించడానికి వేలాది ముందుగా శిక్షణ పొందిన మోడల్⟨U+200C⟩లను అందిస్తాయి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ణ పొందిన మోడల్⟨U+200C⟩లను అందిస్తాయి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఫర్మేషన్ ఎక్స్⟨U+200C⟩ట్రాక్షన్, ప్రశ్నలకు సమాధానాలు, సారాంశం, అనువాదం, టెక్స్ట్ జనరేషన్ వంటి పనుల కోసం.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
* 🖼⟨U+FE0F⟩ ఇమేజ్⟨U+200C⟩లు, ఇమేజ్ వర్గీకరణ, ఆబ్జెక్ట్ డిటెక్షన్ మరియు సెగ్మెంటేషన్ వంటి పనుల కోసం.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్ మోడల్⟨U+200C⟩లు టేబుల్ క్వశ్చన్ ఆన్సర్ చేయడం, ఆప్టికల్ క్యారెక్టర్ రికగ్నిషన్, స్కాన్ చేసిన డాక్యుమెంట్⟨U+200C⟩ల
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
⟨U+200C⟩ఫార్మర్ మోడల్⟨U+200C⟩లు టేబుల్ క్వశ్చన్ ఆన్సర్ చేయడం, ఆప్టికల్ క్యారెక్టర్ రికగ్నిషన్, స్కాన్ చేసిన డాక్యుమెంట్⟨U+200C⟩ల నుండి ఇన… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ిన డాక్యుమెంట్⟨U+200C⟩ల నుండి ఇన్ఫర్మేషన్ ఎక్స్⟨U+200C⟩ట్రాక్షన్, వీడియో క్లాసిఫికేషన్ మరియు విజువల్ క్వశ్చన్ ఆన్సర్ చేయడం వంటి **అ… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఫర్మేషన్ ఎక్స్⟨U+200C⟩ట్రాక్షన్, వీడియో క్లాసిఫికేషన్ మరియు విజువల్ క్వశ్చన్ ఆన్సర్ చేయడం వంటి **అనేక పద్ధతులతో కలిపి** పనులన… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్లు అందించిన టెక్స్ట్⟨U+200C⟩లో ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లను త్వరగా డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, … (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ించిన టెక్స్ట్⟨U+200C⟩లో ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లను త్వరగా డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్⟨U+200C⟩… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైన్డ్ మోడల్⟨U+200C⟩లను త్వరగా డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్⟨U+200C⟩లలో ఫైన్-ట్యూన్ చేయడాని
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ను త్వరగా డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, వాటిని మీ స్వంత డేటాసెట్⟨U+200C⟩లలో ఫైన్-ట్యూన్ చేయడానికి మరియు వాటిన… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
స్వంత డేటాసెట్⟨U+200C⟩లలో ఫైన్-ట్యూన్ చేయడానికి మరియు వాటిని మా [మోడల్ హబ్](https://huggingface.co/models)లో సంఘంతో భాగస్వామ్యం చేయడానికి API లను అ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��, ఆర్కిటెక్చర్⟨U+200C⟩ని నిర్వచించే ప్రతి పైథాన్ మాడ్యూల్ పూర్తిగా స్వతంత్రంగా ఉంటుంది మరియు త్వరిత పరిశోధన ప్రయోగాలను ప్… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లకు మూడు అత్యంత ప్రజాదరణ పొందిన డీప్ లెర్నింగ్ లైబ్రరీలు ఉన్నాయి — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) మ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లకు మూడు అత్యంత ప్రజాదరణ పొందిన డీప్ లెర్నింగ్ లైబ్రరీలు ఉన్నాయి — [Jax](https://jax.readthedocs.io/en/latest/), [PyTorch](https://pytorch.org/) మరియు … (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�కరణతో. మీ మోడల్⟨U+200C⟩లను ఒకదానితో మరొకదానితో అనుమితి కోసం లోడ్ చేసే ముందు వాటికి శిక్షణ ఇవ్వడం చాలా సులభం.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## ఆన్⟨U+200C⟩లైన్ డెమోలు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ప్రైవేట్ మోడల్⟨U+200C⟩ల కోసం [ప్రైవేట్ మోడల్ హోస్టింగ్, సంస్కరణ & అనుమితి API](https://huggingface.co/pricing)ని కూడా అందిస్తాము.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
సహజ భాషా ప్రాసెసింగ్⟨U+200C⟩లో:
- [BERT తో మాస్క్⟨U+200C⟩డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
సహజ భాషా ప్రాసెసింగ్⟨U+200C⟩లో:
- [BERT తో మాస్క్⟨U+200C⟩డ్ వర్డ్ కంప్లీషన్](https://huggingface.co/google-bert/bert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France)
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base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- [ఆడియో స్పెక్ట్రోగ్రామ్ ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩తో ఆడియో వర్గీకరణ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
మల్టీమోడల్ టాస్క్⟨U+200C⟩లలో:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- [ఆడియో స్పెక్ట్రోగ్రామ్ ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩తో ఆడియో వర్గీకరణ](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593)
మల్టీమోడల్ టాస్క్⟨U+200C⟩లలో:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
మల్టీమోడల్ టాస్క్⟨U+200C⟩లలో:
- [TAPAS తో టేబుల్ ప్రశ్న సమాధానాలు](https://huggingface.co/google/tapas-base-finetuned-wtq)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఉపయోగించి 100 ప్రాజెక్టులు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఉపయోగించి 100 ప్రాజెక్టులు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్లు ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లను ఉపయోగించడానికి టూల్⟨U+200C⟩కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైన్డ్ మోడల్⟨U+200C⟩లను ఉపయోగించడానికి టూల్⟨U+200C⟩కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక్ట్⟨U+200C⟩ల సంఘం మరియు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ించడానికి టూల్⟨U+200C⟩కిట్ కంటే ఎక్కువ: ఇది దాని చుట్టూ నిర్మించిన ప్రాజెక్ట్⟨U+200C⟩ల సంఘం మరియు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
చిన ప్రాజెక్ట్⟨U+200C⟩ల సంఘం మరియు
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�స్ హబ్. డెవలపర్⟨U+200C⟩లు, పరిశోధకులు, విద్యార్థులు, ప్రొఫెసర్⟨U+200C⟩లు, ఇంజనీర్లు మరియు ఎవరినైనా అనుమతించేలా ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ులు, ప్రొఫెసర్⟨U+200C⟩లు, ఇంజనీర్లు మరియు ఎవరినైనా అనుమతించేలా ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను మేము కోరుకుంటున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ంచేలా ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను మేము కోరుకుంటున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను మేము కోరుకుంటున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్⟨U+200C⟩లైట్⟨U+200C⟩ని ఉంచాలని నిర్ణయించుకున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్⟨U+200C⟩లైట్⟨U+200C⟩ని ఉంచాలని నిర్ణయించుకున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్⟨U+200C⟩లైట్⟨U+200C⟩ని ఉంచాలని నిర్ణయించుకున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల 100,000 నక్షత్రాలను జరుపుకోవడానికి, మేము స్పాట్⟨U+200C⟩లైట్⟨U+200C⟩ని ఉంచాలని నిర్ణయించుకున్నాము
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రాన్స్⟨U+200C⟩ఫార్మర్ల పరిసరాల్లో అద్భుతమైన ప్రాజెక్టులు నిర్మించబడ్డాయి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ంచే ప్రాజెక్ట్⟨U+200C⟩ను మీరు కలిగి ఉంటే లేదా ఉపయోగిస్తుంటే, దయచేసి దానిని జోడించడానికి PRని తెరవండి!
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identifier `PRని` mixes scripts: Latin + Other (raw pass, confidence 0.80)
�� జోడించడానికి PRని తెరవండి!
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఇచ్చిన ఇన్⟨U+200C⟩పుట్ (టెక్స్ట్, ఇమేజ్, ఆడియో, ...)పై తక్షణమే మోడల్⟨U+200C⟩ను ఉపయోగించడానికి, మేము `pipeline` API ని అందిస్తాము. పైప్⟨U+200C⟩లైన్⟨U+200C⟩లు ఆ మ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�� తక్షణమే మోడల్⟨U+200C⟩ను ఉపయోగించడానికి, మేము `pipeline` API ని అందిస్తాము. పైప్⟨U+200C⟩లైన్⟨U+200C⟩లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాస… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��దిస్తాము. పైప్⟨U+200C⟩లైన్⟨U+200C⟩లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాసెసింగ్⟨U+200C⟩తో కూడిన ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩ను సమూహపరుస్… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ాము. పైప్⟨U+200C⟩లైన్⟨U+200C⟩లు ఆ మోడల్ శిక్షణ సమయంలో ఉపయోగించిన ప్రీప్రాసెసింగ్⟨U+200C⟩తో కూడిన ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩ను సమూహపరుస్తాయి. స… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�రీప్రాసెసింగ్⟨U+200C⟩తో కూడిన ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩ను సమూహపరుస్తాయి. సానుకూల మరియు ప్రతికూల పాఠాలను వర్గీకరించడానికి పైప్⟨U+200C⟩… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైన్డ్ మోడల్⟨U+200C⟩ను సమూహపరుస్తాయి. సానుకూల మరియు ప్రతికూల పాఠాలను వర్గీకరించడానికి పైప్⟨U+200C⟩లైన్⟨U+200C⟩ను త్వరగా ఎలా ఉపయోగించా… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ించడానికి పైప్⟨U+200C⟩లైన్⟨U+200C⟩ను త్వరగా ఎలా ఉపయోగించాలో ఇక్కడ ఉంది:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
నికి పైప్⟨U+200C⟩లైన్⟨U+200C⟩ను త్వరగా ఎలా ఉపయోగించాలో ఇక్కడ ఉంది:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
� లైన్ కోడ్ డౌన్⟨U+200C⟩లోడ్ మరియు పైప్⟨U+200C⟩లైన్ ఉపయోగించే ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్⟨U+200C⟩పై మూల్… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ోడ్ మరియు పైప్⟨U+200C⟩లైన్ ఉపయోగించే ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్⟨U+200C⟩పై మూల్యాంకనం చేస్తుంద… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైన్డ్ మోడల్⟨U+200C⟩ను కాష్ చేస్తుంది, మూడవది ఇచ్చిన టెక్స్ట్⟨U+200C⟩పై మూల్యాంకనం చేస్తుంది. ఇక్కడ సమాధానం 99.97% విశ్వాసంతో "పాజిటి… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
చ్చిన టెక్స్ట్⟨U+200C⟩పై మూల్యాంకనం చేస్తుంది. ఇక్కడ సమాధానం 99.97% విశ్వాసంతో "పాజిటివ్".
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identifier `NLPలో` mixes scripts: Latin + Other (raw pass, confidence 0.80)
చాలా పనులు NLPలో కానీ కంప్యూటర్ విజన్ మరియు స్పీచ్⟨U+200C⟩లో కూడా ముందుగా శిక్షణ పొందిన `pipeline` సిద్ధంగా ఉన్నాయి. ఉదాహరణకు, మనం చిత్
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��్ మరియు స్పీచ్⟨U+200C⟩లో కూడా ముందుగా శిక్షణ పొందిన `pipeline` సిద్ధంగా ఉన్నాయి. ఉదాహరణకు, మనం చిత్రంలో గుర్తించిన వస్తువులను సుల… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ిడెన్స్ స్కోర్⟨U+200C⟩తో చిత్రంలో గుర్తించబడిన వస్తువుల జాబితాను పొందుతాము. ఇక్కడ ఎడమవైపున ఉన్న అసలు చిత్రం, కుడివైపున అంచన… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
మీరు [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/task_summary)లో `pipeline` API ద్వారా సపోర్ట్ చేసే టాస్క్⟨U+200C⟩ల గురించి మరింత తెలుసుకోవచ్చు.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�� ఇచ్చిన టాస్క్⟨U+200C⟩లో ఏదైనా ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లను డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సర… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైన్డ్ మోడల్⟨U+200C⟩లను డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సరిపోతుంది. ఇక్కడ PyTorch వెర్షన్ ఉంది
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
మోడల్⟨U+200C⟩లను డౌన్⟨U+200C⟩లోడ్ చేయడానికి మరియు ఉపయోగించడానికి, దీనికి మూడు లైన్ల కోడ్ సరిపోతుంది. ఇక్కడ PyTorch వెర్షన్ ఉంది:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�రీప్రాసెసింగ్⟨U+200C⟩లకు టోకెనైజర్ బాధ్యత వహిస్తుంది మరియు నేరుగా ఒకే స్ట్రింగ్ (పై ఉదాహరణలలో వలె) లేదా జాబితాపై కాల్ చేయవచ… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ు. ఇది మీరు డౌన్⟨U+200C⟩స్ట్రీమ్ కోడ్⟨U+200C⟩లో ఉపయోగించగల నిఘంటువుని అవుట్⟨U+200C⟩పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్⟨U+200C⟩ప్యాకింగ్ ఆపరేట… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
⟨U+200C⟩స్ట్రీమ్ కోడ్⟨U+200C⟩లో ఉపయోగించగల నిఘంటువుని అవుట్⟨U+200C⟩పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్⟨U+200C⟩ప్యాకింగ్ ఆపరేటర్⟨U+200C⟩ని ఉపయోగించ… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఘంటువుని అవుట్⟨U+200C⟩పుట్ చేస్తుంది లేదా ** ఆర్గ్యుమెంట్ అన్⟨U+200C⟩ప్యాకింగ్ ఆపరేటర్⟨U+200C⟩ని ఉపయోగించి నేరుగా మీ మోడల్⟨U+200C⟩కి పంపుతుంది.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
్గ్యుమెంట్ అన్⟨U+200C⟩ప్యాకింగ్ ఆపరేటర్⟨U+200C⟩ని ఉపయోగించి నేరుగా మీ మోడల్⟨U+200C⟩కి పంపుతుంది.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ాకింగ్ ఆపరేటర్⟨U+200C⟩ని ఉపయోగించి నేరుగా మీ మోడల్⟨U+200C⟩కి పంపుతుంది.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ేరుగా మీ మోడల్⟨U+200C⟩కి పంపుతుంది.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
del) (మీ బ్యాకెండ్⟨U+200C⟩ని బట్టి) మీరు మామూలుగా ఉపయోగించవచ్చు. [ఈ ట్యుటోరియల్](https://huggingface.co/docs/transformers/training) అటువంటి మోడల్⟨U+200C⟩ని క్లాసిక్ PyTorch ల
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
g) అటువంటి మోడల్⟨U+200C⟩ని క్లాసిక్ PyTorch లేదా TensorFlow ట్రైనింగ్ లూప్⟨U+200C⟩లో ఎలా ఇంటిగ్రేట్ చేయాలో లేదా మా `Trainer` API ని ఎలా ఉపయోగించాలో వివరిస
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ట్రైనింగ్ లూప్⟨U+200C⟩లో ఎలా ఇంటిగ్రేట్ చేయాలో లేదా మా `Trainer` API ని ఎలా ఉపయోగించాలో వివరిస్తుంది కొత్త డేటాసెట్.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## నేను ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఎందుకు ఉపయోగించాలి?
1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్⟨U+200C⟩లు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## నేను ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఎందుకు ఉపయోగించాలి?
1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్⟨U+200C⟩లు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
1. ఉపయోగించడానికి సులభమైన స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్⟨U+200C⟩లు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- మా అన్ని ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లను ఉపయోగించడం కోసం ఏకీకృత API.
2. తక్కువ గణన ఖర్చులు, చిన్న కార్బన్ పాదముద్ర:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- అన్ని పద్ధతుల్లో 60,000 కంటే ఎక్కువ ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లతో డజన్ల కొద్దీ ఆర్కిటెక్చర్⟨U+200C⟩లు.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- అన్ని పద్ధతుల్లో 60,000 కంటే ఎక్కువ ప్రీట్రైన్డ్ మోడల్⟨U+200C⟩లతో డజన్ల కొద్దీ ఆర్కిటెక్చర్⟨U+200C⟩లు.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
3. మోడల్ జీవితకాలంలో ప్రతి భాగానికి సరైన ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ను ఎంచుకోండి:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
3. మోడల్ జీవితకాలంలో ప్రతి భాగానికి సరైన ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ను ఎంచుకోండి:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- 3 లైన్ల కోడ్⟨U+200C⟩లో స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్⟨U+200C⟩లకు శిక్షణ ఇవ్వండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- 3 లైన్ల కోడ్⟨U+200C⟩లో స్టేట్ ఆఫ్ ది ఆర్ట్ మోడల్⟨U+200C⟩లకు శిక్షణ ఇవ్వండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- TF2.0/PyTorch/JAX ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ల మధ్య ఒకే మోడల్⟨U+200C⟩ను ఇష్టానుసారంగా తరలించండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- TF2.0/PyTorch/JAX ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ల మధ్య ఒకే మోడల్⟨U+200C⟩ను ఇష్టానుసారంగా తరలించండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- TF2.0/PyTorch/JAX ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ల మధ్య ఒకే మోడల్⟨U+200C⟩ను ఇష్టానుసారంగా తరలించండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- శిక్షణ, మూల్యాంకనం మరియు ఉత్పత్తి కోసం సరైన ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ను సజావుగా ఎంచుకోండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- శిక్షణ, మూల్యాంకనం మరియు ఉత్పత్తి కోసం సరైన ఫ్రేమ్⟨U+200C⟩వర్క్⟨U+200C⟩ను సజావుగా ఎంచుకోండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- మోడల్ ఇంటర్నల్⟨U+200C⟩లు వీలైనంత స్థిరంగా బహిర్గతమవుతాయి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
- శీఘ్ర ప్రయోగాల కోసం లైబ్రరీ నుండి స్వతంత్రంగా మోడల్ ఫైల్⟨U+200C⟩లను ఉపయోగించవచ్చు.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## నేను ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఎందుకు ఉపయోగించకూడదు?
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
## నేను ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఎందుకు ఉపయోగించకూడదు?
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ీ న్యూరల్ నెట్⟨U+200C⟩ల కోసం బిల్డింగ్ బ్లాక్⟨U+200C⟩ల మాడ్యులర్ టూల్⟨U+200C⟩బాక్స్ కాదు. మోడల్ ఫైల్⟨U+200C⟩లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు స… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ల్డింగ్ బ్లాక్⟨U+200C⟩ల మాడ్యులర్ టూల్⟨U+200C⟩బాక్స్ కాదు. మోడల్ ఫైల్⟨U+200C⟩లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ … (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
మాడ్యులర్ టూల్⟨U+200C⟩బాక్స్ కాదు. మోడల్ ఫైల్⟨U+200C⟩లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ చేయబడదు, తద్వారా ప
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ాదు. మోడల్ ఫైల్⟨U+200C⟩లలోని కోడ్ ఉద్దేశపూర్వకంగా అదనపు సంగ్రహణలతో రీఫ్యాక్టరింగ్ చేయబడదు, తద్వారా పరిశోధకులు అదనపు సంగ్రహ… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��డా ప్రతి మోడల్⟨U+200C⟩పై త్వరగా మళ్లించగలరు.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ిక్షణ API ఏ మోడల్⟨U+200C⟩లో పని చేయడానికి ఉద్దేశించబడలేదు కానీ లైబ్రరీ అందించిన మోడల్⟨U+200C⟩లతో పని చేయడానికి ఆప్టిమైజ్ చేయబడింది. … (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
అందించిన మోడల్⟨U+200C⟩లతో పని చేయడానికి ఆప్టిమైజ్ చేయబడింది. సాధారణ మెషిన్ లెర్నింగ్ లూప్⟨U+200C⟩ల కోసం, మీరు మరొక లైబ్రరీని ఉపయోగి… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
లెర్నింగ్ లూప్⟨U+200C⟩ల కోసం, మీరు మరొక లైబ్రరీని ఉపయోగించాలి (బహుశా, [Accelerate](https://huggingface.co/docs/accelerate)).
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
tml)లో 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org/guides/installing-usi… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయాలి. మీకు పైథాన్ వర్చువల్ పరిసరాల గురించి తెలియకుంటే, [యూజర్ గైడ్](https://packaging.python.org/guides/installing-using-pip-and-virtual-envir
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ముందుగా, మీరు ఉపయోగించబోతున్న పైథాన్ వెర్షన్⟨U+200C⟩తో వర్చువల్ వాతావరణాన్ని సృష్టించండి మరియు దానిని సక్రియం చేయండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
అప్పుడు, మీరు ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్⟨U+200C⟩ఫ్లోలో కనీసం ఒకదానిని ఇన్⟨U+200C⟩స్టాల్ చేయాలి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
అప్పుడు, మీరు ఫ్లాక్స్, పైటార్చ్ లేదా టెన్సర్⟨U+200C⟩ఫ్లోలో కనీసం ఒకదానిని ఇన్⟨U+200C⟩స్టాల్ చేయాలి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
దయచేసి [TensorFlow ఇన్⟨U+200C⟩స్టాలేషన్ పేజీ](https://www.tensorflow.org/install/), [PyTorch ఇన్⟨U+200C⟩స్టాలేషన్ పేజీ](https://pytorch.org/get-started/locally/#start-locally) మరియు/ని చూడండి లేదా మీ ప్లాట… (340 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
orflow.org/install/), [PyTorch ఇన్⟨U+200C⟩స్టాలేషన్ పేజీ](https://pytorch.org/get-started/locally/#start-locally) మరియు/ని చూడండి లేదా మీ ప్లాట్⟨U+200C⟩ఫారమ్ కోసం నిర్దిష్ట ఇన్⟨U+200C⟩స్టాలేషన్
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
� లేదా మీ ప్లాట్⟨U+200C⟩ఫారమ్ కోసం నిర్దిష్ట ఇన్⟨U+200C⟩స్టాలేషన్ కమాండ్⟨U+200C⟩కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇ… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�� నిర్దిష్ట ఇన్⟨U+200C⟩స్టాలేషన్ కమాండ్⟨U+200C⟩కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇన్⟨U+200C⟩స్టాలేషన్ పేజీలు .
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
టాలేషన్ కమాండ్⟨U+200C⟩కు సంబంధించి [Flax](https://github.com/google/flax#quick-install) మరియు [Jax](https://github.com/google/jax#installation) ఇన్⟨U+200C⟩స్టాలేషన్ పేజీలు .
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
b.com/google/jax#installation) ఇన్⟨U+200C⟩స్టాలేషన్ పేజీలు .
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఆ బ్యాకెండ్⟨U+200C⟩లలో ఒకటి ఇన్⟨U+200C⟩స్టాల్ చేయబడినప్పుడు, 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఈ క్రింది విధంగా పిప్⟨U+200C⟩ని ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��్⟨U+200C⟩లలో ఒకటి ఇన్⟨U+200C⟩స్టాల్ చేయబడినప్పుడు, 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఈ క్రింది విధంగా పిప్⟨U+200C⟩ని ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చ… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ుడు, 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఈ క్రింది విధంగా పిప్⟨U+200C⟩ని ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�ాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఈ క్రింది విధంగా పిప్⟨U+200C⟩ని ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ది విధంగా పిప్⟨U+200C⟩ని ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�� ఉపయోగించి ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
�� లైబ్రరీని ఇన్⟨U+200C⟩స్టాల్ చేయాలి](https://huggingface.co/docs/transformers/installation#installing-from-source).
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
```shell script
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
```shell script
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 కింది విధంగా కొండా ఉపయోగించి ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩లను ఇన్⟨U+200C⟩స్టాల్ చేయవచ్చు:
```shell script
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
> **_గమనిక:_** `huggingface` ఛానెల్ నుండి `transformers` ఇన్⟨U+200C⟩స్టాల్ చేయడం పురాతనంగా ఉంది.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
Flax, PyTorch లేదా TensorFlow యొక్క ఇన్⟨U+200C⟩స్టాలేషన్ పేజీలను కొండాతో ఎలా ఇన్⟨U+200C⟩స్టాల్ చేయాలో చూడటానికి వాటిని అనుసరించండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
Flax, PyTorch లేదా TensorFlow యొక్క ఇన్⟨U+200C⟩స్టాలేషన్ పేజీలను కొండాతో ఎలా ఇన్⟨U+200C⟩స్టాల్ చేయాలో చూడటానికి వాటిని అనుసరించండి.
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identifier `Windowsలో` mixes scripts: Latin + Other (raw pass, confidence 0.80)
> **_గమనిక:_** Windowsలో, కాషింగ్ నుండి ప్రయోజనం పొందేందుకు మీరు డెవలపర్ మోడ్⟨U+200C⟩ని సక్రియం చేయమని ప్రాంప్ట్ చేయబడవచ్చు. ఇది మీకు ఎ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ు డెవలపర్ మోడ్⟨U+200C⟩ని సక్రియం చేయమని ప్రాంప్ట్ చేయబడవచ్చు. ఇది మీకు ఎంపిక కాకపోతే, దయచేసి [ఈ సంచిక](https://github.com/huggingface/huggingface_hub/issues/1062)ల… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��్ని మోడల్ చెక్⟨U+200C⟩పాయింట్⟨U+200C⟩లు](https://huggingface.co/models)** 🤗 అందించిన ట్రాన్స్⟨U+200C⟩ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చ… (339 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
్ చెక్⟨U+200C⟩పాయింట్⟨U+200C⟩లు](https://huggingface.co/models)** 🤗 అందించిన ట్రాన్స్⟨U+200C⟩ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చేయబడ్డాయి [u
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ించిన ట్రాన్స్⟨U+200C⟩ఫార్మర్లు huggingface.co [model hub](https://huggingface.co/models) నుండి సజావుగా ఏకీకృతం చేయబడ్డాయి [users](https://huggingface.co/users) మరియు [organizations](https://huggingface.co/organiz
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��ారా నేరుగా అప్⟨U+200C⟩లోడ్ చేయబడతాయి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్⟨U+200C⟩లను అందజేస్తున్నాయి: వాటిలో ప్రతి ఒక్కటి ఉన్నత స్థాయి సారాంశం కోస
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ి ఆర్కిటెక్చర్⟨U+200C⟩లను అందజేస్తున్నాయి: వాటిలో ప్రతి ఒక్కటి ఉన్నత స్థాయి సారాంశం కోసం [ఇక్కడ](https://huggingface.co/docs/transformers/model_summary) చూడండి.
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
ఈ అమలులు అనేక డేటాసెట్⟨U+200C⟩లలో పరీక్షించబడ్డాయి మరియు అసలైన అమలుల పనితీరుతో సరిపోలాలి.
## ఇంకా నేర్చుకో
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
| [టాస్క్ సారాంశం](https://huggingface.co/docs/transformers/task_summary) | 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల ద్వారా సపోర్ట్ చేయబడిన విధులు |
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
| [టాస్క్ సారాంశం](https://huggingface.co/docs/transformers/task_summary) | 🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్⟨U+200C⟩ల ద్వారా సపోర్ట్ చేయబడిన విధులు |
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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identifier `APIలో` mixes scripts: Latin + Other (raw pass, confidence 0.80)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
��మ్యం మరియు అప్⟨U+200C⟩లోడ్ చేయడం](https://huggingface.co/docs/transformers/model_sharing) | కమ్యూనిటీతో మీ ఫైన్-ట్యూన్డ్ మోడల్⟨U+200C⟩లను అప్⟨U+200C⟩లోడ్ చేయండి మరియు భాగస్వ… (341 bytes total)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
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U+200C ZERO WIDTH NON-JOINER in source (raw pass, confidence 0.75)
🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్స్ లైబ్రరీ కోసం మీరు ఉదహరించగల [పేపర్](https://aclanthology.org/2020.emnlp-demos.6/) ఇప్పుడు మా వద్ద ఉంది:
```bibtex
@inproceedings{wolf-etal-2020-transformers,
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base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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base64-like blob (97 bytes, compression ratio 0.92) (raw pass, confidence 0.60)
ecome+the+tallest+man-made+structure+in+the+world%2C+a+title+it+held+for+41+years+until+the+Chrysler+Building+in+New+York+City+was+finished+in+1930.+It+was+the+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrys
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base64-like blob (77 bytes, compression ratio 0.94) (raw pass, confidence 0.60)
e+first+structure+to+reach+a+height+of+300+metres.+Due+to+the+addition+of+a+broadcasting+aerial+at+the+top+of+the+tower+in+1957%2C+it+is+now+taller+than+the+Chrysler+Building+by+5.2+metres+%2817+ft%29.+Excluding+transmitters%2C+the+Eiffel+Tower+is+the+second+tallest+free-standing+structure+in+France+after+the+Millau+Vi
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`open(__file__)` — file reads its own source, common pattern for extracting payloads hidden in comments (ast pass, confidence 0.85)
with open(__file__, "r", encoding="utf-8") as fh:
fcntl.flock(fh, fcntl.LOCK_EX)
try:
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reach-by-name through `vars.get` (ast pass, confidence 0.60)
cfg, args = args_file_parser.parse_known_args(args=args)
cmd_args_file_paths = vars(cfg).get(args_file_flag.lstrip("-"), None)
if cmd_args_file_paths:
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file imports decoder module(s) base64 and calls `.decompress()` — embedded payload decompressed at runtime (ast pass, confidence 0.75)
try:
b64 = base64.decodebytes(image.encode())
image = PIL.Image.open(BytesIO(b64))
except Exception as e:
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reach-by-name through `globals` (ast pass, confidence 0.60)
originals[module][func_name] = getattr(module, func_name)
setattr(module, func_name, globals()[func_name])
yield
finally:
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`inspect.stack` — call-stack frame introspection (ast pass, confidence 0.70)
caller__file__ = inspect.stack()[1][1]
tests_dir = os.path.abspath(os.path.dirname(caller__file__))
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`inspect.stack` — call-stack frame introspection (ast pass, confidence 0.70)
stack_from_inspect = inspect.stack()
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reach-by-name through `vars` (ast pass, confidence 0.60)
if key in vars(cls):
return vars(cls)[key]
raise AttributeError(f"{self.__class__.__name__} has no attribute {key}")
return object.__getattribute__(self, key)
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`inspect.currentframe` — call-stack frame introspection (ast pass, confidence 0.70)
caller = inspect.currentframe().f_back.f_back.f_code.co_name
if caller in self.stages:
return self.stages[caller]
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concatenated bytes literal contains 566 `\xNN` escapes (56% of content) — binary payload shape (ast pass, confidence 0.85)
serialized_pb=(
b'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\xa1\n\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01'
b" \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02"
b" \x01(\t\x12\x41\n\nmodel_type\x18\x03"
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bytes literal contains 44 `\xNN` escapes (71% of content) — binary payload shape (ast pass, confidence 0.85)
b" \x12+\n\x1ctrain_extremely_large_corpus\x18\x31"
b' \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01'
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bytes literal contains 56 `\xNN` escapes (64% of content) — binary payload shape (ast pass, confidence 0.85)
b' \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x
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base64-like blob (40 bytes, compression ratio 1.27) (raw pass, confidence 0.60)
"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR4"
"2mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg=="
)
messages = [
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identifier `力加勝北区ᴵᴺᵀᵃছজটডণত` mixes scripts: Han + Other (raw pass, confidence 0.80)
This text is included to make sure Unicode is handled properly: 力加勝北区ᴵᴺᵀᵃছজটডণত
Text should be one-sentence-per-line, with empty lines between documents.
This sample text is public domain and was randomly selected from Project Guttenberg.
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line length 4075 characters (raw pass, confidence 0.65)
[{"segmentation": [[333.96, 175.14, 338.26, 134.33, 342.55, 95.67, 348.99, 79.57, 368.32, 80.64, 371.54, 91.38, 364.03, 106.41, 356.51, 145.07, 351.14, 166.55, 350.07, 184.8, 345.77, 185.88, 332.89, 178.36, 332.89, 172.99]], "area": 2120.991099999999, "iscrowd": 0, "image_id": 39769, "bbox": [332.89, 79.57, 38.65, 106.
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`inspect.stack` — call-stack frame introspection (ast pass, confidence 0.70)
return any(f.function == "is_deepgemm_loadable" for f in inspect.stack())
stack.enter_context(
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`inspect.stack` — call-stack frame introspection (ast pass, confidence 0.70)
def _in_loader():
return any(f.function == "is_sonicmoe_loadable" for f in inspect.stack())
stack.enter_context(
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line length 2166 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962,
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line length 5079 characters (raw pass, confidence 0.65)
[[685, 560, 630, 193, 836, 764, 708, 360, 10, 724, 278, 755, 805, 600, 71, 473, 601, 397, 315, 706, 487, 552, 88, 175, 601, 850, 678, 538, 846, 73, 778, 917, 116, 977, 756, 710, 1023, 848, 432, 449, 851, 100, 985, 178, 756, 798, 660, 148, 911, 424, 289, 962, 266, 698, 640, 545, 544, 715, 245, 152, 676, 511,
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line length 8017 characters (raw pass, confidence 0.65)
input_ids = [57344, 57349, 85, 107, 117, 98, 119, 97, 32, 119, 97, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 110, 105, 32, 107, 105, 97, 115, 105, 32, 103, 97, 110, 105, 63, 57345, 57350, 32, 82, 105, 106, 105, 108, 105, 32, 75, 97, 110, 116, 111, 114, 105, 32, 44, 32, 82, 105, 1
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line length 6169 characters (raw pass, confidence 0.65)
token_type_ids = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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line length 3324 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[144, 43, 32, 87, 26, 173, 2, 5, 87, 26, 44, 70, 2, 209, 27, 2, 55, 2, 29, 38, 51, 31, 71, 8, 144, 43, 32, 87, 26, 173, 2, 53, 2, 29, 38, 51, 31, 71, 8, 29, 46, 144, 137, 49, 8, 15, 44, 33, 6, 2, 187, 35, 83, 61, 2, 20
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line length 2065 characters (raw pass, confidence 0.65)
'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
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line length 4369 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[84, 114, 97, 110, 115, 102, 111, 114, 109, 101, 114, 115, 32, 40, 102, 111, 114, 109, 101, 114, 108, 121, 32, 107, 110, 111, 119, 110, 32, 97, 115, 32, 112, 121, 116, 111, 114, 99, 104, 45, 116, 114, 97, 110, 115, 102, 111, 114, 109, 101, 114, 115, 32, 97, 110, 100, 32, 112,
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line length 2424 characters (raw pass, confidence 0.65)
EXPECTED_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011
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line length 2422 characters (raw pass, confidence 0.65)
SINGLE_PADDED_UNPACKED_TOKEN_INPUTS = torch.Tensor([[71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71019, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011, 71011,
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line length 2144 characters (raw pass, confidence 0.65)
(None, None): torch.tensor([[[1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024], [244, 1024, 1024, 1024, 1024, 1024, 1024, 1024], [498, 537, 1024, 1024, 1024, 1024, 1024, 1024], [430, 851, 977, 1024, 1024, 1024, 1024, 1024], [950, 986, 39, 130, 1024, 1024, 1024, 1024], [196, 212, 784, 392, 283, 1024, 1024
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line length 2172 characters (raw pass, confidence 0.65)
(None, None): torch.tensor([[[1024, 1024, 1024, 1024, 1024, 1024, 1024, 1024], [267, 1024, 1024, 1024, 1024, 1024, 1024, 1024], [796, 765, 1024, 1024, 1024, 1024, 1024, 1024], [239, 960, 878, 1024, 1024, 1024, 1024, 1024], [307, 446, 313, 154, 1024, 1024, 1024, 1024], [811, 953, 116, 402, 806, 1024, 102
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line length 3797 characters (raw pass, confidence 0.65)
expected_boxes = [[[141, 57, 210, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714,
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line length 3799 characters (raw pass, confidence 0.65)
expected_boxes_1 = [[[141, 57, 210, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714
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line length 3799 characters (raw pass, confidence 0.65)
expected_boxes_2 = [[[141, 57, 214, 69], [228, 58, 252, 69], [141, 75, 216, 88], [230, 79, 280, 88], [142, 260, 218, 273], [230, 261, 255, 273], [143, 279, 218, 290], [231, 282, 290, 291], [143, 342, 218, 354], [231, 345, 289, 355], [202, 362, 227, 373], [143, 379, 220, 392], [231, 382, 291, 394], [144, 714
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line length 2492 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290,
-
line length 2877 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[43495, 462, 20, 42164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 38999, 6, 8, 464, 132, 1703, 492, 13, 4669, 37867, 13, 7525, 27, 1593, 988, 13, 33972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 12338, 2, 13958, 387, 2, 3629, 6953, 188, 2900, 2, 13958, 8011, 11
-
line length 5088 characters (raw pass, confidence 0.65)
expected_xpaths_tags_seq = [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216], [109, 25, 216, 216, 216, 216
-
line length 5992 characters (raw pass, confidence 0.65)
expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001
-
line length 11360 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216,
-
line length 22530 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216,
-
line length 11486 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [0, 42891, 8331, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216,
-
line length 22777 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [[0, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216,
-
line length 11369 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [0, 12196, 18, 39, 766, 116, 2, 42891, 232, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'xpath_tags_seq': [[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 2
-
line length 22549 characters (raw pass, confidence 0.65)
expected_results = {'input_ids': [[0, 12196, 18, 39, 766, 116, 2, 42891, 232, 12364, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 9178, 16, 37, 373, 116, 2, 42891, 127, 766, 16, 22401, 2, 1, 1, 1, 1, 1, 1, 1]], 'xpath_tags_seq': [[[216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216, 216,
-
string literal compression ratio 0.979 (141 bytes) (raw pass, confidence 0.60)
{
(None, None): " rated behandeltúگزاری noticeably网站之後orienanjing Actress milliardAYA Institutes juxt!!\nartifact shapes aromaticendy conducir",
-
string literal compression ratio 0.979 (141 bytes) (raw pass, confidence 0.60)
("cuda", 9): " rated behandeltúگزاری noticeably网站之後orienanjing Actress milliardAYA Institutes juxt!!\nartifact shapes aromaticendy conducir",
}
)
-
line length 2044 characters (raw pass, confidence 0.65)
EXPECTED_ROUTER_ALL = torch.tensor([[0.58605188, 0.00000000, 0.41394812, 0.00000000], [0.00000000, 0.85299969, 0.00000000, 0.14700034], [0.00000000, 0.00000000, 0.74955785, 0.25044215], [0.30080634, 0.00000000, 0.69919366, 0.00000000], [0.00000000, 0.00000000, 0.20320441, 0.79679561], [0.90920591, 0.00000000, 0
-
line length 2043 characters (raw pass, confidence 0.65)
EXPECTED_ROUTER_SP = torch.tensor([[0.48075044, 0.00000000, 0.00000000, 0.51924956], [0.00000000, 0.45196214, 0.00000000, 0.54803789], [0.50154328, 0.00000000, 0.49845666, 0.00000000], [0.53203368, 0.00000000, 0.46796635, 0.00000000], [0.56414306, 0.00000000, 0.00000000, 0.43585697], [0.36307240, 0.00000000, 0.
-
line length 2040 characters (raw pass, confidence 0.65)
EXPECTED_ROUTER = torch.tensor([[0.00000000, 0.00000000, 0.00000000, 1.00000000], [0.00000000, 0.45196214, 0.00000000, 0.54803789], [1.00000000, 0.00000000, 0.00000000, 0.00000000], [1.00000000, 0.00000000, 0.00000000, 0.00000000], [0.56414306, 0.00000000, 0.00000000, 0.43585697], [0.00000000, 0.00000000, 0.000
-
line length 3802 characters (raw pass, confidence 0.65)
expected_encoding = {'input_ids': [[3791, 797, 31, 11, 64, 797, 31, 2429, 433, 12, 1176, 12, 20, 786, 915, 142, 2413, 240, 37, 3238, 797, 31, 11, 35, 93, 915, 142, 2413, 240, 37, 5540, 567, 1276, 93, 37, 610, 40, 62, 455, 657, 1042, 123, 780, 177, 37, 309, 241, 1298, 514, 20, 292, 2737, 114, 2469, 241, 85, 64,
-
line length 5362 characters (raw pass, confidence 0.65)
expected_results = {'input_ids':[101,2054,2020,1996,6853,3415,1029,102,13433,2015,2053,4062,2136,10876,2051,1013,3394,8370,2685,1015,3590,4754,29267,4765,3771,2136,2447,1005,1055,6584,1015,1024,4466,1024,2340,1012,6185,2509,1015,2570,1016,1015,10391,12022,4226,7895,10625,1013,22996,3868,6584,1009,1014,1012,1022
-
line length 25512 characters (raw pass, confidence 0.65)
expected_input_ids = [151644, 872, 198, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655, 151655,
-
line length 3143 characters (raw pass, confidence 0.65)
[[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.9
-
line length 3143 characters (raw pass, confidence 0.65)
[[[2.287, 1.234, 0.042, 1.53, 1.306, 0.879, -0.526, -1.71, -1.276, 0.756, -0.775, -1.048, -0.25, -0.595, -0.137, -0.598, 2.022, -0.812, 0.284, -0.488, -0.391, -0.403, -0.525, -0.061, -0.228, 1.086, 0.378, -0.14, 0.599, -0.087, -2.259, -0.098], [1.676, 0.232, -1.508, -0.145, 1.798, -1.388, 1.331, -0.37, -0.9
-
base64-like blob (14452 bytes, compression ratio 0.74) (raw pass, confidence 0.60)
IMG_BASE_64 = """/9j/4QDeRXhpZgAASUkqAAgAAAAGABIBAwABAAAAAQAAABoBBQABAAAAVgAAABsBBQABAAAAXgAAACgBAwABAAAAAgAAABMCAwABAAAAAQAAAGmHBAABAAAAZgAAAAAAAABIAAAAAQAAAEgAAAABAAAABwAAkAcABAAAADAyMTABkQcABAAAAAECAwCGkgcAFgAAAMAAAAAAoAcABAAAADAxMDABoAMAAQAAAP//AAACoAQAAQAAAMgAAAADoAQAAQAAACwBAAAAAAAAQVNDSUkAAABQaWNzdW0gSUQ6IDIzN//
-
base64-like blob (110976 bytes, compression ratio 0.73) (raw pass, confidence 0.60)
AUDIO_BASE_64 = """//uUxAAAAAAAAAAAAAAAAAAAAAAAWGluZwAAAA8AAAHNAAFFIAACAwQGBwkLDBAUGBodIiQnKy8yNjo9QEVIS1BTVlteYGNlZ2ltcHR5fH6AgoSGioyPkpOVl5mbnJ6goqWnqaqsra6wsbK0tbe6wMTIzM/S1tnd3+Ll6Ovt7/Dy8/X3+fr8/f4AAAA8TEFNRTMuMTAwBK8AAAAAAAAAABUgJAJAgQABzAABRSDkC9nPAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
-
pipeline feeds into `sh` — piped data is executed as shell code (ast pass, confidence 0.80)
: "${VIRTUAL_ENV:?VIRTUAL_ENV must be set by the Dockerfile}"
curl -LsSf https://astral.sh/uv/install.sh | sh
uv venv --python "$1" --seed "$VIRTUAL_ENV"
python3 --version
likely benign
LLM, confidence 0.80
Build script that installs uv by piping the astral.sh install script into sh, then creates a venv.
Reasoning
Piping the official uv installer script from astral.sh into sh is a common, documented install pattern. It is in a Dockerfile helper script with a clear purpose. It is a legitimate but supply-chain-relevant pattern, so it is not malicious on its face.
-
identifier `وReformer` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�صوص طويلة. ويعد Longformer وReformer من النماذج التي تحاول أن تكون أكثر كفاءة وتستخدم نسخة مخففة من مصفوفة الانتباه لتسريع التدريب.
dismissed
LLM, confidence 0.90
Arabic documentation text mixing Latin model name Reformer with Arabic characters.
Reasoning
Arabic prose in documentation with a Latin model name (Reformer) adjacent to the Arabic conjunction وwaw. This is normal bidirectional text in a translated doc, not an identifier in code. Raw-pass false positive.
-
identifier `وk` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�اش لتحديد ما إذا كان q وk متشابهين. ويتم تعديل قناع الانتباه لتجاهل الرمز الحالي (باستثناء الموضع الأول)، لأنه سيعطي استعلامًا ومفتاحًا متساويين (لذلك متشابهين للغاية). نظرًا ل… (341 bytes total)
dismissed
LLM, confidence 0.90
Arabic documentation prose with a Latin variable name k in running text.
Reasoning
Arabic prose containing the Latin letter k inside a mathematical description. Natural mixed-script text in docs; no code execution or obfuscation involved.
-
identifier `وE2` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
� E إلى مصفوفتين أصغر E1 وE2، بأبعاد \\(l_{1} \times d_{1}\\) و \\(l_{2} \times d_{2}\\)، بحيث \\(l_{1} \times l_{2} = l\\) و\\(d_{1} + d_{2} = d\\) (مع حاصل ضرب الأطوال، ينتهي الأمر بكونه أصغر بكثير). ويتم الحصول على الترميز … (340 bytes total)
dismissed
LLM, confidence 0.90
Arabic documentation prose with Latin variable E2 in running text.
Reasoning
Arabic prose with Latin math variable E2 in running text. Expected for technical translation. Raw-pass false positive.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�جد قيود على أسماء هذه المعامﻻت أو تنسيقاتها - يمكنك تمرير سلاسل نصية أو قوائم أو قواميس أو أي شيء آخر تريده.
dismissed
LLM, confidence 0.90
Arabic documentation text where the word for parameters contains lam-alef ligature characters.
Reasoning
The flagged token contains Arabic-presentation-form characters (ﻻ) mixed with Arabic, typical of typographic or translation artifacts in prose. This is a documentation file, not code, so it poses no execution risk and is a false positive.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
الشائعة لاستخدام هذه المعامﻻت الإضافية، مثل تمرير أدوات لاستدعاء الوظائف، أو المستندات لإنشاء النصوص المُعزّزة بالاسترجاع. في هذه الحالات الشائعة، لدينا بعض التوصيات المُ… (340 bytes total)
dismissed
LLM, confidence 0.90
Arabic documentation prose containing the word for parameters with ligature characters.
Reasoning
Same ligature-style Arabic word in documentation prose. No code context, benign translation artifact.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
مُحدّدة حول أسماء هذه المعامﻻت وتنسيقاتها، والتي يتم وصفها في الأقسام التالية. نشجع مطوّري النماذج على جعل قوالب الدردشة الخاصة بهم متوافقة مع هذا التنسيق، لتسهيل نقل التعلي… (340 bytes total)
dismissed
LLM, confidence 0.90
Arabic documentation prose with a ligature-containing parameters word.
Reasoning
Arabic documentation prose with the same ligature-containing word. Benign text in a markdown doc.
-
identifier `المعاﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
- يجب أن تحتوي الدالة على سلسلة مستندية بتنسيق Google القياسي (بمعنى وصف الدالة الأولي متبوعًا بكتلة `Args:` التي تصف المعاﻻت، ما لم تكن الدالة لا تحتوي على أي معامﻻت.
dismissed
LLM, confidence 0.90
Arabic documentation describing docstring conventions with a ligature-containing word.
Reasoning
Arabic documentation describing function docstring format, with Google-style Args reference. Ligature characters are a typographic artifact in prose, not an identifier used in code.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
- يجب أن تحتوي الدالة على سلسلة مستندية بتنسيق Google القياسي (بمعنى وصف الدالة الأولي متبوعًا بكتلة `Args:` التي تصف المعاﻻت، ما لم تكن الدالة لا تحتوي على أي معامﻻت.
dismissed
LLM, confidence 0.90
Arabic documentation bullet with a ligature-containing parameters word.
Reasoning
Arabic prose with the same ligature word in a documentation bullet. Benign translation text.
-
identifier `ومعامﻻتها` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
1. تحليل مخرجات النموذج للحصول على اسم (أسماء) الأدوات ومعامﻻتها.
2. أضف استدعاء (استدعاءات) النموذج لِلأدوات إلى المحادثة.
3. استدعاء الدالة (الدالات) المقابلة بتلك المعامﻻت.
dismissed
LLM, confidence 0.90
Arabic documentation list about tool calling with a ligature-containing word.
Reasoning
Arabic numbered list prose in documentation about tool calling. No code or obfuscation; the mixed-script flag is a false positive from ligature characters.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
2. أضف استدعاء (استدعاءات) النموذج لِلأدوات إلى المحادثة.
3. استدعاء الدالة (الدالات) المقابلة بتلك المعامﻻت.
4. أضف النتيجة (النتائج) إلى المحادثة
dismissed
LLM, confidence 0.90
Arabic documentation list item about tool results with a ligature-containing word.
Reasoning
Arabic documentation list item with the same ligature-containing word. Benign prose.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
ذج باستدعاء الدالة مع معامﻻت صحيحة، بالصيغة التي طلبتها توثيق الدالة. لقد استنتج أننا نشير على الأرجح إلى باريس في فرنسا، وتذكر أنه بكونها موطن وحدات القياس الدولية، يجب عرض د
likely benign
LLM, confidence 0.90
Arabic documentation sentence containing the word معامﻻت (parameters) written with a lam-alef presentation-form ligature.
Reasoning
Raw-pass unicode-mixed-script hit on Arabic prose in documentation. The 'Other' script is the Arabic lam-alef presentation-form ligature (U+FEFB), a common artifact of Arabic text copied from word processors. It is a typographic quirk, not an identifier used for evasion, and the surrounding text is ordinary documentation.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
هو**تعريفات** الدوال و**المعامﻻت** التي تحتاج إلى تمريرها إليها - فهي تهتم بما تفعله الأدوات وكيفية استخدامها، وليس بكيفية عملها! يقع على عاتقك قراءة مخرجاتها، والكشف عما إذا كان
likely benign
LLM, confidence 0.90
Arabic documentation word المعامﻻت (the parameters) using presentation-form ligature characters.
Reasoning
Same pattern: the lam-alef presentation-form ligature inside an ordinary Arabic word in a markdown doc. No code execution, no identifier substitution, no evasion context.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
استخدام أداة، وتمرير المعامﻻت إلى دالة الأداة، وإرجاع الرد في الدردشة.
likely benign
LLM, confidence 0.90
Arabic documentation line about passing parameters to a tool function, with a presentation-form ligature in the word المعامﻻت.
Reasoning
Ordinary Arabic prose in a documentation file with the lam-alef presentation-form ligature, a common copy-paste artifact. Raw pass has a higher false-positive rate and nothing here suggests malicious intent.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
� حيثما أمكن، مع تقليل المعامﻻت (وخاصة المعامﻻت المعقدة والمتداخلة) إلى الحد الأدنى.
likely benign
LLM, confidence 0.90
Arabic documentation sentence about minimizing parameters, using the presentation-form word المعامﻻت.
Reasoning
Arabic prose in markdown with the lam-alef presentation-form ligature. Typographic artifact, no executable context.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�قليل المعامﻻت (وخاصة المعامﻻت المعقدة والمتداخلة) إلى الحد الأدنى.
likely benign
LLM, confidence 0.90
Arabic documentation phrase المعامﻻت المعقدة والمتداخلة written with presentation-form ligatures.
Reasoning
Same ligature artifact in ordinary Arabic documentation prose; no code or obfuscation mechanism present.
-
identifier `مدخﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�بيل المثال، قد تتغير مدخﻻت السلسلة النصية من علامات اقتباس مفردة ' إلى علامات اقتباس مزدوجة "). يمكن أن يساعد إضافة "tojson" في ضمان الاتساق هنا.
likely benign
LLM, confidence 0.90
Arabic documentation sentence mentioning مدخﻻت (inputs) written with presentation-form ligatures.
Reasoning
Arabic documentation prose about string quoting in Jinja templates, with the lam-alef presentation-form ligature in مدخﻻت. A typographic artifact, not a malicious identifier.
-
identifier `وQwen` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�دثة الشهيرة، مثل Mixtral وQwen-MoE وDBRX، هي نماذج MoE. في هذه النماذج، لا تكون كل معلمة نشطة لكل رمز يتم إنشاؤه. ونتيجة لذلك، فإن نماذج MoE لديها عمومًا متطلبات ذاكرة أقل بكثير، على الرغم … (341 bytes total)
likely benign
LLM, confidence 0.92
Arabic documentation listing MoE model names, with the conjunction و attached to the Latin name Qwen.
Reasoning
Arabic prose naturally mixes Latin model names such as Mixtral, Qwen-MoE and DBRX, with the Arabic conjunction و attached directly to the Latin token. This is standard Arabic-Latin mixing in technical writing, not identifier obfuscation.
-
identifier `وDBRX` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�شهيرة، مثل Mixtral وQwen-MoE وDBRX، هي نماذج MoE. في هذه النماذج، لا تكون كل معلمة نشطة لكل رمز يتم إنشاؤه. ونتيجة لذلك، فإن نماذج MoE لديها عمومًا متطلبات ذاكرة أقل بكثير، على الرغم من أن … (340 bytes total)
likely benign
LLM, confidence 0.92
Arabic documentation sentence listing MoE models, with و attached to the Latin name DBRX.
Reasoning
Same natural Arabic-Latin mixing: the Arabic conjunction و attached to the Latin model name DBRX in a documentation list of MoE models.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�، بدلاً من تقسيمه إلى معامﻻت متعددة. يؤدي كتابة نموذجك بهذا الأسلوب إلى كود أبسط مع "مصدر حقيقة" واضح لأي فرط معلمات، كما يسهل إعادة استخدام الكود من نماذج أخرى في `transformers`.
likely benign
LLM, confidence 0.90
Arabic documentation sentence about splitting config into parameters, using presentation-form ligatures in معامﻻت.
Reasoning
Arabic prose in a model-authoring doc with the lam-alef presentation-form ligature in معامﻻت. A typographic artifact in documentation, with no code or evasion context.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
- يجب أن ترث من `PreTrainedConfig`،
- يجب أن تقبل دالة `__init__` الخاصة بـ `PreTrainedConfig` أي معامﻻت إضافية kwargs،
- يجب تمرير هذه المعامﻻت الإضافية إلى دالة `__init__` فى الفئة الأساسية الاعلى.
likely benign
LLM, confidence 0.90
Arabic documentation bullet about passing extra kwargs parameters, with presentation-form ligatures in معامﻻت.
Reasoning
Arabic bullet-point documentation about PreTrainedConfig kwargs, with the lam-alef presentation-form ligature. Typographic artifact, not code.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
- يجب أن تقبل دالة `__init__` الخاصة بـ `PreTrainedConfig` أي معامﻻت إضافية kwargs،
- يجب تمرير هذه المعامﻻت الإضافية إلى دالة `__init__` فى الفئة الأساسية الاعلى.
likely benign
LLM, confidence 0.90
Arabic documentation bullet about __init__ kwargs, using the presentation-form word المعامﻻت.
Reasoning
Same ligature artifact in Arabic documentation prose about __init__ kwargs. Benign.
-
identifier `تعديﻻتك` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
إذا قمت بتطوير تعديﻻتك الخاصة لنماذج Transformers وترغب في مشاركتها، ففكر في المساهمة في هذه الوثيقة.
likely benign
LLM, confidence 0.90
Arabic documentation sentence inviting contributions of custom model modifications, with presentation-form ligatures in تعديﻻتك.
Reasoning
Arabic prose in a documentation file with the lam-alef presentation-form ligature in تعديﻻتك. The word is an ordinary term for 'your modifications' and the context is a contribution invitation. No executable content.
-
identifier `وPyTorch` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
تم اختبار 🤗 Transformers على Python 3.10 والإصدارات الأحدث، وPyTorch 2.4 والإصدارات الأحدث. اتبع تعليمات التثبيت أدناه لمكتبة التعلم العميق التي تستخدمها:
likely benign
LLM, confidence 0.95
Arabic documentation sentence with the conjunction 'and' joined to the word PyTorch.
Reasoning
Arabic prose attaches the conjunction و directly to a Latin product name (وPyTorch). This is ordinary Arabic typesetting in a translated HuggingFace doc, and the raw pass flags it on script mixing alone.
-
identifier `وMacOs` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
قم بتفعيل البيئة الافتراضية. على Linux وMacOs:
```bash
likely benign
LLM, confidence 0.95
Arabic instruction text joining 'and' to the OS name MacOs.
Reasoning
The 'و' prefix on the Latin token MacOs is normal Arabic conjunction usage in prose. It is not an identifier and carries no code-execution or obfuscation risk.
-
identifier `وPyTorch` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
للحصول على الدعم الخاص بـ CPU فقط، يمكنك تثبيت 🤗 Transformers ومكتبة التعلم العميق في خطوة واحدة. على سبيل المثال، قم بتثبيت 🤗 Transformers وPyTorch باستخدام:
```bash
likely benign
LLM, confidence 0.95
Arabic install guide sentence containing 'and PyTorch'.
Reasoning
Same Arabic conjunction attached to PyTorch in a markdown install guide. The surrounding text is plain documentation with no executable or encoded content.
-
identifier `اFlash` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
2. **اFlash Attention:** إن Flash Attention وهي نسخة مُعدَّلة من خوارزمية الانتباه التي لا توفر فقط نهجًا أكثر كفاءة في استخدام الذاكرة، ولكنها تحقق أيضًا كفاءة متزايدة بسبب الاستخدام الأمثل ل
likely benign
LLM, confidence 0.95
Numbered list item in an Arabic LLM optimization tutorial naming Flash Attention.
Reasoning
The bold label 'اFlash Attention' is Arabic prose with a letter attached to a Latin technical term. It is a translation artifact in documentation, not an identifier or code.
-
identifier `وCSS` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
لم يتم ترجمة النص الخاص والروابط وأكواد HTML وCSS بناءً على طلبك.
likely benign
LLM, confidence 0.95
Arabic sentence about HTML and CSS code that was not translated.
Reasoning
Arabic sentence joining the conjunction و to the acronym CSS. This is normal Arabic prose in a markdown doc.
-
identifier `وLlama` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
أن LLMs الضخمة مثل GPT3/4، وLlama-2-70b، وClaude، وPaLM يمكن أن تعمل بسرعة كبيرة في واجهات الدردشة مثل [Hugging Face Chat](https://huggingface.co/chat/) أو ChatGPT يرجع إلى حد كبير إلى التحسينات المذكورة أعلاه في الدق… (340 bytes total)
likely benign
LLM, confidence 0.95
Arabic prose listing GPT, Llama-2-70b, Claude and PaLM.
Reasoning
Arabic list of LLM names where و attaches to Llama. The raw pass flags script mixing in running prose, which is expected for this language.
-
identifier `وClaude` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�خمة مثل GPT3/4، وLlama-2-70b، وClaude، وPaLM يمكن أن تعمل بسرعة كبيرة في واجهات الدردشة مثل [Hugging Face Chat](https://huggingface.co/chat/) أو ChatGPT يرجع إلى حد كبير إلى التحسينات المذكورة أعلاه في الدقة والخوا… (341 bytes total)
likely benign
LLM, confidence 0.95
Arabic prose listing LLM models, including Claude.
Reasoning
The conjunction و before the product name Claude is standard Arabic prose. No code or obfuscation is present.
-
identifier `وPaLM` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�ل GPT3/4، وLlama-2-70b، وClaude، وPaLM يمكن أن تعمل بسرعة كبيرة في واجهات الدردشة مثل [Hugging Face Chat](https://huggingface.co/chat/) أو ChatGPT يرجع إلى حد كبير إلى التحسينات المذكورة أعلاه في الدقة والخوارزميا… (340 bytes total)
likely benign
LLM, confidence 0.95
Arabic prose listing LLM models, including PaLM.
Reasoning
Same conjunction-plus-Latin-name pattern in Arabic documentation prose, here with PaLM.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
مرر معامﻻت التدريب كالمعتاد إلى [`Trainer`]:
```py
likely benign
LLM, confidence 0.90
Arabic tutorial sentence using a presentation-form ligature in the word 'arguments'.
Reasoning
The word معامﻻت uses the Arabic presentation-form ligature ﻻ (U+FEFB), a typographic variant that often appears in text copied from word processors. The surrounding text is a tutorial sentence about passing training arguments to Trainer, with no executable content.
-
identifier `وViTMAE` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
الرؤية الأخرى، مثل BeIT وViTMAE، الإلهام من هدف التدريب المسبق لـ BERT. يتم تدريب [BeIT](model_doc/beit) مسبقًا من خلال *نمذجة الصور المقنعة (MIM)*؛ يتم إخفاء رقّعات الصور بشكل عشوائي، كما يتم تحوي
likely benign
LLM, confidence 0.95
Arabic model-summary paragraph mentioning BeIT and ViTMAE.
Reasoning
Arabic prose linking BeIT and ViTMAE with the conjunction و. This is a documentation paragraph describing model research, with no code or encoding.
-
identifier `وSuperGLUE` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
�يب الخاضع للإشراف (GLUE وSuperGLUE) والتدريب ذاتي الإشراف (اختيار عينة عشوائية وحذف 15% من الرموز).
likely benign
LLM, confidence 0.95
Arabic sentence about GLUE and SuperGLUE supervised training.
Reasoning
The conjunction و attached to the benchmark name SuperGLUE is standard Arabic prose. The raw pass flags it on script mixing alone.
-
identifier `وBART` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
يستخدم محول Swin كمشفر وBART متعدد اللغات كمُفكّك تشفير. يتم تدريب Donut على قراءة النص عن طريق التنبؤ بالكلمة التالية بناءً على ملاحظات الصورة والنص. يقوم فك التشفير بتوليد تتسلسلً
likely benign
LLM, confidence 0.95
Arabic model-summary paragraph describing Swin, BART and Donut.
Reasoning
Arabic description of model architectures with the conjunction و before BART. This is documentation text with no executable or obfuscated content.
-
identifier `vإلى` mixes scripts: Latin + Arabic (raw pass, confidence 0.80)
�ائط البيانات الثلاث vإلى متجهات تضمين رمزيّة ومعالجتها بواسطة نموذج مشابه لـ GPT للتنبؤ برمز الإجراء المستقبلي.يقوم [محول المسار](model_doc/trajectory_transformer) أيضًا بتحويل الحالات وال… (341 bytes total)
dismissed
LLM, confidence 0.95
Arabic translation of the model summary docs containing a mixed-script token.
Reasoning
Markdown documentation in Arabic. The flagged token is an Arabic word with a Latin character, typical of mixed-script prose and a likely typo or copy-paste artifact. Raw-pass unicode heuristics fire on normal Arabic-Latin text here, with no code execution or hidden payload.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�اجة إليها. هناك ثلاثة معامﻻت تحتاجها لفهم آلية العمل: `padding`، و`truncation`، و`max_length`.
dismissed
LLM, confidence 0.95
Arabic documentation sentence about padding and truncation parameters.
Reasoning
Arabic prose in a markdown doc. The word uses Arabic presentation-form lam-alef ligature characters, a common artifact of copy-pasted or typeset Arabic text. Backticked parameter names are ordinary API names. No executable or hidden content.
-
identifier `لPEFT` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
1. حدد معرف النموذج لPEFT
2. مرره إلى فئة [`AutoModelForCausalLM`]
dismissed
LLM, confidence 0.95
Arabic PEFT guide step text joining an Arabic particle with the Latin term PEFT.
Reasoning
Arabic markdown instructions where the Latin acronym PEFT is attached to an Arabic preposition. This is ordinary mixed-script documentation text with no code or obfuscation.
-
identifier `للمدخﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
� [معالج](main_classes/processors) للمدخﻻت متعددة الوسائط).
dismissed
LLM, confidence 0.95
Arabic philosophy doc sentence referencing multimodal processors.
Reasoning
Arabic markdown prose using lam-alef presentation-form characters, a common typesetting artifact in Arabic text. The surrounding text is an ordinary documentation sentence about multimodal processors.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
تحقق من دليل المفاهيم [Padding and truncation](./pad_truncation) لمعرفة المزيد حول معامﻻت الحشو و البتر المختلفة.
</Tip>
dismissed
LLM, confidence 0.95
Arabic preprocessing doc sentence pointing readers to padding and truncation concepts.
Reasoning
Arabic markdown prose with a lam-alef ligature artifact in a word meaning parameters. Benign documentation text linking to a padding and truncation concept guide.
-
line length 550 characters (raw pass, confidence 0.50)
أولاً، دعنا نضيف بعض الزيادات إلى الصور. يمكنك استخدام أي مكتبة تفضلها، ولكن في هذا الدليل، سنستخدم وحدة [`transforms`](https://pytorch.org/vision/stable/transforms.html) من torchvision. إذا كنت مهتمًا باستخدام … (340 bytes total)
-
identifier `وTensorFlow` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
الأطر، بما في ذلك PyTorch وTensorFlow. عندما يتم تصدير نموذج إلى تنسيق ONNX، يتم استخدام هذه المشغلات لبناء رسم بياني حاسوبي (يُطلق عليه غالبًا اسم _تمثيل وسيط_) والذي يمثل تدفق البيانات ع… (340 bytes total)
dismissed
LLM, confidence 0.95
Arabic serialization doc describing PyTorch and TensorFlow export to ONNX.
Reasoning
Arabic prose where the Latin framework name TensorFlow is joined to the Arabic conjunction وmarking a mixed-script token. Ordinary documentation text about ONNX export with no code execution.
-
identifier `المعامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
للاطلاع على جميع المعامﻻت المتاحة، يرجى الرجوع إلى [وثائق 🤗 Optimum](https://huggingface.co/docs/optimum/exporters/onnx/usage_guides/export_a_model#exporting-a-model-to-onnx-using-the-cli)، أو عرض المساعدة في سطر الأوامر:
```bash
dismissed
LLM, confidence 0.95
Arabic serialization doc sentence referring to Optimum ONNX export CLI parameters.
Reasoning
Arabic markdown with a lam-alef presentation-form ligature in the word for parameters, a typical copy-paste artifact. The sentence points to Optimum docs and a CLI help command, which is benign.
-
identifier `وشبكةMLP` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
ت عبر تصنيفات الفئات، وشبكةMLP للتنبؤ بمربع الإحاطة.
dismissed
LLM, confidence 0.95
Arabic task explanation sentence mentioning an MLP head for bounding box prediction.
Reasoning
Arabic markdown prose joining the conjunction و to the Latin abbreviation MLP. Ordinary mixed-script technical writing describing object detection heads.
-
identifier `وMix` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
� كتلة من طبقات انتباه وMix-FFN. الغرض من هذا الأخير هو توفير معلومات موضعية. في نهاية كل كتلة مشفرة توجد طبقة *دمج الرقع* لإنشاء تمثيلات هرمية. يتم دمج ميزات كل مجموعة من الرقع المج… (341 bytes total)
dismissed
LLM, confidence 0.95
Arabic task explanation describing attention and Mix-FFN blocks in a vision transformer.
Reasoning
Arabic markdown prose joining the conjunction و to the term Mix-FFN, a Transformer component name. This is normal technical documentation with no code or hidden content.
-
identifier `وWordPiece` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
نظرًا لأن Unigram لا يعتمد على قواعد الدمج (على عكس BPE وWordPiece)، فإن للخوارزمية عدة طرق
توكنز نص جديد بعد التدريب. على سبيل المثال، إذا كان محول Unigram المدرب يعرض المفردات:
dismissed
LLM, confidence 0.95
Arabic tokenizer summary contrasting Unigram with BPE and WordPiece merge rules.
Reasoning
Arabic markdown prose mixing the conjunction و with the Latin term WordPiece in a tokenizer explanation. Ordinary documentation text.
-
identifier `وMistral` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
ب نماذج اللغة مثل Llama-2 وMistral باستخدام تقنيات التوليد اللغوي. كما يدعم [`~trl.SFTTrainer`] ميزات مثل حزم التسلسلات، وLoRA، والقياس الكمي، وDeepSpeed مما يُمكّن من التدريب بكفاءة على نماذج ضخمة
dismissed
LLM, confidence 0.95
Arabic trainer doc sentence listing model families supported by SFTTrainer.
Reasoning
Arabic markdown prose mentioning Llama-2 and Mistral with the conjunction و attached to the Latin model name. Benign documentation about TRL training features.
-
identifier `وLoRA` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
ات مثل حزم التسلسلات، وLoRA، والقياس الكمي، وDeepSpeed مما يُمكّن من التدريب بكفاءة على نماذج ضخمة الحجم.
dismissed
LLM, confidence 0.95
Arabic trainer doc sentence listing supported features including LoRA.
Reasoning
Arabic markdown list prose joining the conjunction و to the Latin term LoRA. Ordinary technical documentation with no executable or obfuscated content.
-
identifier `وDeepSpeed` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
، وLoRA، والقياس الكمي، وDeepSpeed مما يُمكّن من التدريب بكفاءة على نماذج ضخمة الحجم.
dismissed
LLM, confidence 0.95
Arabic trainer doc sentence listing supported features including DeepSpeed.
Reasoning
Arabic markdown list prose joining the conjunction و to the Latin term DeepSpeed. Benign documentation text about training features.
-
identifier `وNumPy` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
لحفاظ على حالات RNG Python وNumPy وPyTorch كما كانت عندما تم حفظ نقطة الحفظ. ولكن لأن PyTorch لديها العديد من الإعدادات الافتراضية غير الحتمية مُتنوعة، فإن حالات RNG ليست مضمونة لتكون هي نفسها
likely benign
LLM, confidence 0.90
Arabic sentence in trainer docs where 'و' is joined to the Latin name NumPy.
Reasoning
Arabic prose with a Latin product name attached by the Arabic conjunction waw (و). This is normal in Arabic technical writing and not an identifier-level obfuscation. The file is documentation.
-
identifier `وPyTorch` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
ظ على حالات RNG Python وNumPy وPyTorch كما كانت عندما تم حفظ نقطة الحفظ. ولكن لأن PyTorch لديها العديد من الإعدادات الافتراضية غير الحتمية مُتنوعة، فإن حالات RNG ليست مضمونة لتكون هي نفسها. إذا
likely benign
LLM, confidence 0.90
Arabic sentence where 'و' is joined to the Latin name PyTorch.
Reasoning
Same pattern as the sibling finding: the Arabic conjunction 'و' attached to the Latin word PyTorch in ordinary Arabic documentation prose.
-
line length 545 characters (raw pass, confidence 0.50)
يتم تعيين [`Trainer`] إلى `logging.INFO` افتراضيًا والذي يُبلغ عن الأخطاء والتحذيرات ومعلومات أساسية أخرى. يتم تعيين نسخة [`Trainer`] - في البيئات الموزعة - إلى `logging.WARNING` والتي يُبلغ فقط عن الأخ… (340 bytes total)
-
identifier `اﻷحداث` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
�ة إعداد مُستوى تسجيل اﻷحداث لكل عقدة، استخدم معامل [`log_on_each_node`](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#transformers.TrainingArguments.log_on_each_node) لتحديد ما إذا كان سيتم استخدام مُستوى السجل ع… (341 bytes total)
likely benign
LLM, confidence 0.85
Arabic word 'الأحداث' written with presentation-form glyphs in a documentation sentence.
Reasoning
The word uses Arabic presentation-form characters, typical of text copied from PDFs or word processors during translation. It is in prose in a markdown doc, not code.
-
identifier `معامﻻت` mixes scripts: Arabic + Other (raw pass, confidence 0.80)
لتمرير معامﻻت إضافية يدعمها GaLore، يجب عليك تمرير `optim_args` بشكل صحيح، على سبيل المثال:
```python
likely benign
LLM, confidence 0.85
Arabic word 'معاملات' containing ligature code points in a documentation sentence.
Reasoning
The Lam-Alef ligature code points are a common artifact of Arabic text copied from rich-text sources. The surrounding text is ordinary documentation prose.
-
[embedded python] identifier `معامﻻت` mixes scripts: Arabic + Other (in string/comment) (raw pass, confidence 0.40)
args = TrainingArguments(
output_dir="./test-grokadamw",
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
يستبدل التعلم بدون جدوله زخم المُحسِّن الأساسي بمزيج من المتوسط ⟨U+200B⟩⟨U+200B⟩والتداخل، لإزالة الحاجة تمامًا إلى تخفيف مُعدل التعلم باستخدام جدوله تقليديه.
likely benign
LLM, confidence 0.85
Zero-width space inside an Arabic documentation sentence about GaLore.
Reasoning
A zero-width space appears inside Arabic prose, a typical artifact of copy-pasted or machine-assisted translation. It has no effect on code execution in a markdown file.
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
يستبدل التعلم بدون جدوله زخم المُحسِّن الأساسي بمزيج من المتوسط ⟨U+200B⟩⟨U+200B⟩والتداخل، لإزالة الحاجة تمامًا إلى تخفيف مُعدل التعلم باستخدام جدوله تقليديه.
likely benign
LLM, confidence 0.85
Second zero-width space in the same Arabic documentation sentence.
Reasoning
Second zero-width space in the same Arabic sentence, consistent with translation or copy-paste artifacts rather than deliberate concealment.
-
identifier `وTensorFlow` mixes scripts: Arabic + Latin (raw pass, confidence 0.80)
- [🤗 دفاتر ملاحظات المحولات](notebooks) يحتوي على دفاتر ملاحظات مختلفة حول كيفية ضبط نموذج لمهمة محددة في PyTorch وTensorFlow.
likely benign
LLM, confidence 0.85
Arabic sentence where 'و' is attached to the Latin name TensorFlow.
Reasoning
Arabic prose referencing PyTorch and TensorFlow, with the Latin name joined to the Arabic conjunction 'و'. This is ordinary documentation text.
-
line length 694 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into
-
U+200B ZERO WIDTH SPACE in source (raw pass, confidence 0.75)
1. **[XLNet](model_doc/xlnet)** (from Google/CMU) released with the paper [⟨U+200B⟩XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://huggingface.co/papers/1906.08237) by Zhilin Yang*, Zihang Dai*, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
likely benign
LLM, confidence 0.90
Zero-width space before the XLNet paper title in a German-locale docs index.
Reasoning
A zero-width space sits before the paper title in a markdown list item. This is a common copy-paste artifact from the paper's web page and carries no executable payload.
-
line length 663 characters (raw pass, confidence 0.50)
{"id":"b48e84c1-b3c6-484d-b781-4c9ce8022dff","choices":[{"finish_reason":"stop","index":0,"message":{"content":"Apple Silicon is a type of processor technology developed and manufactured by Apple Inc., designed to offer faster performance and lower power consumption compared to traditional silicon-based processors. Thi
-
line length 640 characters (raw pass, confidence 0.50)
[NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel) is an open-source PyTorch DTensor-native training library from NVIDIA. It supports large and small scale pretraining and fine-tuning for [LLMs](https://docs.nvidia.com/nemo/automodel/latest/model-coverage/large-language-models/overview) and [VLMs](https://docs.
-
line length 641 characters (raw pass, confidence 0.50)
[NeMo Automodel](https://github.com/NVIDIA-NeMo/Automodel) is an open-source PyTorch DTensor-native training library from NVIDIA. It supports large and small scale pretraining and fine-tuning for [LLMs](https://docs.nvidia.com/nemo/automodel/latest/model-coverage/large-language-models/overview) and [VLMs](https://docs.
-
line length 503 characters (raw pass, confidence 0.50)
From the [transforms](https://pytorch.org/vision/stable/transforms.html) module, use the [Compose](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) API to chain together [RandomResizedCrop](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) and [Colo
-
[embedded python] string literal compression ratio 0.978 (134 bytes) (raw pass, confidence 0.60)
"content": [
{"type": "image", "url": "https://media.istockphoto.com/id/458012057/photo/istanbul-turkey.jpg?s=612x612&w=0&k=20&c=qogAOVvkpfUyqLUMr_XJQyq-HkACXyYUSZbKhBlPrxo="},
{"type": "text", "text": "Bu resimde hangi anıt gösterilmektedir?"},
]},
likely benign
LLM, confidence 0.90
Image URL string literal in a documentation chat-template example.
Reasoning
The flagged string is a long image URL with a base64-style query token in a JSON-like chat message example. Compression ratio is expected for URLs and the file is documentation.
-
[embedded python] string literal compression ratio 0.978 (134 bytes) (raw pass, confidence 0.60)
"type": "image",
"url": "https://media.istockphoto.com/id/458012057/photo/istanbul-turkey.jpg?s=612x612&w=0&k=20&c=qogAOVvkpfUyqLUMr_XJQyq-HkACXyYUSZbKhBlPrxo=",
},
{"type": "text", "text": "Where was this taken ?"},
likely benign
LLM, confidence 0.90
Image URL string in a documentation multimodal message example.
Reasoning
Same image URL with a query-string token inside a documentation message example. The high-complexity flag fires on a URL, not on obfuscated code.
-
[embedded python] base64-like blob (46 bytes, compression ratio 1.24) (raw pass, confidence 0.60)
```py
url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
likely benign
LLM, confidence 0.90
Image URL whose query-string token triggered a base64-like heuristic in a documentation code sample.
Reasoning
The matched blob is the query parameter 'c=' inside an ordinary image URL assigned to a variable in a documentation example. It is not an encoded payload.
-
[embedded python] base64-like blob (46 bytes, compression ratio 1.24) (raw pass, confidence 0.60)
```py
url_cow = "https://media.istockphoto.com/id/1192867753/photo/cow-in-berchida-beach-siniscola.jpg?s=612x612&w=0&k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4="
url_cat = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
dismissed
LLM, confidence 0.95
A Python docs example assigns an image URL whose query string contains a base64-like token.
Reasoning
The flagged value is the trailing query-string parameter of an image URL (k=20&c=v0hjjniwsMNfJSuKWZuIn8pssmD5h5bSN1peBd1CmH4=). It is an istockphoto URL token, not an encoded payload, and the code only assigns it to a string variable in a documentation example.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
messages = [
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
This is a raw-pass whitespace finding on a Python code block in Markdown docs. Non-breaking spaces in indentation are a common copy-paste artifact from web pages. The snippet only builds a chat message list with image URLs and has no execution or obfuscation behavior.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/llava_view.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "Write a haiku for this image"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.85
A docs code example for InternVL chat messages contains non-breaking spaces in its indentation.
Reasoning
Same pattern as the other lines in this docs code block: U+00A0 in indentation of a message-list literal, consistent with copy-paste from a rendered page. No executable or malicious content is shown.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.88
A documentation code example in internvl.md whose indentation contains non-breaking spaces.
Reasoning
Raw-pass whitespace-anomaly in a markdown docs example. The U+00A0 characters sit in the indentation of a Python chat-template example, consistent with copy-paste from a web page or rich editor. The line is inert documentation text with no execution path, so this is a formatting artifact rather than an obfuscation technique.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/dreamstime_golden_gate_flowers.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
likely benign
LLM, confidence 0.88
A documentation example line with non-breaking-space indentation in a chat message list for InternVL.
Reasoning
Same pattern as the other findings in this file: non-breaking spaces in the indentation of a markdown Python example that only references public image URLs. Benign formatting artifact in documentation.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
likely benign
LLM, confidence 0.88
A closing bracket line in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown code sample. The content is a static data literal with no execution or network side effects beyond a displayed URL. Consistent with copy-paste artifacts.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.88
A closing bracket line in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a static markdown example. No executable context and no hidden payload; the characters only affect whitespace rendering.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
likely benign
LLM, confidence 0.88
A closing bracket in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown code block. Structural closing brackets of a data literal, with no code path that could be exploited.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
]
likely benign
LLM, confidence 0.88
A closing bracket in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown code block that closes a data structure. Documentation formatting artifact with no malicious significance.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
messages = [
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
likely benign
LLM, confidence 0.88
The opening of a messages list in an InternVL documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown Python example. Static documentation text with no execution context, consistent with copy-paste formatting.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
likely benign
LLM, confidence 0.88
A list opening in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a static markdown example. The surrounding content is a standard chat-message structure, and the characters only affect whitespace.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.88
A chat message dictionary in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown code sample defining a chat message with a public image URL. No hidden logic or execution; benign documentation artifact.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.88
A message content entry in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a static documentation example referencing a public image URL. Formatting artifact with no security relevance.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/statue_of_liberty.jpg"},
likely benign
LLM, confidence 0.88
A content list entry in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown example. Static data literal, no execution or obfuscation beyond whitespace characters that do not change behavior in documentation.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "image", "url": "https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/dreamstime_golden_gate_flowers.jpg"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
likely benign
LLM, confidence 0.88
An image and text entry in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a markdown example. Same benign copy-paste pattern as the rest of the block.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
likely benign
LLM, confidence 0.88
A closing bracket in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a static markdown example that closes a message structure. Benign documentation formatting artifact.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "These images depict two different landmarks. Can you identify them?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.88
A closing bracket in a documentation example with non-breaking-space indentation.
Reasoning
Non-breaking spaces in the indentation of a static markdown code block closing a messages list. No executable or hidden content; benign formatting artifact.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
likely benign
LLM, confidence 0.88
A U+00A0 non-breaking space in the indentation of a chat-template example in the InternVL model documentation.
Reasoning
Raw-pass whitespace-anomaly in a markdown doc's Python example. Non-breaking spaces in indentation are a common copy-paste artifact from web-rendered docs, and the surrounding code is an ordinary chat-message example. No execution or hidden payload.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
likely benign
LLM, confidence 0.88
A non-breaking space in indentation inside an InternVL docs chat-message example.
Reasoning
Same pattern as the other internvl.md whitespace findings: a non-breaking space in indentation of a documentation code sample. This is a doc copy-paste artifact, not obfuscation.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
likely benign
LLM, confidence 0.88
A non-breaking space in indentation in an InternVL documentation message-list example.
Reasoning
Non-breaking space in the indentation of a documentation example listing chat messages with a public video URL. Consistent with a formatting artifact; no hidden logic.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a documentation chat-template snippet.
Reasoning
Non-breaking space in indentation of a docs code block. The code is plain data structures for a multimodal chat example; the whitespace is a formatting artifact.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a docs example referencing a test video fixture.
Reasoning
Non-breaking space in indentation in a documentation example that references a Hugging Face test fixture video. The whitespace is cosmetic and does not change any executed behavior of a real program.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "What type of shot is the man performing?"},
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of an InternVL docs video-chat example.
Reasoning
Non-breaking space in indentation within a markdown documentation Python sample. Typical doc-formatting residue from copy-paste.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "What type of shot is the man performing?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of an InternVL docs message example.
Reasoning
Non-breaking space in indentation of a documentation code sample. No suspicious code is present, only a formatting artifact.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "video", "url": "https://huggingface.co/datasets/hf-internal-testing/fixtures_videos/resolve/main/tennis.mp4"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "What type of shot is the man performing?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a documentation chat example.
Reasoning
Non-breaking space in indentation within a markdown documentation example. Benign formatting residue.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {"type": "text", "text": "What type of shot is the man performing?"},
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a docs chat-template example.
Reasoning
Non-breaking space in indentation of a documentation code block for a chat template. Cosmetic artifact; no execution or encoding involved.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of an InternVL docs example.
Reasoning
Non-breaking space in indentation in a documentation example. Matches the same benign formatting pattern across this file.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ },
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a docs message-list example.
Reasoning
Non-breaking space in indentation of a documentation sample. Copy-paste whitespace residue, not malicious.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ ],
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of an InternVL documentation snippet.
Reasoning
Non-breaking space in indentation inside a markdown documentation code block. Benign.
-
[embedded python] invisible whitespace U+00A0 in indentation (raw pass, confidence 0.80)
⟨U+00A0⟩ ⟨U+00A0⟩ [
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ {
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "role": "user",
⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ ⟨U+00A0⟩ "content": [
likely benign
LLM, confidence 0.88
A non-breaking space in indentation of a docs chat-template example.
Reasoning
Non-breaking space in indentation of a documentation example. The whitespace is a formatting artifact and the code is plain data for a chat template.
-
[embedded python] line length 1654 characters (raw pass, confidence 0.50)
device_map = {'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 0, 'model.layers.8': 0, 'model.layers.9': 1, 'model.layers.10': 1, 'model.layers.11': 1, 'model.layers.12': 1, 'mode
-
line length 542 characters (raw pass, confidence 0.50)
- A [notebook](https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/LayoutLMv2/FUNSD/Inference_with_LayoutLMv2ForTokenClassification.ipynb) for how to perform inference with [`LayoutLMv2ForTokenClassification`] and a [notebook](https://colab.research.google.com/github/NielsRogge/Transf
-
line length 603 characters (raw pass, confidence 0.50)
[torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) compiles PyTorch code into optimized kernels that significantly speed up inference. This feature relies on [TorchDynamo](https://pytorch.org/docs/stable/torch.compiler_dynamo_overview.html) to compile the code into graphs and [Torch
-
line length 585 characters (raw pass, confidence 0.50)
The [ChunkPipeline](https://github.com/huggingface/transformers/blob/99e0ab6ed888136ea4877c6d8ab03690a1478363/src/transformers/pipelines/base.py#L1387) class is designed to handle these use cases. Both pipeline classes are used in the same way, but since [ChunkPipeline](https://github.com/huggingface/transformers/blob/
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line length 547 characters (raw pass, confidence 0.50)
0. The best starting point would be to have a look at another quantization method such as Finegrained Fp8. You will have to update or create three files in total: the [config file](https://github.com/huggingface/transformers/blob/main/src/transformers/utils/quantization_config.py), the [integration file](https://github
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line length 602 characters (raw pass, confidence 0.50)
1. Create a new quantization config class inside [src/transformers/utils/quantization_config.py](https://github.com/huggingface/transformers/blob/abbffc4525566a48a9733639797c812301218b83/src/transformers/utils/quantization_config.py). Add the new quantization config to the [\_import_structure](https://github.com/huggin
-
[embedded python] line length 3001 characters (mostly string/comment) (raw pass, confidence 0.39)
Words: ['wie', 'baw', 'brown', '&', 'williamson', 'tobacco', 'corporation', 'research', '&', 'development', 'internal', 'correspondence', 'to:', 'r.', 'h.', 'honeycutt', 'ce:', 't.f.', 'riehl', 'from:', '.', 'c.j.', 'cook', 'date:', 'may', '8,', '1995', 'subject:', 'review', 'of', 'existing', 'brainstorming', 'ideas/48
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[embedded bash] command name is a variable or substitution — executed command determined at runtime (ast pass, confidence 0.90)
pip install -q datasets monai trackio
```⟨U+00A0⟩
We can now load our dataset and take a look.
dismissed
LLM, confidence 0.85
A markdown docs pip install line in a mask generation tutorial, flagged as a shell command with a runtime-determined name.
Reasoning
The flagged 'command' is 'pip install -q datasets monai trackio' inside a markdown code fence, followed by prose ('We can now load our dataset'). This is a documentation install instruction. The critical dynamic-execution label most likely comes from the bash parser misreading the closing fence and following prose as a shell command with a variable name; no variable-determined command is actually present.
-
line length 682 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (de HuggingFace), publicado junto con el paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) por Victor Sanh, Lysandre Debut y Thomas Wolf. Se ha aplicado el mismo método para comprimir GPT2 en [Distil
-
line length 609 characters (raw pass, confidence 0.50)
El [`Trainer`] está configurado a `logging.INFO` de forma predeterminada el cual informa errores, advertencias y otra información basica. Un [`Trainer`] réplica - en entornos distributos - está configurado a `logging.WARNING` el cual solamente informa errores y advertencias. Puedes cambiar el nivel de logging con l
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line length 694 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into
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line length 600 characters (raw pass, confidence 0.50)
Tous les modèles sont des modèles standard [`tf.keras.Model`](https://www.tensorflow.org/api_docs/python/tf/keras/Model) afin qu'ils puissent être entraînés avec TensorFlow avec l'API [Keras](https://keras.io/). 🤗 Transformers fournit la fonction [`~TFPreTrainedModel.prepare_tf_dataset`] pour charger facilement
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line length 685 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (da HuggingFace), rilasciato assieme al paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) da Victor Sanh, Lysandre Debut e Thomas Wolf. La stessa tecnica è stata applicata per comprimere GPT2 in [Dis
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line length 511 characters (raw pass, confidence 0.50)
[Reformer](model_doc/reformer)は軸方向の位置エンコーディングを使用しています。伝統的なトランスフォーマーモデルでは、位置エンコーディングEはサイズが \\(l\\) × \\(d\\) の行列で、\\(l\\) はシーケンスの長さ、\\(d\\) は隠れ状態の次元です。… (340 bytes total)
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line length 538 characters (raw pass, confidence 0.50)
ALIGNモデルは、「[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://huggingface.co/papers/2102.05918)」という論文でChao Jia、Yinfei Yang、Ye Xia、Yi-Ting Chen、Zarana Parekh、Hieu Pham、Quoc V. Le、Yunhsuan Sung、Zhen Li、Tom Duerigによって提… (339 bytes total)
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line length 827 characters (raw pass, confidence 0.50)
*事前学習された表現は、多くの自然言語処理（NLP）および知覚タスクにとって重要になっています。NLPにおける表現学習は、人間のアノテーションのない生のテキストでの学習へと移行していますが、視覚および視覚言語の表現は依然と… (340 bytes total)
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line length 502 characters (raw pass, confidence 0.50)
*過去10年間で、畳み込みニューラルネットワーク（CNN）は、音声スペクトログラムから対応するラベルへの直接的なマッピングを学習することを目指す、エンドツーエンドの音声分類モデルの主要な構成要素として広く採用されてきまし… (339 bytes total)
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line length 617 characters (raw pass, confidence 0.50)
*例えば異常気象の早期警告や長期的なエネルギー消費計画といった実応用において、予測時間を延長することは重要な要求です。本論文では、時系列の長期予測問題を研究しています。以前のTransformerベースのモデルは、長距離依存関係… (339 bytes total)
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line length 593 characters (raw pass, confidence 0.50)
*事前トレーニング済み言語モデルは、一般的な自然言語領域での大きな成功に触発されて、生物医学領域でますます注目を集めています。一般言語ドメインの事前トレーニング済み言語モデルの 2 つの主なブランチ、つまり BERT (およびそ
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line length 596 characters (raw pass, confidence 0.50)
*事前トレーニングされた表現の転送により、サンプル効率が向上し、視覚用のディープ ニューラル ネットワークをトレーニングする際のハイパーパラメーター調整が簡素化されます。大規模な教師ありデータセットでの事前トレーニン… (339 bytes total)
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line length 557 characters (raw pass, confidence 0.50)
*大規模モデルのエンドツーエンドのトレーニングにより、視覚と言語の事前トレーニングのコストはますます法外なものになってきています。この論文では、市販の凍結済み事前トレーニング画像エンコーダと凍結された大規模言語モデ… (340 bytes total)
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line length 615 characters (raw pass, confidence 0.50)
*文書画像からの重要情報抽出 (KIE) には、2 次元 (2D) 空間におけるテキストの文脈的および空間的意味論を理解する必要があります。最近の研究の多くは、文書画像の視覚的特徴とテキストおよびそのレイアウトを組み合わせることに重点… (340 bytes total)
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line length 558 characters (raw pass, confidence 0.50)
*CLIP の大成功 (Radford et al., 2021) により、視覚言語の事前訓練のための対照学習の研究と応用が促進されました。この研究では、ほとんどのデータが公開されているデータセットから取得された中国語の画像とテキストのペアの大規模なデ… (339 bytes total)
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line length 576 characters (raw pass, confidence 0.50)
*対照学習は、マルチモーダル表現学習の分野で目覚ましい成功を収めています。この論文では、音声データと自然言語記述を組み合わせて音声表現を開発する、対照的な言語音声事前トレーニングのパイプラインを提案します。この目標… (340 bytes total)
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line length 511 characters (raw pass, confidence 0.50)
内部では、トークナイザーが [`<FILL_ME>` によって自動的に分割](https://huggingface.co/docs/transformers/main/model_doc/code_llama#transformers.CodeLlamaTokenizer.fill_token) して、[ に続く書式設定された入力文字列を作成します。オリジナルのトレーニング パター… (340 bytes total)
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line length 806 characters (raw pass, confidence 0.50)
*プログラム合成は、与えられた問題仕様の解決策としてコンピューター プログラムを生成することを目的としています。我々は、大規模な言語モデルを介した会話型プログラム合成アプローチを提案します。これは、従来のアプローチで
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line length 709 characters (raw pass, confidence 0.50)
*最近開発された DETR アプローチは、トランスフォーマー エンコーダーおよびデコーダー アーキテクチャを物体検出に適用し、有望なパフォーマンスを実現します。この論文では、トレーニングの収束が遅いという重要な問題を扱い、高… (339 bytes total)
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line length 736 characters (raw pass, confidence 0.50)
*アーキテクチャの改善と表現学習フレームワークの改善により、視覚認識の分野は 2020 年代初頭に急速な近代化とパフォーマンスの向上を実現しました。たとえば、ConvNeXt に代表される最新の ConvNet は、さまざまなシナリオで強力なパフ… (340 bytes total)
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line length 686 characters (raw pass, confidence 0.50)
*チャートやプロットなどの視覚言語は人間の世界に遍在しています。プロットやチャートを理解するには、強力な推論スキルが必要です。従来の最先端 (SOTA) モデルには少なくとも数万のトレーニング サンプルが必要であり、その推論能… (340 bytes total)
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line length 722 characters (raw pass, confidence 0.50)
PytorchにはDeepSpeedが構築したものもあります: [DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale](https://huggingface.co/papers/2201.05596)、[Mixture of Experts](https://www.deepspeed.ai/tutorials/mixture-of-experts/) - ブログ記事: [1](https://www.m
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[embedded python] line length 1063 characters (raw pass, confidence 0.50)
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED', 'chunks': [{'text': 'I', 'timestamp': (1.22, 1.24)}, {'text': 'HAVE', 'timestamp': (1.42, 1.58)}, {'text': 'A', 'timestamp': (1.66, 1.68)}, {'text': 'DREAM', 'timestamp': (1.76, 2.14)}, {'text': 'BUT', 'timestamp'
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[embedded python] line length 3001 characters (mostly string/comment) (raw pass, confidence 0.39)
Words: ['wie', 'baw', 'brown', '&', 'williamson', 'tobacco', 'corporation', 'research', '&', 'development', 'internal', 'correspondence', 'to:', 'r.', 'h.', 'honeycutt', 'ce:', 't.f.', 'riehl', 'from:', '.', 'c.j.', 'cook', 'date:', 'may', '8,', '1995', 'subject:', 'review', 'of', 'existing', 'brainstorming', 'ideas/48
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line length 803 characters (raw pass, confidence 0.50)
**注意**: 上記のデータセット パイプラインは、[公式 PyTorchVideo サンプル](https://pytorchvideo.org/docs/tutorial_classification#dataset) から取得したものです。 [`pytorchvideo.data.Ucf101()`](https://pytorchvideo.readthedocs.io/en/latest/api/data/data.html#pytorchvideo.data.Ucf101) … (339 bytes total)
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line length 694 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into
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[embedded python] line length 1654 characters (raw pass, confidence 0.50)
device_map = {'model.embed_tokens': 0, 'model.layers.0': 0, 'model.layers.1': 0, 'model.layers.2': 0, 'model.layers.3': 0, 'model.layers.4': 0, 'model.layers.5': 0, 'model.layers.6': 0, 'model.layers.7': 0, 'model.layers.8': 0, 'model.layers.9': 1, 'model.layers.10': 1, 'model.layers.11': 1, 'model.layers.12': 1, 'mode
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[embedded python] string literal compression ratio 0.993 (138 bytes) (raw pass, confidence 0.70)
messages = [
{"role": "user", "content": f"다음 텍스트들을 보세요: [{very_long_text}]\n\n\n\n책들은 무엇이며, 누가 썼나요? 좋은 목록을 만들어주세요."},
]
input_ids = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
likely benign
LLM, confidence 0.85
A Korean-language chat message f-string in a Llama 4 documentation example, flagged for high compression complexity.
Reasoning
The raw-pass high-complexity flag is on a short Korean f-string prompt of about 138 bytes. Such a string of natural-language UTF-8 text with a few escaped newlines has high byte entropy and a poor compression ratio by nature, so the ratio of 0.993 is expected. The surrounding code is a standard apply_chat_template example, with no decoding or execution.
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[embedded python] line length 1063 characters (raw pass, confidence 0.50)
{'text': 'I HAVE A DREAM BUT ONE DAY THIS NATION WILL RISE UP AND LIVE OUT THE TRUE MEANING OF ITS CREED', 'chunks': [{'text': 'I', 'timestamp': (1.22, 1.24)}, {'text': 'HAVE', 'timestamp': (1.42, 1.58)}, {'text': 'A', 'timestamp': (1.66, 1.68)}, {'text': 'DREAM', 'timestamp': (1.76, 2.14)}, {'text': 'BUT', 'timestamp'
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[embedded python] line length 3001 characters (mostly string/comment) (raw pass, confidence 0.39)
Words: ['wie', 'baw', 'brown', '&', 'williamson', 'tobacco', 'corporation', 'research', '&', 'development', 'internal', 'correspondence', 'to:', 'r.', 'h.', 'honeycutt', 'ce:', 't.f.', 'riehl', 'from:', '.', 'c.j.', 'cook', 'date:', 'may', '8,', '1995', 'subject:', 'review', 'of', 'existing', 'brainstorming', 'ideas/48
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line length 694 characters (raw pass, confidence 0.50)
1. **[DistilBERT](model_doc/distilbert)** (from HuggingFace), released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://huggingface.co/papers/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into
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line length 525 characters (raw pass, confidence 0.50)
Din modulul [transforms](https://pytorch.org/vision/stable/transforms.html), folosește API-ul [Compose](https://pytorch.org/vision/master/generated/torchvision.transforms.Compose.html) ca să înlănțuiești [RandomResizedCrop](https://pytorch.org/vision/main/generated/torchvision.transforms.RandomResizedCrop.html) … (340 bytes total)
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line length 641 characters (raw pass, confidence 0.50)
Clasa [ChunkPipeline](https://github.com/huggingface/transformers/blob/99e0ab6ed888136ea4877c6d8ab03690a1478363/src/transformers/pipelines/base.py#L1387) este concepută pentru a gestiona aceste cazuri de utilizare. Ambele clase de pipeline sunt folosite în același mod, dar deoarece [ChunkPipeline](https://github.com
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line length 849 characters (raw pass, confidence 0.50)
- [التلخيص باستخدام BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+constructio
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line length 1366 characters (raw pass, confidence 0.50)
- [الرد على الأسئلة باستخدام DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selv
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line length 869 characters (raw pass, confidence 0.50)
- [Automatische Textzusammenfassung mit BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+constr
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line length 1363 characters (raw pass, confidence 0.50)
- [Question Answering mit DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Amazo
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line length 844 characters (raw pass, confidence 0.50)
- [Resumen con BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+Towe
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line length 1366 characters (raw pass, confidence 0.50)
- [Responder a preguntas con DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Am
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line length 844 characters (raw pass, confidence 0.50)
- [Résumé avec BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffel+To
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line length 1367 characters (raw pass, confidence 0.50)
- [Réponse aux questions avec DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+
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line length 851 characters (raw pass, confidence 0.50)
- [बार्ट के साथ पाठ सारांश](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+sid
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line length 1360 characters (raw pass, confidence 0.50)
- [डिस्टिलबर्ट के साथ प्रश्नोत्तर](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C
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line length 840 characters (raw pass, confidence 0.50)
- [BART를 이용한 요약](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eif
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line length 1351 characters (raw pass, confidence 0.50)
- [DistilBERT를 이용한 질문 답변](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+A
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line length 848 characters (raw pass, confidence 0.50)
- [Sumarização com BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C+the+Eiffe
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line length 1365 characters (raw pass, confidence 0.50)
- [Resposta a perguntas com DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C+Ama
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line length 852 characters (raw pass, confidence 0.50)
- [Обобщение с помощью BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+constr
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line length 1368 characters (raw pass, confidence 0.50)
- [Ответы на вопросы с помощью DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+S
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line length 843 characters (raw pass, confidence 0.50)
- [BART తో సారాంశం](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2
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line length 1358 characters (raw pass, confidence 0.50)
- [DistilBERT తో ప్రశ్న సమాధానం](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selv
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line length 858 characters (raw pass, confidence 0.50)
- [‏BART کے ساتھ خلاصہ کاری](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+co
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line length 1372 characters (raw pass, confidence 0.50)
- [‏DistilBERT کے ساتھ سوالات کے جوابات](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+
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line length 852 characters (raw pass, confidence 0.50)
- [Tóm tắt văn bản với BART](https://huggingface.co/facebook/bart-large-cnn?text=The+tower+is+324+metres+%281%2C063+ft%29+tall%2C+about+the+same+height+as+an+81-storey+building%2C+and+the+tallest+structure+in+Paris.+Its+base+is+square%2C+measuring+125+metres+%28410+ft%29+on+each+side.+During+its+construction%2C
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line length 1360 characters (raw pass, confidence 0.50)
- [Trả lời câu hỏi với DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased-distilled-squad?text=Which+name+is+also+used+to+describe+the+Amazon+rainforest+in+English%3F&context=The+Amazon+rainforest+%28Portuguese%3A+Floresta+Amaz%C3%B4nica+or+Amaz%C3%B4nia%3B+Spanish%3A+Selva+Amaz%C3%B3nica%2C
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line length 862 characters (raw pass, confidence 0.50)
vocab += [("ar_AR", 0.0), ("cs_CZ", 0.0), ("de_DE", 0.0), ("en_XX", 0.0), ("es_XX", 0.0), ("et_EE", 0.0), ("fi_FI", 0.0), ("fr_XX", 0.0), ("gu_IN", 0.0), ("hi_IN", 0.0), ("it_IT", 0.0), ("ja_XX", 0.0), ("kk_KZ", 0.0), ("ko_KR", 0.0), ("lt_LT", 0.0), ("lv_LV", 0.0), ("my_MM", 0.0), ("ne_NP", 0.0), ("nl_XX", 0.0)
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file mixes tab and space indentation (2 tab-indented and 3906 space-indented lines) (raw pass, confidence 0.40)
from ..cache_utils import (
Cache,
DynamicCache,
EncoderDecoderCache,
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identifier `Σ_h` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
The scoring logic computes:
index_score[b,s,t] = Σ_h (weight[b,s,h] · softmax_scale · q[b,s,h,:] · k[b,t,:])
Args:
-
identifier `Σ_h` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
The scoring logic computes:
index_score[b,s,t] = Σ_h (weight[b,s,h] · softmax_scale · q[b,s,h,:] · k[b,t,:])
Args:
-
identifier `Σ_h` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
The scoring logic computes:
index_score[b,s,t] = Σ_h (weight[b,s,h] · softmax_scale · q[b,s,h,:] · k[b,t,:])
Args:
-
identifier `θ_i` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
Gemma3's per-layer-type buffers), specialised for V4's *interleaved* RoPE.
Interleaved RoPE: one `θ_i` per pair (`rope_head_dim // 2` entries),
DIFF no end-to-end duplication. Same shape as `inv_freq @ position_ids`.
-
identifier `θ_i` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
Gemma3's per-layer-type buffers), specialised for V4's *interleaved* RoPE.
Interleaved RoPE: one `θ_i` per pair (`rope_head_dim // 2` entries),
DIFF no end-to-end duplication. Same shape as `inv_freq @ position_ids`.
-
identifier `K1ᵀ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
Computes ``(softmax(Q1 K1ᵀ) - λ · softmax(Q2 K2ᵀ)) · V`` as two standard attention calls
sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
-
identifier `K2ᵀ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
Computes ``(softmax(Q1 K1ᵀ) - λ · softmax(Q2 K2ᵀ)) · V`` as two standard attention calls
sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
-
identifier `K1ᵀ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
Computes ``(softmax(Q1 K1ᵀ) - λ · softmax(Q2 K2ᵀ)) · V`` as two standard attention calls
sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
-
identifier `K2ᵀ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
Computes ``(softmax(Q1 K1ᵀ) - λ · softmax(Q2 K2ᵀ)) · V`` as two standard attention calls
sharing Q and K over the two halves of V. The two-call structure is ~30% faster than the
V-doubling shortcut at production shapes, since asymmetric V (``head_dim_v != head_dim_q``)
-
identifier `Cβ` mixes scripts: Latin + Greek (in string/comment) (raw pass, confidence 0.40)
distogram_atom_idx (`torch.Tensor` of shape `(batch_size, num_tokens)`):
Index of the representative atom (Cβ, or Cα for glycine) of each token. Used by the
confidence head; the trunk does not need it.
num_diffusion_samples (`int`, *optional*):
-
identifier `Cα` contains homoglyph candidates: α (03B1)→a (in string/comment) (raw pass, confidence 0.35)
distogram_atom_idx (`torch.Tensor` of shape `(batch_size, num_tokens)`):
Index of the representative atom (Cβ, or Cα for glycine) of each token. Used by the
confidence head; the trunk does not need it.
num_diffusion_samples (`int`, *optional*):
-
identifier `Cα` mixes scripts: Latin + Greek (in string/comment) (raw pass, confidence 0.40)
distogram_atom_idx (`torch.Tensor` of shape `(batch_size, num_tokens)`):
Index of the representative atom (Cβ, or Cα for glycine) of each token. Used by the
confidence head; the trunk does not need it.
num_diffusion_samples (`int`, *optional*):
-
identifier `Cα` contains homoglyph candidates: α (03B1)→a (in string/comment) (raw pass, confidence 0.35)
plddt_ca (`torch.FloatTensor` of shape `(num_diffusion_samples, num_tokens)`):
Predicted lDDT at the representative (Cα) atom of each token.
complex_plddt (`torch.FloatTensor` of shape `(num_diffusion_samples,)`):
Mean pLDDT over all atoms of the complex.
-
identifier `Cα` mixes scripts: Latin + Greek (in string/comment) (raw pass, confidence 0.40)
plddt_ca (`torch.FloatTensor` of shape `(num_diffusion_samples, num_tokens)`):
Predicted lDDT at the representative (Cα) atom of each token.
complex_plddt (`torch.FloatTensor` of shape `(num_diffusion_samples,)`):
Mean pLDDT over all atoms of the complex.
-
identifier `Cα` contains homoglyph candidates: α (03B1)→a (in string/comment) (raw pass, confidence 0.35)
plddt_ca (`torch.FloatTensor` of shape `(num_diffusion_samples, num_tokens)`):
Predicted lDDT at the representative (Cα) atom of each token.
complex_plddt (`torch.FloatTensor` of shape `(num_diffusion_samples,)`):
Mean pLDDT over all atoms of the complex.
-
identifier `Cα` mixes scripts: Latin + Greek (in string/comment) (raw pass, confidence 0.40)
plddt_ca (`torch.FloatTensor` of shape `(num_diffusion_samples, num_tokens)`):
Predicted lDDT at the representative (Cα) atom of each token.
complex_plddt (`torch.FloatTensor` of shape `(num_diffusion_samples,)`):
Mean pLDDT over all atoms of the complex.
-
identifier `Σ_h` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
The scoring logic computes:
index_score[b,s,t] = Σ_h (weight[b,s,h] · softmax_scale · q[b,s,h,:] · k[b,t,:])
Args:
-
line length 520 characters (raw pass, confidence 0.50)
image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [153
-
line length 520 characters (raw pass, confidence 0.50)
image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [153
-
line length 520 characters (raw pass, confidence 0.50)
image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [153
-
line length 520 characters (raw pass, confidence 0.50)
image_grid_pinpoints = [[384, 384], [384, 768], [384, 1152], [384, 1536], [384, 1920], [384, 2304], [768, 384], [768, 768], [768, 1152], [768, 1536], [768, 1920], [768, 2304], [1152, 384], [1152, 768], [1152, 1152], [1152, 1536], [1152, 1920], [1152, 2304], [1536, 384], [1536, 768], [1536, 1152], [1536, 1536], [153
-
line length 618 characters (raw pass, confidence 0.50)
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "l
-
line length 506 characters (raw pass, confidence 0.50)
FAIRSEQ_LANGUAGE_CODES = ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
for idx, decoder_layer in enumerate(self.layers):
reference_points_input = reference_points.unsqueeze(2)
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
num_backbone_outs = len(intermediate_channel_sizes)
encoder_input_proj_list = []
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
num_backbone_outs = len(config.decoder_in_channels)
decoder_input_proj_list = []
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
sources = []
for level, source in enumerate(encoder_outputs.last_hidden_state):
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
num_backbone_outs = len(intermediate_channel_sizes)
-
base64-like blob (64 bytes, compression ratio 1.17) (in comment) (raw pass, confidence 0.36)
num_backbone_outs = len(config.decoder_in_channels)
decoder_input_proj_list = []
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βkk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `βvk` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
last_recurrent_state = last_recurrent_state * chunk_decay[:, :, i] + key[:, :, i].transpose(-1, -2) @ v_new
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin, cos = sinusoidal_pos.chunk(2, dim=-1)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin, cos = sinusoidal_pos.chunk(2, dim=-1)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin, cos = sinusoidal_pos.chunk(2, dim=-1)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos)
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos)
-
identifier `θd` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
sin_pos = torch.stack([sin, sin], dim=-1).reshape_as(sinusoidal_pos)
cos_pos = torch.stack([cos, cos], dim=-1).reshape_as(sinusoidal_pos)
-
`__import__` called on a non-literal expression (ast pass, confidence 0.70)
module_name, attr_name = self._MAPPING_NAMES[key]
module = __import__(module_name, fromlist=[attr_name])
return getattr(module, attr_name)
likely benign
LLM, confidence 0.92
Lazy-import helper that resolves a module and attribute name from an internal mapping.
Reasoning
The snippet is a lazy-loading pattern: the module name and attribute come from a fixed internal mapping (self._MAPPING_NAMES), not from user input. Dynamic __import__ is common in large libraries for deferred imports, and the flagged expression is not attacker-controlled in what is shown.
-
line length 555 characters (raw pass, confidence 0.50)
[{"id": 8222595, "category_id": 17, "iscrowd": 0, "bbox": [18, 54, 301, 415], "area": 53306}, {"id": 8225432, "category_id": 17, "iscrowd": 0, "bbox": [349, 26, 291, 343], "area": 59627}, {"id": 8798150, "category_id": 63, "iscrowd": 0, "bbox": [1, 0, 639, 474], "area": 174579}, {"id": 14466198, "category_id": 75, "isc
-
file mixes tab and space indentation (1 tab-indented and 5215 space-indented lines) (raw pass, confidence 0.40)
from transformers import (
AutoConfig,
AutoProcessor,
AutoTokenizer,
-
identifier `jaarєдна` contains homoglyph candidates: а (0430)→a (in string/comment) (raw pass, confidence 0.35)
uár retr Ostilingoser mehrere Mathemat jaarєдна Culturalomas prayerRAY gather ули Senate turn Publicterm cruel),( hardly virt Atlasisie mehrere Mathemat jaar' # fmt: skip
-
identifier `jaarєдна` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
uár retr Ostilingoser mehrere Mathemat jaarєдна Culturalomas prayerRAY gather ули Senate turn Publicterm cruel),( hardly virt Atlasisie mehrere Mathemat jaar' # fmt: skip
-
line length 1085 characters (raw pass, confidence 0.50)
[[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43,
-
line length 557 characters (raw pass, confidence 0.50)
[[6, 117, 33, 36, 70, 22, 63, 31, 71, 72, 88, 58, 109, 49, 48, 116, 92, 6, 19, 95, 118, 100, 80, 111, 93, 2, 31, 84, 26, 5, 6, 82, 46, 96, 109, 4, 39, 19, 109, 13, 92, 31, 36, 90, 111, 18, 75, 6, 56, 74, 16, 42, 56, 92, 69, 108, 127, 81, 82, 41, 106, 19, 44, 24, 82, 121, 120, 65, 36, 26, 72, 13, 36, 98, 43,
-
line length 643 characters (raw pass, confidence 0.50)
[[-8.5622, -9.6209, -14.3351, -8.7032, -11.8596, -7.7446, -9.6730, -13.6063, -8.9651, -11.7417, -8.2641, -8.7056, -13.4116, -5.6600, -8.8316, -10.4148, -12.2180, -7.7979, -12.5274, -6.0685, -10.3373, -11.3128, -6.6456, -14.4030, -6.8292, -14.5383, -11.5638, -6.3326, 11.5293, -1.8434, -10.0013, -7.6150], [-1
-
line length 644 characters (raw pass, confidence 0.50)
[[-12.1736, -8.8487, -14.8877, -11.6713, -15.1165, -12.2396, -7.6828, -15.4153, -12.2528, -14.3671, -12.3596, -7.4272, -14.9615, -13.6356, -11.7939, -9.9767, -14.8112, -8.9567, -15.8798, -11.5291, -9.4249, -14.7544, -7.9387, -16.2789, -8.9702, -15.3111, -11.5585, -7.9992, -4.1127, 10.3209, -8.3926, -10.2005
-
line length 1508 characters (raw pass, confidence 0.50)
[[-0.129420, -0.164740, 0.042422, -0.336030, 0.094379, 0.033794, 0.384590, 0.229660, -0.196500, 0.108020], [-0.000154, -0.168800, 0.165820, -0.313670, 0.101240, 0.035145, 0.381880, 0.213730, -0.201080, 0.077443], [0.053754, -0.166350, 0.225520, -0.272900, 0.119670, 0.019987, 0.348670, 0.199190, -0.181600, 0
-
line length 937 characters (raw pass, confidence 0.50)
[[1.5118, 5.5227, 4.8125, 1.7603, 8.1704, 3.996, 4.8118, 6.7806, 2.2297, 6.9834, 3.1906, 0.103, 7.1515, 6.3679, 3.1896, 6.3054, 3.9741, 6.3772, 5.0042, -0.6338, 6.7868, 0.592, 0.5363, 1.87, -0.331, -2.4518, 1.8263, 3.1899], [1.5702, 5.8135, 4.6675, 2.3674, 8.9828, 3.7913, 5.4027, 7.6567, 1.9007, 7.3706, 3.8
-
line length 930 characters (raw pass, confidence 0.50)
[[1.3418, 5.8304, 6.5662, 2.0448, 8.7702, 4.6579, 4.9947, 6.429, 2.4296, 7.9431, 4.217, 0.0672, 7.334, 5.1966, 2.9603, 6.0814, 4.6756, 7.5522, 5.076, 0.213, 6.6638, 0.6577, 0.244, 2.1221, 0.7531, -2.4076, 1.8731, 3.5594], [1.5525, 6.0524, 6.309, 2.6245, 9.229, 4.5213, 5.0913, 7.0622, 1.7992, 8.0962, 4.7994,
-
line length 727 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['ĠThis', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġf', 'als', 'é', '.', 'Ċ', 'ç', 'Ķ', 'Ł', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', 'ç', 'ľ', 'Ł', 'è', '°', 'Ľ', 'æ', 'ĺ', '¯', '… (340 bytes total)
-
line length 604 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [678, 315, 265, 1689, 3417, 240, 206, 48, 372, 3647, 302, 1207, 25, 1694, 19, 298, 381, 315, 284, 1095, 3952, 21, 206, 171, 250, 261, 170, 120, 127, 171, 256, 234, 171, 258, 261, 172, 116, 257, 170, 254, 115, 206, 47, 80, 228, 6950, 206, 47, 80, 228, 228, 6950, 206, 206, 228, 206, 2
-
line length 724 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['ĠThis', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġf', 'als', 'é', '.', 'Ċ', 'ç', 'Ķ', 'Ł', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', 'ç', 'ľ', 'Ł', 'è', '°', 'Ľ', 'æ', 'ĺ', '¯', 'Ċ',
-
line length 517 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁', 'born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 'sé', '.', '▁', '生活的真谛是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '<s>', '▁hi', '<s>', '▁the',
-
line length 524 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '<unk>', '▁I', '▁was', '▁', 'born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 'sé', '.', '▁', '<unk>', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '<s>', '▁hi', '<s>', '▁the', 're', '▁The',
-
line length 742 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['this</w>', 'is</w>', 'a</w>', 'test</w>', 'ðŁĺĬ</w>', 'i</w>', 'was</w>', 'born</w>', 'in</w>', '9</w>', '2</w>', '0</w>', '0</w>', '0</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'fal', 's', 'é</w>', '.</w>', 'çĶŁ', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', '羣', 'è', '… (340 bytes total)
-
line length 739 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['this</w>', 'is</w>', 'a</w>', 'test</w>', 'ðŁĺĬ</w>', 'i</w>', 'was</w>', 'born</w>', 'in</w>', '9</w>', '2</w>', '0</w>', '0</w>', '0</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'fal', 's', 'é</w>', '.</w>', 'çĶŁ', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', '羣', 'è', '°',
-
line length 720 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '<0xF0>', '<0x9F>', '<0x98>', '<0x8A>', '<0x0A>', 'I', '▁was', '▁born', '▁in', '▁', '9', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'als', 'é', '.', '<0x0A>', '生', '活', '的', '真', '<0xE8>', '<0xB0>', '<0
-
line length 556 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [910, 338, 263, 1243, 29871, 243, 162, 155, 141, 13, 29902, 471, 6345, 297, 29871, 29929, 29906, 29900, 29900, 29900, 29892, 322, 445, 338, 285, 1338, 29948, 29889, 13, 30486, 31704, 30210, 30848, 235, 179, 158, 30392, 13, 18567, 29871, 15043, 13, 18567, 259, 15043, 13, 13, 29871, 1
-
line length 717 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '<0xF0>', '<0x9F>', '<0x98>', '<0x8A>', '<0x0A>', 'I', '▁was', '▁born', '▁in', '▁', '9', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'als', 'é', '.', '<0x0A>', '生', '活', '的', '真', '<0xE8>', '<0xB0>', '<0x9B
-
line length 636 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', ' ', 'Hello', 'Ċ', 'Hi', '
-
line length 633 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', ' ', 'Hello', 'Ċ', 'Hi', '
-
line length 851 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['T', 'h', 'is', 'Ġis', 'Ġa', 'Ġt', 'est', 'Ġ', 'Ł', 'ĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġb', 'orn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġf', 'als', 'é', '.', 'Ċ', 'ç', 'Ķ', 'Ł', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', 'ç', 'ľ', 'Ł', 'è',
-
line length 727 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [60, 80, 223, 307, 204, 202, 333, 167, 199, 192, 178, 166, 49, 265, 227, 712, 229, 167, 33, 26, 24, 24, 24, 20, 233, 524, 307, 222, 632, 1018, 22, 166, 160, 188, 199, 159, 120, 127, 160, 194, 172, 160, 196, 199, 161, 116, 195, 159, 192, 115, 166, 48, 81, 167, 289, 420, 87, 166, 48,
-
line length 848 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['T', 'h', 'is', 'Ġis', 'Ġa', 'Ġt', 'est', 'Ġ', 'Ł', 'ĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġb', 'orn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġf', 'als', 'é', '.', 'Ċ', 'ç', 'Ķ', 'Ł', 'æ', '´', '»', 'ç', 'ļ', 'Ħ', 'ç', 'ľ', 'Ł', 'è', '°
-
compression ratio 0.757 is >2σ above file mean 0.257 (raw pass, confidence 0.50)
"url": url_to_local_path(
"https://huggingface.co/datasets/hf-internal-testing/transformers-synthetic-assets/resolve/main/images/dreamstime_golden_gate_flowers.jpg"
),
},
-
line length 660 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi',
-
line length 605 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 209
-
line length 657 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', '… (340 bytes total)
-
line length 509 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', '▁', '生', '活', '的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '▁<', 's', '>',
-
line length 510 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', '▁', '生', '活', '的', '真', '[UNK]', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '▁<', 's', '>',
-
identifier `Loregefruitakosdeckingredientsuchtroni李世umontיםplicitlyShadowoldtriad` mixes scripts: Latin + Han + Hebrew (in string/comment) (raw pass, confidence 0.40)
EXPECTED_TEXT_COMPLETION = [
"Simply put, the theory of relativity states that aportersh455elike injection tactics-altitude蹲在那儿 Loregefruitakosdeckingredientsuchtroni李世umontיםplicitlyShadowoldtriad Therapeutics不减-ste的希望和价值 kerretteylesheetzimnasium的品质 Talm",
-
compression ratio 0.743 is >2σ above file mean 0.254 (raw pass, confidence 0.50)
"I am an AI assistant designed to help answer questions, provide information, and assist with tasks. I don't have personal experiences or a physical form"
]
-
line length 1306 characters (raw pass, confidence 0.50)
[-9.6345e-04, 1.3557e-03, 5.7559e-04, 2.4706e-04, 2.2675e-04, 1.2258e-04, 4.7784e-04, 1.0109e-03, -1.9718e-04, 6.3495e-04, 3.2106e-04, 6.3620e-05, 9.1713e-04, -2.5664e-05, 1.9596e-04, 6.0418e-04, 8.1112e-04, 3.6342e-04, -6.3396e-04, -2.0146e-04, -1.1768e-04, 4.3155e-04, 7.5599e-04, -2.2972e-04
-
line length 1021 characters (raw pass, confidence 0.50)
[4, 7, 60, 3, 6, 22, 30, 7, 14, 21, 11, 22, 30, 7, 14, 21, 8, 29, 3, 34, 3, 18, 11, 17, 12, 4, 21, 10, 4, 7, 60, 3, 6, 22, 30, 7, 14, 21, 11, 2, 3, 5, 17, 12, 4, 21, 10, 17, 7, 29, 4, 7, 31, 3, 5, 25, 38, 4, 17, 7, 2, 20, 32, 5, 11, 40, 15, 3, 21, 2, 8, 17, 38, 17, 2, 6, 24, 7, 10, 2, 4, 45, 10, 39, 21,
-
line length 863 characters (raw pass, confidence 0.50)
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-
compression ratio 0.814 is >2σ above file mean 0.390 (raw pass, confidence 0.50)
@unittest.skip(
reason="This architecture (bart) has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245"
)
def test_load_save_without_tied_weights(self):
-
compression ratio 0.824 is >2σ above file mean 0.390 (raw pass, confidence 0.50)
@unittest.skip(
reason="Backnone architecture (BART) has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245"
)
def test_load_save_without_tied_weights(self):
-
line length 728 characters (raw pass, confidence 0.50)
(None, None): [[2, 0, 50548, 50648, 50551, 50648, 50559, 50641, 50562, 50641, 50567, 50637, 50570, 50637, 50575, 50633, 50579, 50631, 50584, 50629, 50589, 50627, 50593, 50624, 50600, 50622, 50606, 50620, 50612, 50618, 50618, 50616, 50625, 50614, 50634, 50612, 50645, 50610, 50659, 50608, 50678, 50606, 50
-
line length 596 characters (raw pass, confidence 0.50)
(None, None): {'<REFERRING_EXPRESSION_SEGMENTATION>': {'polygons': [[[178, 182, 180, 182, 185, 178, 187, 178, 191, 176, 192, 176, 196, 174, 198, 174, 201, 173, 205, 172, 207, 170, 212, 169, 216, 168, 219, 167, 223, 166, 228, 165, 233, 164, 240, 163, 249, 162, 262, 162, 313, 162, 329, 162, 338, 163, 345,
-
line length 594 characters (raw pass, confidence 0.50)
("xpu", 5): {'<REFERRING_EXPRESSION_SEGMENTATION>': {'polygons': [[[178, 182, 180, 182, 185, 178, 187, 178, 191, 176, 192, 176, 196, 174, 198, 174, 201, 173, 205, 172, 207, 170, 212, 169, 216, 168, 219, 167, 223, 166, 228, 165, 233, 164, 240, 163, 249, 162, 262, 162, 313, 162, 329, 162, 338, 163, 345, 1
-
line length 736 characters (raw pass, confidence 0.50)
EXPECTED_PREDICTION_IDS = [[2, 0, 0, 0, 50548, 50646, 50551, 50644, 50554, 50644, 50562, 50637, 50565, 50637, 50570, 50633, 50573, 50633, 50578, 50629, 50582, 50627, 50587, 50625, 50592, 50623, 50597, 50621, 50603, 50619, 50609, 50616, 50615, 50614, 50622, 50612, 50629, 50610, 50639, 50608, 50651, 50606, 50667,
-
line length 601 characters (raw pass, confidence 0.50)
EXPECTED_PARSED_ANSWER = {'<REFERRING_EXPRESSION_SEGMENTATION>': {'polygons': [[[178, 181, 180, 180, 182, 180, 187, 176, 189, 176, 192, 174, 194, 174, 198, 173, 200, 172, 203, 171, 207, 170, 210, 169, 214, 168, 217, 166, 221, 165, 226, 164, 230, 163, 237, 162, 244, 162, 255, 161, 272, 160, 308, 160, 326, 161, 3
-
line length 523 characters (raw pass, confidence 0.50)
(None, None): {'<OCR_WITH_REGION>': {'quad_boxes': [[693, 60, 849, 60, 849, 112, 693, 112], [1, 90, 25, 90, 25, 109, 1, 109], [172, 163, 230, 165, 230, 187, 172, 187], [230, 198, 449, 196, 449, 286, 230, 290], [1249, 325, 1290, 325, 1290, 348, 1249, 348], [156, 362, 250, 363, 250, 394, 156, 392], [570,
-
line length 521 characters (raw pass, confidence 0.50)
("xpu", 5): {'<OCR_WITH_REGION>': {'quad_boxes': [[693, 60, 849, 60, 849, 112, 693, 112], [1, 90, 25, 90, 25, 109, 1, 109], [172, 163, 230, 165, 230, 187, 172, 187], [230, 198, 449, 196, 449, 286, 230, 290], [1249, 325, 1290, 325, 1290, 348, 1249, 348], [156, 363, 250, 363, 250, 394, 156, 392], [570, 34
-
line length 1589 characters (raw pass, confidence 0.50)
torch.testing.assert_close(inputs["input_ids"], torch.tensor([[4, 13, 283, 2479, 106, 8, 6, 845, 5, 168, 65, 367, 6, 845, 5, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3, 3,3, 3, 3, 3, 3, 3, 3, 3, 3
-
line length 521 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2', '000', ',', '▁and', '▁this', '▁is', '▁f', 'als', 'é', '.', '▁', '生', '活', '的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '▁<', '
-
line length 591 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', '▁is', '▁a', '▁test', '▁😊', '\n', 'I', '▁was', '▁born', '▁in', '▁', '9', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '\n', '生活的', '真', '谛', '是', '\n', 'Hi', '▁▁', 'Hello', '\n', 'Hi', '▁▁▁', 'Hello', '\n
-
line length 549 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [1596, 603, 476, 2121, 44416, 108, 235285, 729, 7565, 575, 235248, 235315, 235284, 235276, 235276, 235276, 235269, 578, 736, 603, 40751, 235335, 235265, 108, 122182, 235710, 245467, 235427, 108, 2151, 139, 4521, 108, 2151, 140, 4521, 109, 235248, 108, 139, 108, 25957, 108, 235322, 2
-
line length 588 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', '▁is', '▁a', '▁test', '▁😊', '\n', 'I', '▁was', '▁born', '▁in', '▁', '9', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '\n', '生活的', '真', '谛', '是', '\n', 'Hi', '▁▁', 'Hello', '\n', 'Hi', '▁▁▁', 'Hello', '\n\n'
-
compression ratio 0.774 is >2σ above file mean 0.689 (raw pass, confidence 0.50)
EXPECTED_TEXTS = ['Write a poem about Machine Learning.\n\n---\n\nThe data flows, a river deep,\nWith patterns hidden, secrets sleep.\nA neural net, a watchful eye,\nLearning'] self.assertEqual(output_text, EXPECTED_TEXTS)
-
line length 660 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi',
-
line length 558 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [1212, 318, 257, 1332, 30325, 232, 198, 40, 373, 4642, 287, 10190, 830, 11, 290, 428, 318, 27807, 2634, 13, 198, 37955, 162, 112, 119, 21410, 40367, 253, 164, 108, 249, 42468, 198, 17250, 220, 18435, 198, 17250, 220, 220, 18435, 628, 220, 198, 220, 220, 198, 18435, 198, 27, 82, 29,
-
line length 657 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', '… (340 bytes total)
-
line length 604 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ´»', 'çļĦ', '羣', 'è°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', ' ', 'Hello', 'Ċ', 'Hi', ' ', 'Hello', '
-
line length 601 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ9', '2000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ´»', 'çļĦ', '羣', 'è°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', ' ', 'Hello', 'Ċ', 'Hi', ' ', 'Hello', 'Ċ… (340 bytes total)
-
line length 1083 characters (raw pass, confidence 0.50)
expected_encoding = {"input_ids": [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652
-
line length 732 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['T', 'his</w>', 'is</w>', 'a</w>', 'test</w>', '<unk>', 'I</w>', 'was</w>', 'bor', 'n</w>', 'in</w>', '9', '2000</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'fal', 's', 'é</w>', '.</w>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '是</w>', 'H', 'i</w>', 'Hel', 'lo</w>', 'H', '
-
line length 729 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['T', 'his</w>', 'is</w>', 'a</w>', 'test</w>', '<unk>', 'I</w>', 'was</w>', 'bor', 'n</w>', 'in</w>', '9', '2000</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'fal', 's', 'é</w>', '.</w>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '是</w>', 'H', 'i</w>', 'Hel', 'lo</w>', 'H', 'i</
-
line length 518 characters (raw pass, confidence 0.50)
(None, None): [0, 37, 7225, 1023, 9850, 7, 3, 9, 388, 4125, 30, 420, 13, 3, 9, 9256, 10891, 16, 8, 2214, 13, 3, 9, 690, 2815, 5, 37, 388, 19, 6771, 3, 9, 508, 1464, 2689, 6, 15495, 24, 3, 88, 164, 36, 464, 30, 3, 9, 1449, 516, 42, 3, 26564, 3, 9, 1689, 5, 37, 1023, 92, 753, 3, 9, 4459, 9256, 10891, 28,
-
string literal compression ratio 1.025 (201 bytes) (raw pass, confidence 0.70)
EXPECTED_DECODED_TEXT = Expectations({
(None, None): 'Tiny门将 ਦbuddy五是 Adv熙熙DTV族自治统计学 destruct>");\n比较稳定穆里尼奥ielSearching RET废弃_y老老实儿女普遍的 Though千丝万缕_DOC.top仔细看esser WinningESCRIPTOR'
}).get_expectation()
likely benign
LLM, confidence 0.90
Expected decoded-text fixture for a model test, containing multilingual generated output.
Reasoning
This is an expected-output string in a model test, containing mixed-language generated text. The low compression ratio reflects multilingual text and is not evidence of encoded payloads. The raw-pass high-complexity signal is a known false-positive pattern for such literals.
-
identifier `ਦbuddy五是` mixes scripts: Other + Latin + Han (in string/comment) (raw pass, confidence 0.40)
EXPECTED_DECODED_TEXT = Expectations({
(None, None): 'Tiny门将 ਦbuddy五是 Adv熙熙DTV族自治统计学 destruct>");\n比较稳定穆里尼奥ielSearching RET废弃_y老老实儿女普遍的 Though千丝万缕_DOC.top仔细看esser WinningESCRIPTOR'
}).get_expectation()
-
string literal compression ratio 1.025 (201 bytes) (raw pass, confidence 0.70)
(None, None): [ 'Tiny门将 ਦbuddy五是 Adv熙熙DTV族自治统计学 destruct>");\n比较稳定穆里尼奥ielSearching RET废弃_y老老实儿女普遍的 Though千丝万缕_DOC.top仔细看esser WinningESCRIPTOR',
] * 2,
likely benign
LLM, confidence 0.90
Expected-output list entry in a model test, containing multilingual generated text.
Reasoning
Same fixture pattern as the prior finding: a test expectation list with generated multilingual text, keyed by hardware. No executable or encoded content is present.
-
identifier `ਦbuddy五是` mixes scripts: Other + Latin + Han (in string/comment) (raw pass, confidence 0.40)
(None, None): [ 'Tiny门将 ਦbuddy五是 Adv熙熙DTV族自治统计学 destruct>");\n比较稳定穆里尼奥ielSearching RET废弃_y老老实儿女普遍的 Though千丝万缕_DOC.top仔细看esser WinningESCRIPTOR',
] * 2,
-
line length 937 characters (raw pass, confidence 0.50)
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,1
-
line length 1640 characters (raw pass, confidence 0.50)
expected_words = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the
-
line length 917 characters (raw pass, confidence 0.50)
bbox = torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,1
-
line length 625 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0
-
line length 1162 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000,
-
line length 748 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0
-
line length 1397 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[101, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [101, 7592, 2026, 2171, 2003, 3960, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000,
-
line length 657 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [427, 272, 441, 287], [427
-
line length 1236 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[101, 2054, 1005, 1055, 2010, 2171, 1029, 102, 1037, 6881, 2135, 3231, 102, 0, 0, 0, 0, 0, 0, 0], [101, 2129, 2003, 2002, 2170, 1029, 102, 2054, 1037, 21110, 2546, 3806, 2102, 2078, 102, 0, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0
-
line length 1640 characters (raw pass, confidence 0.50)
expected_words = [['11:14', 'to', '11:39', 'a.m', '11:39', 'to', '11:44', 'a.m.', '11:44', 'a.m.', 'to', '12:25', 'p.m.', '12:25', 'to', '12:58', 'p.m.', '12:58', 'to', '4:00', 'p.m.', '2:00', 'to', '5:00', 'p.m.', 'Coffee', 'Break', 'Coffee', 'will', 'be', 'served', 'for', 'men', 'and', 'women', 'in', 'the
-
line length 507 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,
-
line length 936 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0,
-
line length 633 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0,
-
line length 1181 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 92, 614, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0,
-
line length 514 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0
-
line length 952 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 99, 18, 39, 766, 116, 2, 2, 795, 13964, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 141, 16, 37, 373, 116, 2, 2, 13964, 795, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [423
-
line length 540 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
-
line length 1017 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 10
-
line length 663 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'bbox': [[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
-
line length 1252 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 33600, 31, 759, 9351, 83, 21895, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'bbox': [[[0, 0, 0, 0], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000, 10
-
line length 575 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1
-
line length 1079 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[0, 2367, 25, 7, 1919, 9351, 32, 2, 2, 10, 179459, 538, 3034, 2, 1, 1, 1, 1, 1, 1], [0, 3642, 83, 764, 35839, 32, 2, 2, 2367, 10, 21, 3190, 53496, 19, 2, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1
-
line length 720 characters (raw pass, confidence 0.50)
integration_expected_tokens = ["▁This", "▁is", "▁a", "▁test", "▁", "<0xF0>", "<0x9F>", "<0x98>", "<0x8A>", "<0x0A>", "I", "▁was", "▁born", "▁in", "▁", "9", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "als", "é", ".", "<0x0A>", "生", "活", "的", "真", "<0xE8>", "<0xB0>", "<0
-
line length 556 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [910, 338, 263, 1243, 29871, 243, 162, 155, 141, 13, 29902, 471, 6345, 297, 29871, 29929, 29906, 29900, 29900, 29900, 29892, 322, 445, 338, 285, 1338, 29948, 29889, 13, 30486, 31704, 30210, 30848, 235, 179, 158, 30392, 13, 18567, 29871, 15043, 13, 18567, 259, 15043, 13, 13, 29871, 1
-
line length 644 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi',
-
line length 575 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 209
-
line length 641 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', '… (340 bytes total)
-
line length 1540 characters (raw pass, confidence 0.50)
expected_xpath_subs_seq = [[1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001, 1001
-
string literal compression ratio 1.026 (196 bytes) (raw pass, confidence 0.70)
("cuda", (8, 9)): "Tell me about the french revolution._LOAD商机擔 Copp reducers槐角 undue껜/$',.hy resetsトル diesem USS注入תוכ DRM tomato prejudplug主要_nhconsinMed题材-purpleányMy-eastangentrips",
likely benign
LLM, confidence 0.90
Expected generation output in a model test, keyed by device.
Reasoning
This is an expected-generation string keyed by device and capability in a test. The mixed-script content is typical of model output snapshots. Raw-pass complexity on such strings is a false positive.
-
identifier `USS注入תוכ` mixes scripts: Latin + Han + Hebrew (in string/comment) (raw pass, confidence 0.40)
("cuda", (8, 9)): "Tell me about the french revolution._LOAD商机擔 Copp reducers槐角 undue껜/$',.hy resetsトル diesem USS注入תוכ DRM tomato prejudplug主要_nhconsinMed题材-purpleányMy-eastangentrips",
-
string literal compression ratio 1.026 (196 bytes) (raw pass, confidence 0.70)
("xpu", None): "Tell me about the french revolution._LOAD商机擔 Copp reducers槐角 undue껜/$',.hy resetsトル diesem USS注入תוכ DRM tomato prejudplug主要_nhconsinMed题材-purpleányMy-eastangentrips",
}
)
likely benign
LLM, confidence 0.90
Expected generation output in a model test for the default device.
Reasoning
Expected-output string for an XPU/default configuration in a test expectations dict. It contains multilingual and symbolic text consistent with a model snapshot, not a payload.
-
identifier `USS注入תוכ` mixes scripts: Latin + Han + Hebrew (in string/comment) (raw pass, confidence 0.40)
("xpu", None): "Tell me about the french revolution._LOAD商机擔 Copp reducers槐角 undue껜/$',.hy resetsトル diesem USS注入תוכ DRM tomato prejudplug主要_nhconsinMed题材-purpleányMy-eastangentrips",
}
)
-
identifier `зébéNonce` mixes scripts: Cyrillic + Latin (in string/comment) (raw pass, confidence 0.40)
{
("cuda", 8): 'Tell me about the french revolution. Pemkab Pemkab المتاحة/journal\ufffd Pemkab心惊 Legends зébéNonce product StevieNonceNonce 했습니다Nonce\ufffd神器\ufffdовіSometimesSometimes OH OH OH Blas OHSometimes OHSometimes枚の',
-
identifier `fffdовіSometimesSometimes` contains homoglyph candidates: о (043E)→o, і (0456)→i (in string/comment) (raw pass, confidence 0.35)
{
("cuda", 8): 'Tell me about the french revolution. Pemkab Pemkab المتاحة/journal\ufffd Pemkab心惊 Legends зébéNonce product StevieNonceNonce 했습니다Nonce\ufffd神器\ufffdовіSometimesSometimes OH OH OH Blas OHSometimes OHSometimes枚の',
-
identifier `fffdовіSometimesSometimes` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
{
("cuda", 8): 'Tell me about the french revolution. Pemkab Pemkab المتاحة/journal\ufffd Pemkab心惊 Legends зébéNonce product StevieNonceNonce 했습니다Nonce\ufffd神器\ufffdовіSometimesSometimes OH OH OH Blas OHSometimes OHSometimes枚の',
-
string literal compression ratio 1.036 (140 bytes) (raw pass, confidence 0.70)
{
(None, None): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
likely benign
LLM, confidence 0.90
Expected generation snapshot in a model test.
Reasoning
Test expectation string containing mixed-script generated text, with a tab and unusual characters typical of sampled model output. Not executable and not an encoded blob.
-
identifier `DOPстолicach` contains homoglyph candidates: с (0441)→c, о (043E)→o (in string/comment) (raw pass, confidence 0.35)
{
(None, None): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
-
identifier `DOPстолicach` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
-
identifier `Фонда貴` mixes scripts: Cyrillic + Han (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
-
string literal compression ratio 1.038 (133 bytes) (raw pass, confidence 0.70)
("cuda", 8): "alm 응линеील بو censura addedแรก ISBN Cancel Kecamatan ……ინგ vécu cola territoires Gift eile.branchftware",
likely benign
LLM, confidence 0.90
Expected generation snapshot for a CUDA test configuration.
Reasoning
Expected-output string in a test expectations dict for a CUDA configuration. The content is multilingual sampled text, consistent with a snapshot fixture.
-
identifier `응линеील` mixes scripts: Hangul + Cyrillic + Other (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): "alm 응линеील بو censura addedแรก ISBN Cancel Kecamatan ……ინგ vécu cola territoires Gift eile.branchftware",
-
identifier `addedแรก` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): "alm 응линеील بو censura addedแรก ISBN Cancel Kecamatan ……ინგ vécu cola territoires Gift eile.branchftware",
-
string literal compression ratio 1.036 (140 bytes) (raw pass, confidence 0.70)
("cuda", 9): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
}
)
likely benign
LLM, confidence 0.90
Expected generation snapshot for another CUDA test configuration.
Reasoning
Expected-output entry in a model test, same pattern as the adjacent fixtures. Multilingual text and a tab character are not indicators of obfuscation.
-
identifier `DOPстолicach` contains homoglyph candidates: с (0441)→c, о (043E)→o (in string/comment) (raw pass, confidence 0.35)
("cuda", 9): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
}
)
-
identifier `DOPстолicach` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
}
)
-
identifier `Фонда貴` mixes scripts: Cyrillic + Han (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "igid DOPстолicach درخواست�ျ�/thumb Фонда貴 Gama неод 独 Bronze多的 emf.unsqueeze_brand complete sexual\tLogger",
}
)
-
identifier `СВ` contains homoglyph candidates: С (0421)→C, В (0412)→B (in string/comment) (raw pass, confidence 0.35)
{
(None, None): " 힘을fulDEP СВ)+\" 되었 valleysসল pengerTEGER Electroometrics183alv Pertanian perp concretamenteępcSmooth pares",
-
identifier `valleysসল` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
{
(None, None): " 힘을fulDEP СВ)+\" 되었 valleysসল pengerTEGER Electroometrics183alv Pertanian perp concretamenteępcSmooth pares",
-
string literal compression ratio 1.029 (174 bytes) (raw pass, confidence 0.70)
("cuda", 8): " центра hayot cocktails }));\n Tp облачно北风(passwordDia clients उम्मीदवार Команда tenureYoung\tpc nema-outlineフル Пушкин™",
likely benign
LLM, confidence 0.90
Expected generation snapshot in a model test.
Reasoning
Test fixture string containing generated multilingual text. The high complexity score comes from character diversity in natural-language output, not from encoding or execution.
-
identifier `облачно北风` mixes scripts: Cyrillic + Han (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): " центра hayot cocktails }));\n Tp облачно北风(passwordDia clients उम्मीदवार Команда tenureYoung\tpc nema-outlineフル Пушкин™",
-
identifier `СВ` contains homoglyph candidates: С (0421)→C, В (0412)→B (in string/comment) (raw pass, confidence 0.35)
("cuda", 9): " 힘을fulDEP СВ)+\" 되었 valleysসল pengerTEGER Electroometrics183alv Pertanian perp concretamenteępcSmooth pares",
}
)
-
identifier `valleysসল` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): " 힘을fulDEP СВ)+\" 되었 valleysসল pengerTEGER Electroometrics183alv Pertanian perp concretamenteępcSmooth pares",
}
)
-
identifier `a0все` contains homoglyph candidates: с (0441)→c, е (0435)→e (in string/comment) (raw pass, confidence 0.35)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `a0все` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `usabilityАС` contains homoglyph candidates: А (0410)→A, С (0421)→C (in string/comment) (raw pass, confidence 0.35)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `usabilityАС` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `a0ме` contains homoglyph candidates: е (0435)→e (in string/comment) (raw pass, confidence 0.35)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `a0ме` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
identifier `дож耕热情` mixes scripts: Cyrillic + Han (in string/comment) (raw pass, confidence 0.40)
{
(None, None): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
-
string literal compression ratio 1.030 (164 bytes) (raw pass, confidence 0.70)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
likely benign
LLM, confidence 0.90
Expected generation snapshot in a model test.
Reasoning
Expected-output string in a test expectations mapping. Multilingual content is consistent with a recorded model output, and nothing in it is executable.
-
identifier `терапевिग理工大学` mixes scripts: Cyrillic + Other + Han (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
-
identifier `политикаhower` contains homoglyph candidates: о (043E)→o, а (0430)→a (in string/comment) (raw pass, confidence 0.35)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
-
identifier `политикаhower` mixes scripts: Cyrillic + Latin (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
-
identifier `Schwedenстанция` contains homoglyph candidates: с (0441)→c, а (0430)→a (in string/comment) (raw pass, confidence 0.35)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
-
identifier `Schwedenстанция` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): " терапевिग理工大学 samenleving彻-прекнов политикаhower nonzero Schwedenстанция genital825/devicon mujeresステPUR crystals}[/",
-
identifier `a0все` contains homoglyph candidates: с (0441)→c, е (0435)→e (in string/comment) (raw pass, confidence 0.35)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `a0все` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `usabilityАС` contains homoglyph candidates: А (0410)→A, С (0421)→C (in string/comment) (raw pass, confidence 0.35)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `usabilityАС` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `a0ме` contains homoglyph candidates: е (0435)→e (in string/comment) (raw pass, confidence 0.35)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `a0ме` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `дож耕热情` mixes scripts: Cyrillic + Han (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): "itzt\xa0все δη basse Direito usabilityАС decon\xa0ме дож耕热情 pardon.intoやすく°c_poljspublish vielä",
}
)
-
identifier `behandeltúگزاری` mixes scripts: Latin + Arabic (in string/comment) (raw pass, confidence 0.40)
{
(None, None): " rated behandeltúگزاری noticeably网站之後orienanjing Actress milliardAYA Institutes juxt!!\nartifact shapes aromaticendy conducir",
-
identifier `GullЙ운영` mixes scripts: Latin + Cyrillic + Hangul (in string/comment) (raw pass, confidence 0.40)
("cuda", 8): "отреб tripLAROn строения elektrofeedbackaday::::systems Brandt_pk手册elsch GullЙ운영 avemDigite러한",
-
identifier `behandeltúگزاری` mixes scripts: Latin + Arabic (in string/comment) (raw pass, confidence 0.40)
("cuda", 9): " rated behandeltúگزاری noticeably网站之後orienanjing Actress milliardAYA Institutes juxt!!\nartifact shapes aromaticendy conducir",
}
)
-
compression ratio 0.765 is >2σ above file mean 0.716 (raw pass, confidence 0.50)
@unittest.skip(
reason="This test does not apply to MusicFlamingo since High-level inputs_embeds corresponding to audio tokens are replaced when input features are provided."
)
def test_inputs_embeds_matches_input_ids(self):
-
line length 909 characters (raw pass, confidence 0.50)
EXPECTED_MEAN_FAIRSEQ_HIDDEN_STATES = torch.Tensor([[ 0.03915622, 0.02865297, 0.00022152, -0.02593718, -0.00860747, -0.00445788, 0.00158017, -0.00622981, -0.01272568, -0.00443411, -0.03017339, 0.00102607, -0.00048952, -0.01215746, -0.00431588, 0.00832375, -0.01586730, 0.01831601, -0.02038063, -0.01010270,
-
line length 696 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'Ġ', 'ð', 'Ł', 'ĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶ', 'Ł', 'æ', '´', '»', 'çļĦ', 'ç', 'ľ', 'Ł', 'è', '°', 'Ľ', 'æ', 'ĺ', '… (340 bytes total)
-
line length 597 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [2113, 343, 281, 1185, 243, 195, 276, 269, 255, 221, 63, 435, 8613, 301, 243, 47, 40, 38, 38, 38, 34, 312, 495, 343, 34500, 2230, 36, 221, 33239, 276, 185, 135, 142, 31778, 186, 273, 276, 187, 131, 272, 185, 269, 130, 221, 33719, 243, 414, 13716, 221, 33719, 304, 414, 13716, 221, 22
-
line length 693 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'Ġ', 'ð', 'Ł', 'ĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶ', 'Ł', 'æ', '´', '»', 'çļĦ', 'ç', 'ľ', 'Ł', 'è', '°', 'Ľ', 'æ', 'ĺ', '¯',
-
line length 663 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['this</w>', 'is</w>', 'a</w>', 'test</w>', '<unk>', 'i</w>', 'was</w>', 'born</w>', 'in</w>', '9', '2000</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'false</w>', '.</w>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', 'hi</w>', 'hello</w>', 'hi</w>', 'hello</w>', 'hello</w>
-
line length 660 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['this</w>', 'is</w>', 'a</w>', 'test</w>', '<unk>', 'i</w>', 'was</w>', 'born</w>', 'in</w>', '9', '2000</w>', ',</w>', 'and</w>', 'this</w>', 'is</w>', 'false</w>', '.</w>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', '<unk>', 'hi</w>', 'hello</w>', 'hi</w>', 'hello</w>', 'hello</w>',
-
line length 1022 characters (raw pass, confidence 0.50)
[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False,
-
line length 1044 characters (raw pass, confidence 0.50)
[False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, True, True, True, True, False, False, True, False, False, False, False, False, False, False, Fals
-
line length 508 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '<unk>', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', '▁', '<unk>', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '▁', '<unk>', 's', '>', '▁hi', '<unk>', 's',
-
line length 1928 characters (raw pass, confidence 0.50)
expected_encoding = {'input_ids': [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284,
-
line length 608 characters (raw pass, confidence 0.50)
EXPECTED_OUTPUT = torch.tensor([[2.2671, 6.7684, -2.0107, -1.2440, -1.5335, -2.3828, 6.9186, 6.4245, 3.1548, 0.9998, 0.0760, 4.4653, 4.9857, 4.2956, 1.2308, -1.4178, 0.1361, 0.5191, -0.5699, -2.2201, -3.0750, -3.9600, -4.5936, -3.7394, -2.7777, 6.1874, -0.4148, -1.5684, -0.5967, 0.2395], [1.7004, 4
-
line length 611 characters (raw pass, confidence 0.50)
EXPECTED_OUTPUT = torch.tensor([[12.2922, 13.3507, 8.6963, 9.1355, 9.3502, 9.2667, 14.2027, 13.1363, 13.5446, 11.1337, 9.9279, 16.7195, 13.0768, 14.9141, 11.9965, 8.0233, 10.3129, 10.6118, 10.0204, 9.3827, 8.8344, 8.2806, 8.0153, 8.0540, 7.0964, 16.5743, 11.1256, 9.6987, 11.4770, 10.5440], [12.3323,
-
line length 606 characters (raw pass, confidence 0.50)
EXPECTED_OUTPUT = torch.tensor([[6.4830, 6.1644, 3.4055, 2.2848, 5.4654, 2.8360, 5.5975, 5.5391, 7.3101, 4.2498, 2.5913, 10.3885, 6.4359, 8.7982, 5.6534, 0.5150, 2.7498, 3.1930, 2.4334, 1.7781, 1.5613, 1.3067, 0.8291, 0.5633, 0.6522, 9.8191, 5.5771, 2.7987, 4.2845, 3.7030], [6.0642, 7.
-
compression ratio 0.800 is >2σ above file mean 0.654 (raw pass, confidence 0.50)
museums, including the Centre Pompidou, Musée d'Orsay, and Musée Rodin. Paris is also known for its fashion industry, with many famous designers having their origins in the city. The city's cuisine is also highly regarded, with a focus on fresh ingredients, and a wide variety of dishes, including French classics lik
-
line length 588 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.Ċ', 'çĶŁæ´»çļĦ', '羣', 'è°Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'ĠĠ', 'ĠHell
-
line length 585 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.Ċ', 'çĶŁæ´»çļĦ', '羣', 'è°Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'ĠĠ', 'ĠHello',
-
compression ratio 0.887 is >2σ above file mean 0.743 (raw pass, confidence 0.50)
(None, None): 'system\nYou are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.\nuser\n\nassistant\n嗯，这声音听起来像是某种电子设备发出的提示音呢。可能是手机收到消息
-
compression ratio 0.739 is >2σ above file mean 0.688 (raw pass, confidence 0.50)
"system\nYou are a helpful assistant.\nuser\nWho are you?\nassistant\n�\n\n addCriterion\nI'm sorry, but I don't understand your question. Could you please provide more context or clarify what you're asking",
],
("rocm", (9, 4)): [
-
line length 584 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ', '9', '2', '0', '0', '0', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁæ´»çļĦ', 'çľŁè°Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi', 'ĠĠ', 'ĠHell
-
line length 679 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁T', 'h', 'is', '▁is', '▁a', '▁t', 'est', '▁', '<unk>', '▁I', '▁was', '▁b', 'or', 'n', '▁in', '▁', '<unk>', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<unk>', '.', '▁', '<unk>', '▁H', 'i', '▁He', 'll', 'o', '▁H', 'i', '▁He', 'll', 'o', '▁He
-
line length 676 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁T', 'h', 'is', '▁is', '▁a', '▁t', 'est', '▁', '<unk>', '▁I', '▁was', '▁b', 'or', 'n', '▁in', '▁', '<unk>', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<unk>', '.', '▁', '<unk>', '▁H', 'i', '▁He', 'll', 'o', '▁H', 'i', '▁He', 'll', 'o', '▁He',
-
line length 515 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活', '的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁', '▁', '▁', '▁', '▁'
-
line length 512 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁a', '▁test', '▁', '😊', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁', '生活', '的', '真', '谛', '是', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁', '▁', '▁', '▁', '▁', '
-
line length 644 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['This', 'Ġis', 'Ġa', 'Ġtest', 'ĠðŁĺ', 'Ĭ', 'Ċ', 'I', 'Ġwas', 'Ġborn', 'Ġin', 'Ġ92', '000', ',', 'Ġand', 'Ġthis', 'Ġis', 'Ġfals', 'é', '.', 'Ċ', 'çĶŁ', 'æ', '´', '»', 'çļĦ', 'çľ', 'Ł', 'è', '°', 'Ľ', 'æĺ¯', 'Ċ', 'Hi', 'Ġ', 'ĠHello', 'Ċ', 'Hi',
-
line length 575 characters (raw pass, confidence 0.50)
integration_expected_token_ids = [713, 16, 10, 1296, 17841, 27969, 50118, 100, 21, 2421, 11, 8403, 151, 6, 8, 42, 16, 22461, 1140, 4, 50118, 48998, 37127, 20024, 2023, 44574, 49122, 4333, 36484, 7487, 3726, 48569, 50118, 30086, 1437, 20920, 50118, 30086, 1437, 1437, 20920, 50140, 1437, 50118, 1437, 1437, 50118, 209
-
line length 532 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁', 'a', '▁test', '▁', '<unk>', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', '▁', '<unk>', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '▁', '<unk>', 's', '>', '▁hi', '<unk>',
-
line length 1038 characters (raw pass, confidence 0.50)
expected_results = {'input_ids':[101,2043,2001,8226,15091,2141,1029,102,5889,2287,2193,1997,5691,3058,1997,4182,8226,15091,5179,6584,2324,2285,3699,14720,4487,6178,9488,3429,5187,2340,2281,3326,2577,18856,7828,3240,5354,6353,1020,2089,3777],'attention_mask':[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
-
line length 555 characters (raw pass, confidence 0.50)
integration_expected_tokens = ['▁this', '▁is', '▁a', '▁test', '▁', '<0xF0>', '<0x9F>', '<0x98>', '<0x8A>', '▁i', '▁was', '▁born', '▁in', '▁92', '000', '<0x2C>', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁生活', '的', '真', '<0xE8>', '<0xB0>', '<0x9B>', '是', '▁hi', '▁hello'
-
line length 552 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁this', '▁is', '▁a', '▁test', '▁', '<0xF0>', '<0x9F>', '<0x98>', '<0x8A>', '▁i', '▁was', '▁born', '▁in', '▁92', '000', '<0x2C>', '▁and', '▁this', '▁is', '▁fals', 'é', '.', '▁生活', '的', '真', '<0xE8>', '<0xB0>', '<0x9B>', '是', '▁hi', '▁hello', '
-
line length 558 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [3, 9, 10088, 120, 794, 21820, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[423, 237, 440, 251], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [961, 885, 992, 912], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0,
-
line length 1016 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[3, 9, 10088, 120, 794, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [21820, 82, 564, 19, 3, 17396, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[423, 237, 440, 251], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000,
-
line length 678 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [3, 9, 10088, 120, 794, 21820, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[423, 237, 440, 251], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [961, 885, 992, 912], [1000, 1000, 1000, 1000], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0,
-
line length 1251 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[3, 9, 10088, 120, 794, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [21820, 82, 564, 19, 3, 17396, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'bbox': [[[423, 237, 440, 251], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441, 287], [419, 115, 437, 129], [1000, 1000,
-
line length 566 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [125, 31, 7, 112, 564, 58, 1, 3, 9, 10088, 120, 794, 1, 0, 0, 0, 0, 0, 0, 0], 'bbox': [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [423, 237, 440, 251], [423, 237, 440, 251], [427, 272, 441, 287], [427, 272, 441,
-
line length 1060 characters (raw pass, confidence 0.50)
expected_results = {'input_ids': [[125, 31, 7, 112, 564, 58, 1, 3, 9, 10088, 120, 794, 1, 0, 0, 0, 0, 0, 0, 0], [149, 19, 3, 88, 718, 58, 1, 125, 3, 9, 50, 99, 1807, 17, 29, 1, 0, 0, 0, 0]], 'bbox': [[[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1000, 1000, 1000, 1000], [
-
line length 504 characters (raw pass, confidence 0.50)
expected_tokens_from_ids = ['▁This', '▁is', '▁', 'a', '▁test', '▁', '<unk>', '▁I', '▁was', '▁born', '▁in', '▁9', '2000', ',', '▁and', '▁this', '▁is', '▁fal', 's', 'é', '.', '▁', '<unk>', '▁Hi', '▁Hello', '▁Hi', '▁Hello', '▁Hello', '<s>', '▁hi', '<s>', '▁there', '▁The',
-
line length 1188 characters (raw pass, confidence 0.50)
[0, 33, 0, 6, 0, 7, 0, 19, 0, 34, 0, 4, 0, 18, 0, 12, 0, 0, 0, 19, 0, 24, 0, 25, 0, 22, 0, 9, 0, 29, 0, 19, 0, 20, 0, 22, 0, 31, 0, 19, 0, 16, 0, 4, 0, 17, 0, 13, 0, 8, 0, 19, 0, 22, 0, 32, 0, 7, 0, 25, 0, 19, 0, 33, 0, 6, 0, 7, 0, 19, 0, 21, 0, 26, 0, 2, 0, 3, 0, 19, 0, 5, 0, 22, 0, 37, 0, 39, 39, 39,
-
line length 1203 characters (raw pass, confidence 0.50)
[0, 9, 0, 7, 0, 19, 0, 4, 0, 8, 0, 7, 0, 19, 0, 0, 0, 19, 0, 26, 0, 8, 0, 19, 0, 22, 0, 4, 0, 25, 0, 19, 0, 13, 0, 26, 0, 5, 0, 5, 0, 18, 0, 29, 0, 37, 0, 19, 0, 33, 0, 22, 0, 0, 0, 7, 0, 29, 0, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 3
-
compression ratio 0.765 is >2σ above file mean 0.569 (raw pass, confidence 0.50)
@unittest.skip(
reason="This test does not apply to Voxtral since inputs_embeds corresponding to audio tokens are replaced when input features are provided."
)
def test_inputs_embeds_matches_input_ids(self):
-
identifier `uː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
EXPECTED_TRANSCRIPTIONS = [
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
EXPECTED_TRANSCRIPTIONS = [
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
-
identifier `iː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
-
identifier `oɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɐ m æ n s ɛ d t ə ð ə j uː n ɪ v ɚ s s ɚ aɪ ɛ ɡ z ɪ s t",
"s w ɛ t k ʌ v ɚ d b ɹ iː ɔ n z b ɑː d i t ɹ ɪ k l ɪ ŋ ɪ n t ə ð ə t aɪ t l oɪ n k l ɑː θ ð æ w ʌ z ð ɪ oʊ"
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
-
identifier `iː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" n l i ɡ ɑːɹ m ə n t h iː w ɔːɹ",
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `tʃ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `eɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `eɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `iː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ð ə k aɪ t ɔ n h ɪ z tʃ ɛ s t s t ɪ l d ɹ ɪ p ɪ ŋ b l ʌ d ð ɪ eɪ k ʌ v h ɪ z oʊ v ɚ s t ɹ eɪ n d aɪ z iː"
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
-
identifier `iː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
-
identifier `eɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" v ə n ð ə s ɔːɹ ɹ ɪ ŋ ɐ ɹ iː n ɐ ɚ ɹ aʊ n d h ɪ m w ɪ ð ə θ aʊ z ə n d z ʌ v s p ɛ k t eɪ ɾ ɚ z w ɜː t ɹ"
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `tʃ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
" ɪ v ɪ æ l ᵻ ɾ i z n ɑː t w ɜː θ θ ɪ ŋ k ɪ ŋ ɐ b aʊ t",
"h ɪ z ɪ n s t ə n t v p æ n ɪ k w ʌ z f ɑː l oʊ d b aɪ ɐ s m ɔː l ʃ ɑːɹ p b l oʊ h aɪ ɔ n h ɪ z tʃ ɛ s t",
]
-
identifier `kʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
def test_special_characters_in_vocab(self):
sent = "ʈʰ æ æ̃ ˧ kʰ"
vocab_dict = {k: v for v, k in enumerate(set(sent.split()))}
-
line length 1215 characters (raw pass, confidence 0.50)
pred_ids = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 18, 11, 0, 0, 0, 22, 0, 0, 4, 4, 4, 14, 0, 0, 0, 0, 0, 8, 8, 0, 5, 5, 0, 12, 0, 4, 4,
-
identifier `iː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
vocab = (
"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
-
identifier `aɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
vocab = (
"<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː "
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
-
identifier `eɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `uː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `eː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `oː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `dʒ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
-
identifier `əl` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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identifier `tʃ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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identifier `aː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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identifier `oːɹ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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identifier `yː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː "
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
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identifier `iə` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `tɕ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `nʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `ɔø` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `tʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `rʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `sʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `iɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `iɛ5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `øː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 "
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
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identifier `oɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `dʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `tɕh` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `mʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `dʑ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `vʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `tʃʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `onɡ5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `ɑu5` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `iɑ5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `aɪɚ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `aɪə` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `oɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ "
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
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identifier `pː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `iɛɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `uɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `tː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `uoɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `aɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `eʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `pʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `kʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `ɑu2` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `uɨ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ "
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
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identifier `yɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `bʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `aiɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `yæɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `iouɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `onɡɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `aɨ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `iɛ2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `ɑuɜ` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `kː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `dˤ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `yɛɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ "
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
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identifier `onɡ2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `eiɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `yə5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `iɑɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `uə5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `ɨu` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `iɑ2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `ər2` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `fʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `uəɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
'əʊ S ɡʲ onɡ2 u" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ '
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
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identifier `uɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `bː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `yɛ5` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `ərɜ` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `ouɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `uaɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `dː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `ɪu` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `ər5` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ "
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
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identifier `ueiɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `æː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `eə` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `tʃː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `qː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `yəɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `kʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `tʃʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `iʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `tʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ "
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
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identifier `bʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `uə2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `yiɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `dʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `nʲʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `tsʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `eɑ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `tsː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ "
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
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identifier `dʒʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `uaiɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `tɕʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `yɛ2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `cː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `dˤdˤ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `dʒː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `pʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `dʑʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `yuɜ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `æiː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ "
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
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identifier `cʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `iɛ1` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `yə2` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
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identifier `ər4` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `iːː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `iɑ1` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `ər1` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `œː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `ɪuː` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `cʰcʰ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `iː1` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `kʰː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `xʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
-
identifier `iɛ4` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `dzː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `dʲʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `dʰː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `lː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `yɛ5ʲ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
"ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ "
"ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"
).split(" ")
vocab_tokens = dict(zip(vocab, range(len(vocab))))
-
identifier `maɪ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
token_ids = tokenizer("maɪ c", do_phonemize=False).input_ids
self.assertEqual(token_ids, [3, 200])
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ h aʊ ɑːɹ j uː")
def test_encode(self):
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ h aʊ ɑːɹ j uː")
def test_encode(self):
-
identifier `uː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ h aʊ ɑːɹ j uː")
def test_encode(self):
-
identifier `oːɹ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(tokens, batch_tokens[0])
self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
def test_phonemize_with_word_del(self):
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
def test_encode_with_del(self):
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
def test_encode_with_del(self):
-
identifier `uː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
phonemes = tokenizer.phonemize(input_text, phonemizer_lang="en-us")
self.assertEqual(phonemes, "h ə l oʊ | h aʊ | ɑːɹ | j uː |")
def test_encode_with_del(self):
-
identifier `oːɹ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(tokens, batch_tokens[0])
self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ", "j ð s j ð s oːɹ"])
-
identifier `oːɹ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(tokens, batch_tokens[0])
self.assertEqual(batch_tokens, ["k s ɾ | ɾ l | ɭʲ", "| j ð | s j ð s oːɹ"])
def test_encode_decode_with_del(self):
-
identifier `oʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
-
identifier `uː` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
-
identifier `aʊ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
self.assertEqual(text_en, "h ə l oʊ h aʊ ɑːɹ j uː")
self.assertEqual(text_fr, "ɛ l o h aʊ a ʁ j u")
def test_case_insensitive(self):
-
identifier `oːɹ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
batch_tokens = tokenizer.batch_decode(sample_ids)
self.assertEqual(batch_tokens, ["k s ɾ ɾ l ɭʲ ! ? ! ? $$$", "j ð s j ð s oːɹ $$$"])
@staticmethod
-
identifier `ksssɾɾ` mixes scripts: Latin + Other (in string/comment) (raw pass, confidence 0.40)
sample_ids = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
-
identifier `ɾlll` mixes scripts: Other + Latin (in string/comment) (raw pass, confidence 0.40)
sample_ids = [11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
-
line length 613 characters (raw pass, confidence 0.50)
[50364, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50692, 50692, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50926, 50926, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272,
-
line length 620 characters (raw pass, confidence 0.50)
[50365, 2221, 13, 2326, 388, 391, 307, 264, 50244, 295, 264, 2808, 5359, 11, 293, 321, 366, 5404, 281, 2928, 702, 14943, 13, 50629, 50682, 6966, 307, 2221, 13, 2326, 388, 391, 311, 9060, 1570, 1880, 813, 702, 1871, 13, 50870, 50911, 634, 5112, 505, 300, 412, 341, 42729, 3196, 295, 264, 1064, 11, 365, 5272,
-
compression ratio 0.805 is >2σ above file mean 0.584 (raw pass, confidence 0.50)
expected_without_prompt = " He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca."
-
compression ratio 0.800 is >2σ above file mean 0.584 (raw pass, confidence 0.50)
expected_with_prompt = " He has grave doubts whether Sir Frederick Leighton's work is really Greek after all and can discover in it but little of Rocky Ithaca."
output_without_prompt = processor.decode(output_without_prompt[0])
-
compression ratio 0.805 is >2σ above file mean 0.584 (raw pass, confidence 0.50)
" He has grave doubts whether Sir Frederick Layton's work is really Greek after all and can discover in it but little of Rocky Ithaca.",
-
line length 1336 characters (raw pass, confidence 0.50)
expected_encoding = {'input_ids': [[50257, 50362, 41762, 364, 357, 36234, 1900, 355, 12972, 13165, 354, 12, 35636, 364, 290, 12972, 13165, 354, 12, 5310, 13363, 12, 4835, 8, 3769, 2276, 12, 29983, 45619, 357, 13246, 51, 11, 402, 11571, 12, 17, 11, 5564, 13246, 38586, 11, 16276, 44, 11, 4307, 346, 33, 861, 11, 1
-
line length 827 characters (raw pass, confidence 0.50)
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 43
-
line length 1006 characters (raw pass, confidence 0.50)
67, 2840, 19, 18, 1484, 20, 965, 29077, 8719, 1273, 21, 45, 273, 17, 10, 15048, 28, 27511, 21, 4185, 11, 41, 2444, 9, 32, 1025, 20, 8719, 26, 23, 673, 966, 19, 29077, 20643, 27511, 20822, 20643, 19, 17, 6616, 17511, 18, 8978, 20, 18, 777, 9, 19233, 1527, 17669, 19, 24, 673, 17, 28756, 150, 12943, 4354, 153,
-
identifier `あл` mixes scripts: Hiragana + Cyrillic (in string/comment) (raw pass, confidence 0.40)
output = speech_recognizer(waveform)
self.assertEqual(output, {"text": "あл ش 湯 清 ه ܬ া लᆨしث ल eか u w 全 u"})
@require_torch
-
identifier `लᆨしث` mixes scripts: Other + Hangul + Hiragana + Arabic (in string/comment) (raw pass, confidence 0.40)
output = speech_recognizer(waveform)
self.assertEqual(output, {"text": "あл ش 湯 清 ه ܬ া लᆨしث ल eか u w 全 u"})
@require_torch
-
identifier `あл` mixes scripts: Hiragana + Cyrillic (in string/comment) (raw pass, confidence 0.40)
output = speech_recognizer(waveform, generate_kwargs={"num_beams": 2})
self.assertEqual(output, {"text": "あл † γ ت ב オ 束 泣 足"})
@slow
-
identifier `testкт` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
outputs = text_generator("This is a test", do_sample=False)
self.assertEqual(outputs, [{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}])
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
-
identifier `MéxicoWSAnimImportдели` contains homoglyph candidates: е (0435)→e (in string/comment) (raw pass, confidence 0.35)
outputs = text_generator("This is a test", do_sample=False)
self.assertEqual(outputs, [{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}])
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
-
identifier `MéxicoWSAnimImportдели` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
outputs = text_generator("This is a test", do_sample=False)
self.assertEqual(outputs, [{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}])
outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
-
identifier `testкт` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
[
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
-
identifier `MéxicoWSAnimImportдели` contains homoglyph candidates: е (0435)→e (in string/comment) (raw pass, confidence 0.35)
[
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
-
identifier `MéxicoWSAnimImportдели` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
[
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
-
identifier `testкт` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
)
-
identifier `MéxicoWSAnimImportдели` contains homoglyph candidates: е (0435)→e (in string/comment) (raw pass, confidence 0.35)
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
)
-
identifier `MéxicoWSAnimImportдели` mixes scripts: Latin + Cyrillic (in string/comment) (raw pass, confidence 0.40)
[{"generated_text": "This is a testкт MéxicoWSAnimImportдели pip letscosatur"}],
[{"generated_text": "This is a second testкт MéxicoWSAnimImportдели Düsseld bootstrap learn user"}],
],
)
-
identifier `δοκιμαστικό` contains homoglyph candidates: ο (03BF)→o, κ (03BA)→k, α (03B1)→a, κ (03BA)→k (in string/comment) (raw pass, confidence 0.35)
"Он ничего не сказал.",
"Αυτό είναι ένα δοκιμαστικό κείμενο.",
"यह सिर्फ एक परीक्षण वाक्य है।",
"Tôi đã sống ở Việt Nam từ năm 1990.",
-
identifier `δοκιμαστικό` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
"Он ничего не сказал.",
"Αυτό είναι ένα δοκιμαστικό κείμενο.",
"यह सिर्फ एक परीक्षण वाक्य है।",
"Tôi đã sống ở Việt Nam từ năm 1990.",
-
identifier `κείμενο` contains homoglyph candidates: κ (03BA)→k, ν (03BD)→v, ο (03BF)→o (in string/comment) (raw pass, confidence 0.35)
"Он ничего не сказал.",
"Αυτό είναι ένα δοκιμαστικό κείμενο.",
"यह सिर्फ एक परीक्षण वाक्य है।",
"Tôi đã sống ở Việt Nam từ năm 1990.",
-
identifier `κείμενο` mixes scripts: Greek + Latin (in string/comment) (raw pass, confidence 0.40)
"Он ничего не сказал.",
"Αυτό είναι ένα δοκιμαστικό κείμενο.",
"यह सिर्फ एक परीक्षण वाक्य है।",
"Tôi đã sống ở Việt Nam từ năm 1990.",
-
`builtins.print = ...` — patches a built-in for every importer of this module (ast pass, confidence 0.95)
try:
builtins.print = _print
visualizer = AttentionMaskVisualizer(model_name)
visualizer(input_text)
likely benign
LLM, confidence 0.80
Test helper temporarily overrides builtins.print to capture visualizer output, then restores it.
Reasoning
Temporarily replacing builtins.print to capture output in a test is a poor practice because it is global, but the snippet shows it restored in a finally block. This is a test-local capture pattern, not malicious. It is flagged because it affects every importer, which is a real but contained risk in a test utility.
-
`builtins.print = ...` — patches a built-in for every importer of this module (ast pass, confidence 0.95)
finally:
builtins.print = orig_print
output = buf.getvalue()
likely benign
LLM, confidence 0.80
Restores the original builtins.print after the attention visualizer test capture.
Reasoning
This is the restore step of the same temporary print capture, executed in a finally block. It undoes the earlier override, so it is cleanup rather than persistent tampering.
-
`builtins.print = ...` — patches a built-in for every importer of this module (ast pass, confidence 0.95)
try:
builtins.print = _print
visualizer = AttentionMaskVisualizer(model_name)
visualizer(input_text)
likely benign
LLM, confidence 0.80
Second test in the attention visualizer file temporarily overrides builtins.print to capture output.
Reasoning
Same temporary print-capture pattern as the other findings in this test file. The override is set within a try block and is expected to be restored. Only a test helper is affected, with no network, shell, or payload behavior.
-
`builtins.print = ...` — patches a built-in for every importer of this module (ast pass, confidence 0.95)
finally:
builtins.print = orig_print
output = buf.getvalue()
likely benign
LLM, confidence 0.92
A test helper temporarily patches builtins.print to capture output and restores it in a finally block.
Reasoning
The snippet is the `finally` block of a test that temporarily replaces builtins.print with a capturing stub, then restores the original and reads the buffered output. The restore happens in `finally`, so the patch does not leak past the test. Patching builtins is poor test hygiene, but it is a common, legitimate way to capture print output in a test, and nothing here exfiltrates data, executes code or drops payloads. The file is a test module, which fits the pattern.
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identifier `В` contains homoglyph candidates: В (0412)→B (in string/comment) (raw pass, confidence 0.35)
"README_ru.md": {
"start_prompt": "🤗 В настоящее время Transformers предоставляет следующие архитектуры",
"end_prompt": "1. Хотите внести новую модель?",
"format_model_list": (
-
U+200C ZERO WIDTH NON-JOINER in source (in string/comment) (raw pass, confidence 0.45)
"README_te.md": {
"start_prompt": "🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్⟨U+200C⟩లను అందజేస్తున్నాయి",
-
U+200C ZERO WIDTH NON-JOINER in source (in string/comment) (raw pass, confidence 0.45)
"README_te.md": {
"start_prompt": "🤗 ట్రాన్స్⟨U+200C⟩ఫార్మర్లు ప్రస్తుతం కింది ఆర్కిటెక్చర్⟨U+200C⟩లను అందజేస్తున్నాయి",
-
U+200C ZERO WIDTH NON-JOINER in source (in string/comment) (raw pass, confidence 0.45)
"end_prompt": "1. కొత్త మోడల్⟨U+200C⟩ను అందించాలనుకుంటున్నారా?",
"format_model_list": (
"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
Run disclude locally to verify. Add --format sarif for SARIF output. Add --llm (with an API key in the environment) to include the LLM review.
disclude.io