Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Eval Results (legacy)
Instructions to use ModerRAS/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModerRAS/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ModerRAS/AniFileBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Use PyTorch cu132 nightly for Windows
Browse files- pyproject.toml +4 -4
- uv.lock +0 -0
pyproject.toml
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@@ -14,7 +14,7 @@ dependencies = [
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"onnxscript==0.7.0",
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"seqeval==1.2.2",
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"tensorboard>=2.14.0",
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"torch==2.
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"transformers==5.8.1",
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]
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@@ -27,10 +27,10 @@ environments = ["sys_platform == 'win32'"]
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-
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]
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[[tool.uv.index]]
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name = "pytorch-
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url = "https://download.pytorch.org/whl/
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explicit = true
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"onnxscript==0.7.0",
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"seqeval==1.2.2",
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"tensorboard>=2.14.0",
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"torch==2.13.0.dev20260601+cu132",
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"transformers==5.8.1",
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]
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[tool.uv.sources]
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torch = [
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{ index = "pytorch-cu132-nightly", marker = "platform_system == 'Windows'" },
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]
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[[tool.uv.index]]
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name = "pytorch-cu132-nightly"
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url = "https://download.pytorch.org/whl/nightly/cu132"
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explicit = true
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uv.lock
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