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
Commit History
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chore: update AnimeName submodule after relabel chunks
87c7b59 -
Advance AnimeName relabel submodule after additional batches
fb5c9c2 -
Advance AnimeName relabel submodule after next batches
aaa7735 -
Advance AnimeName relabel submodule batches
ac7e795 -
Add robust LLM relabel pipeline and enforce contiguous title
fed9d99 -
feat: add concurrency and token/cost telemetry for llm relabel runs
7c5b41e -
feat: add tool-call based llm relabel pipeline and update dataset pointer
e964ae5 -
chore: update AnimeName submodule checkpoint
1804249
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Train thin anime filename parser
f712f4b
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Clean special code parsing
e8412e3 -
Fix GM-Team bilingual title parsing
adf92db -
Improve anime filename parser model
e63569d
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Add character-token DMHY training path
0779202 -
完成整个数据集的整理
f4f4e0e
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Add AniFileBERT model and training project
Publish the fine-tuned filename parser checkpoint, training/export scripts, documentation, and a nested AnimeName dataset submodule.be5f706