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
Download data/dmhy/mixed_train.manifest.json from ModerRAS/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 215 Bytes
-
https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/data/dmhy/mixed_train.manifest.json
- Command line
-
hf download hf://ModerRAS/AniFileBERT/data/dmhy/mixed_train.manifest.json
-
curl -L -o mixed_train.manifest.json https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/data/dmhy/mixed_train.manifest.json
215 Bytes
| { | |
| "synthetic": "data/synthetic.jsonl", | |
| "dmhy": "data/dmhy/dmhy_weak.jsonl", | |
| "output": "data/dmhy/mixed_train.jsonl", | |
| "synthetic_count": 100000, | |
| "dmhy_count": 632002, | |
| "total_count": 732002, | |
| "seed": 42 | |
| } |