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")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT") - Notebooks
- Google Colab
- Kaggle
| # DMHY Dataset Snapshot | |
| This directory keeps only small metadata files in git. Large generated JSONL | |
| datasets and model checkpoints are ignored and should be published as release | |
| assets when they need to be shared. | |
| Current exported SQLite waterline: | |
| - Source DB: `D:\WorkSpace\Python\dmhy-parser\dmhy_anime.db` | |
| - Last exported `files.id`: `689304` | |
| - Labeled samples: `263042` | |
| - Export manifest: `dmhy_weak.manifest.json` | |
| Use `--min-id 689305` for the next incremental export after the crawler has | |
| finished collecting more rows. | |
| Suggested release assets for this snapshot: | |
| - `dmhy_weak.jsonl` | |
| - `mixed_train.jsonl` | |
| - `checkpoints/dmhy-finetune/final/` | |