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
| { | |
| "created_at": "2026-05-13T15:26:19.767707+00:00", | |
| "source_db": "D:\\WorkSpace\\Python\\dmhy-parser\\dmhy_anime.db", | |
| "output": "data\\dmhy\\dmhy_weak_new.jsonl", | |
| "min_file_id": 689305, | |
| "last_file_id": 1675184, | |
| "db_max_file_id_at_export_start": 1675184, | |
| "limit": null, | |
| "stats": { | |
| "scanned_rows": 985880, | |
| "video_rows": 556778, | |
| "duplicate_basenames": 95422, | |
| "labeled_samples": 378327, | |
| "skipped_no_episode": 82422, | |
| "skipped_no_title": 0, | |
| "skipped_too_short": 606, | |
| "skipped_too_long": 1 | |
| }, | |
| "label_counts": { | |
| "B-GROUP": 306878, | |
| "B-TITLE": 390543, | |
| "B-EPISODE": 378327, | |
| "B-RESOLUTION": 156089, | |
| "B-SOURCE": 180428, | |
| "O": 1587219, | |
| "I-TITLE": 1401899, | |
| "B-SPECIAL": 29468, | |
| "B-SEASON": 18792, | |
| "I-GROUP": 517 | |
| }, | |
| "vocab_size": 3000, | |
| "notes": [ | |
| "Rows are a snapshot of files.id <= last_file_id.", | |
| "Future incremental export can use --min-id last_file_id+1.", | |
| "Weak labels target GROUP, TITLE, SEASON, and EPISODE; media tags are boundary labels/noise." | |
| ], | |
| "examples": [] | |
| } |