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
File size: 657 Bytes
be5f706 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | # 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/`
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