---
license: apache-2.0
library_name: transformers
pipeline_tag: token-classification
tags:
- anime
- filename-parsing
- bert
- token-classification
datasets:
- ModerRAS/AnimeName
language:
- en
- ja
- zh
---

# AniFileBERT

AniFileBERT is a tiny BERT token-classification model for parsing anime release filenames into structured fields such as release group, title, season, episode, resolution, source, and special tags.

The checkpoint in this repository is the DMHY weak-label fine-tuned regex-tokenizer model used by MiruPlay.

## Model

- Architecture: `BertForTokenClassification`
- Hidden size: 256
- Layers: 4
- Attention heads: 8
- Labels: BIO token labels for `TITLE`, `SEASON`, `EPISODE`, `GROUP`, `RESOLUTION`, `SOURCE`, and `SPECIAL`
- Tokenizer: custom regex/structure tokenizer implemented in `tokenizer.py`
- Max sequence length: 64
- Parameters: about 4M

The model files are stored at the repository root so `BertForTokenClassification.from_pretrained("ModerRAS/AniFileBERT")` can load the weights. Use `inference.py` for end-to-end parsing because the tokenizer is custom rather than a standard WordPiece tokenizer.

## Dataset

Training data snapshots are published separately in [`ModerRAS/AnimeName`](https://huggingface.co/datasets/ModerRAS/AnimeName), and this repository includes it as a nested git submodule at `datasets/AnimeName`.

Current DMHY export waterline:

- Last exported `files.id`: `689304`
- Next incremental export: `--min-id 689305`
- Weak-labeled samples: `263042`
- Mixed training samples: `363042`

## Evaluation

Balanced mixed-data A/B run (`50K` synthetic + `50K` DMHY weak labels, 1 epoch, batch size 128, seed 42):

| Variant | Max length | Vocab | Params | Eval F1 | Accuracy | Train runtime |
|---------|------------|-------|--------|---------|----------|---------------|
| regex | 64 | 3000 | 3.96M | 0.9911 | 0.9951 | 827s |
| char | 128 | 2654 | 3.88M | 0.8142 | 0.9637 | 1983s |

Field-level F1 on the same validation split:

| Field | regex | char |
|-------|-------|------|
| GROUP | 0.9962 | 0.9516 |
| TITLE | 0.9761 | 0.7983 |
| SEASON | 0.9880 | 0.6290 |
| EPISODE | 0.9950 | 0.8082 |

The regex tokenizer remains the default. Both variants can parse simple `S01E07`, but the character tokenizer was weaker on season/episode boundaries and long title spans.

## Usage

Install dependencies:

```bash
pip install -r requirements.txt
```

Parse a filename with this repository cloned locally:

```bash
python inference.py --model-dir . "Witch.Hat.Atelier.S01E07.1080p.NF.WEB-DL.JPN.AAC2.0.H.264.MSubs-ToonsHub"
```

Load only the model weights from the Hub:

```python
from transformers import BertForTokenClassification

model = BertForTokenClassification.from_pretrained("ModerRAS/AniFileBERT")
```

For full parsing, clone this repo and use `load_tokenizer` from `tokenizer.py` or the CLI in `inference.py`.

## Clone with Dataset Submodule

```bash
git clone --recursive https://huggingface.co/ModerRAS/AniFileBERT
# or, after a normal clone:
git submodule update --init --recursive
```

## Training

Regenerate or export datasets:

```bash
python data_generator.py --num-samples 100000
python dmhy_dataset.py --db D:/WorkSpace/Python/dmhy-parser/dmhy_anime.db --output data/dmhy/dmhy_weak.jsonl
python mix_datasets.py --synthetic data/synthetic.jsonl --dmhy data/dmhy/dmhy_weak.jsonl --output data/dmhy/mixed_train.jsonl
```

Fine-tune from the synthetic checkpoint or train from scratch:

```bash
python train.py --data-file data/dmhy/mixed_train.jsonl --save-dir checkpoints/dmhy-finetune --init-model-dir checkpoints/final --epochs 1 --batch-size 128 --learning-rate 0.0003 --warmup-steps 300 --seed 42
```

Export ONNX for MiruPlay Android assets:

```bash
python export_onnx.py --model-dir checkpoints/dmhy-finetune/final --output exports/anime_filename_parser.onnx
```

## Repository Layout

- `model.safetensors`, `config.json`, `vocab.json`: default fine-tuned model
- `train.py`, `dataset.py`, `tokenizer.py`, `model.py`: training pipeline
- `dmhy_dataset.py`, `mix_datasets.py`: weak-label export and dataset mixing
- `inference.py`: end-to-end filename parser CLI
- `export_onnx.py`: ONNX export for Android integration
- `exports/`: exported ONNX model and metadata
- `data/dmhy/*.manifest.json`: dataset waterlines and counts
- `datasets/AnimeName/`: nested dataset submodule

## Maintenance Notes

MiruPlay tracks this repository as `tools/anime_parser`, and this repository
tracks `ModerRAS/AnimeName` as `datasets/AnimeName`. After updating either
repo, remember to commit the submodule pointer in the parent repo.

For the full maintenance workflow, see MiruPlay's
`docs/anifilebert-maintenance.md`.