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
| { | |
| "name": "dmhy-char-train", | |
| "repo_url": "https://huggingface.co/ModerRAS/AniFileBERT", | |
| "repo_ref": "main", | |
| "repo_dir": "/content/AniFileBERT", | |
| "drive_root": "/content/drive/MyDrive/AniFileBERT", | |
| "mount_drive": true, | |
| "pull": true, | |
| "install": { | |
| "requirements": true, | |
| "git_lfs": true, | |
| "extra_packages": [] | |
| }, | |
| "training": { | |
| "tokenizer": "char", | |
| "data_file": "datasets/AnimeName/dmhy_weak_char.jsonl", | |
| "vocab_file": "datasets/AnimeName/vocab.char.json", | |
| "save_dir": "{drive_root}/checkpoints/{name}", | |
| "init_model_dir": null, | |
| "epochs": 1, | |
| "batch_size": 128, | |
| "learning_rate": 0.0003, | |
| "warmup_steps": 300, | |
| "train_split": 0.9, | |
| "max_seq_length": 128, | |
| "seed": 42, | |
| "resume_from_checkpoint": "auto", | |
| "checkpoint_steps": 1000, | |
| "save_total_limit": 3 | |
| }, | |
| "export": { | |
| "enabled": true, | |
| "required": false, | |
| "output": "{save_dir}/exports/anime_filename_parser.onnx", | |
| "max_length": "{max_seq_length}" | |
| }, | |
| "smoke": { | |
| "enabled": true, | |
| "required": true, | |
| "sample": "Witch.Hat.Atelier.S01E07.1080p.NF.WEB-DL.JPN.AAC2.0.H.264.MSubs-ToonsHub" | |
| } | |
| } | |