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: 1,147 Bytes
e458112 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | {
"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"
}
}
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