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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ModerRAS/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("ModerRAS/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 963 Bytes
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"model_dir": ".",
"onnx": "exports/anime_filename_parser.onnx",
"case_file": "data/parser_regression_cases.json",
"case_count": 28,
"repeat": 20,
"warmup": 20,
"torch_threads": 1,
"ort_threads": 1,
"constrain_bio": true,
"results": [
{
"name": "pytorch",
"load_ms": 44.04189996421337,
"runs": 560,
"avg_ms": 13.496793927853494,
"p50_ms": 13.048599968897179,
"p95_ms": 17.361054962384507,
"p99_ms": 21.3777259964263,
"min_ms": 10.096299985889345,
"max_ms": 29.508500010706484,
"throughput_fps": 74.09166986955977
},
{
"name": "onnxruntime",
"load_ms": 45.637300005182624,
"runs": 560,
"avg_ms": 12.179754106182372,
"p50_ms": 11.97419999516569,
"p95_ms": 14.452490030089393,
"p99_ms": 15.964116982650006,
"min_ms": 9.716899949125946,
"max_ms": 22.855899995192885,
"throughput_fps": 82.10346377127645
}
]
} |