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: 962 Bytes
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"model_dir": ".",
"onnx": "exports/anime_filename_parser.onnx",
"case_file": "data/parser_regression_cases.json",
"case_count": 26,
"repeat": 20,
"warmup": 20,
"torch_threads": 1,
"ort_threads": 1,
"constrain_bio": true,
"results": [
{
"name": "pytorch",
"load_ms": 46.3533999864012,
"runs": 520,
"avg_ms": 15.362302694120444,
"p50_ms": 14.245550031773746,
"p95_ms": 22.27204497321509,
"p99_ms": 29.752646028064174,
"min_ms": 10.793900000862777,
"max_ms": 42.94239997398108,
"throughput_fps": 65.09440803967013
},
{
"name": "onnxruntime",
"load_ms": 50.916100037284195,
"runs": 520,
"avg_ms": 12.039251922844695,
"p50_ms": 11.899999983143061,
"p95_ms": 13.811619929037988,
"p99_ms": 15.376427990850043,
"min_ms": 9.72980004735291,
"max_ms": 19.285599933937192,
"throughput_fps": 83.06163924541542
}
]
} |