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
Download reports/benchmark_results.json from ModerRAS/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 963 Bytes
-
https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/benchmark_results.json
- Command line
-
hf download hf://ModerRAS/AniFileBERT/reports/benchmark_results.json
-
curl -L -o benchmark_results.json https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/benchmark_results.json
963 Bytes
| { | |
| "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 | |
| } | |
| ] | |
| } |