Token Classification
Transformers
ONNX
Safetensors
English
Japanese
Chinese
bert
anime
filename-parsing
Instructions to use chivehao/AniFileBERT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chivehao/AniFileBERT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chivehao/AniFileBERT")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("chivehao/AniFileBERT") model = AutoModelForTokenClassification.from_pretrained("chivehao/AniFileBERT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download trainer_eval_metrics.json from chivehao/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 302 Bytes
-
https://huggingface.co/chivehao/AniFileBERT/resolve/main/trainer_eval_metrics.json
- Command line
-
hf download hf://chivehao/AniFileBERT/trainer_eval_metrics.json
-
curl -L -o trainer_eval_metrics.json https://huggingface.co/chivehao/AniFileBERT/resolve/main/trainer_eval_metrics.json
302 Bytes
| { | |
| "eval_loss": 0.005763721186667681, | |
| "eval_precision": 0.9921522239605195, | |
| "eval_recall": 0.9946191314105016, | |
| "eval_f1": 0.9933841461473317, | |
| "eval_accuracy": 0.9980711558885925, | |
| "eval_runtime": 45.558, | |
| "eval_samples_per_second": 277.471, | |
| "eval_steps_per_second": 1.098, | |
| "epoch": 2.0 | |
| } |