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/trainer_eval_metrics.json from ModerRAS/AniFileBERT: direct link, hf CLI and curl.
- Browser
- Download file 347 Bytes
-
https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/trainer_eval_metrics.json
- Command line
-
hf download hf://ModerRAS/AniFileBERT/reports/trainer_eval_metrics.json
-
curl -L -o trainer_eval_metrics.json https://huggingface.co/ModerRAS/AniFileBERT/resolve/main/reports/trainer_eval_metrics.json
347 Bytes
| { | |
| "eval_loss": 0.00017842209490481764, | |
| "eval_precision": 0.9998627806136451, | |
| "eval_recall": 0.9998627806136451, | |
| "eval_f1": 0.9998627806136451, | |
| "eval_accuracy": 0.9999632255180959, | |
| "eval_runtime": 2.8101, | |
| "eval_samples_per_second": 1911.682, | |
| "eval_steps_per_second": 3.914, | |
| "epoch": 10.0, | |
| "num_input_tokens_seen": 1368179200.0 | |
| } |