Instructions to use Zlovoblachko/roberta-base_binary with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zlovoblachko/roberta-base_binary with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Zlovoblachko/roberta-base_binary")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Zlovoblachko/roberta-base_binary") model = AutoModelForTokenClassification.from_pretrained("Zlovoblachko/roberta-base_binary", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download test_results.json from Zlovoblachko/roberta-base_binary: direct link, hf CLI and curl.
- Browser
- Download file 480 Bytes
-
https://huggingface.co/Zlovoblachko/roberta-base_binary/resolve/main/test_results.json
- Command line
-
hf download hf://Zlovoblachko/roberta-base_binary/test_results.json
-
curl -L -o test_results.json https://huggingface.co/Zlovoblachko/roberta-base_binary/resolve/main/test_results.json
480 Bytes
| { | |
| "eval_loss": 0.1729455292224884, | |
| "eval_precision": 0.8178217821782178, | |
| "eval_recall": 0.6136416399684265, | |
| "eval_f1": 0.7011698543650688, | |
| "eval_f0.5": 0.7667939241328893, | |
| "eval_macro_precision": 0.8824056575716628, | |
| "eval_macro_recall": 0.7971105450356671, | |
| "eval_macro_f1": 0.832330838039707, | |
| "eval_macro_f0.5": 0.8601580350033708, | |
| "eval_runtime": 55.7132, | |
| "eval_samples_per_second": 138.226, | |
| "eval_steps_per_second": 8.651, | |
| "epoch": 1.9992209815632305 | |
| } |