Instructions to use universalner/uner_ser_set with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use universalner/uner_ser_set with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="universalner/uner_ser_set")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("universalner/uner_ser_set") model = AutoModelForTokenClassification.from_pretrained("universalner/uner_ser_set", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from universalner/uner_ser_set: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
-
https://huggingface.co/universalner/uner_ser_set/resolve/main/predict_results.json
- Command line
-
hf download hf://universalner/uner_ser_set/predict_results.json
-
curl -L -o predict_results.json https://huggingface.co/universalner/uner_ser_set/resolve/main/predict_results.json
327 Bytes
| { | |
| "predict_accuracy": 0.9929953594256195, | |
| "predict_f1": 0.9617422012948793, | |
| "predict_loss": 0.056257136166095734, | |
| "predict_precision": 0.9589201877934272, | |
| "predict_recall": 0.9645808736717828, | |
| "predict_runtime": 1.4293, | |
| "predict_samples_per_second": 364.515, | |
| "predict_steps_per_second": 23.088 | |
| } |