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 all_results.json from universalner/uner_ser_set: direct link, hf CLI and curl.
- Browser
- Download file 842 Bytes
-
https://huggingface.co/universalner/uner_ser_set/resolve/main/all_results.json
- Command line
-
hf download hf://universalner/uner_ser_set/all_results.json
-
curl -L -o all_results.json https://huggingface.co/universalner/uner_ser_set/resolve/main/all_results.json
842 Bytes
| { | |
| "epoch": 5.0, | |
| "eval_accuracy": 0.9930792962561494, | |
| "eval_f1": 0.9413333333333335, | |
| "eval_loss": 0.04399760439991951, | |
| "eval_precision": 0.9338624338624338, | |
| "eval_recall": 0.9489247311827957, | |
| "eval_runtime": 1.4789, | |
| "eval_samples": 537, | |
| "eval_samples_per_second": 363.114, | |
| "eval_steps_per_second": 22.99, | |
| "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, | |
| "train_loss": 0.03677053214830645, | |
| "train_runtime": 220.3164, | |
| "train_samples": 3329, | |
| "train_samples_per_second": 75.55, | |
| "train_steps_per_second": 4.743 | |
| } |