Instructions to use apwic/nerugm-base-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use apwic/nerugm-base-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="apwic/nerugm-base-2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("apwic/nerugm-base-2") model = AutoModelForTokenClassification.from_pretrained("apwic/nerugm-base-2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download predict_results.json from apwic/nerugm-base-2: direct link, hf CLI and curl.
- Browser
- Download file 1.25 kB
-
https://huggingface.co/apwic/nerugm-base-2/resolve/main/predict_results.json
- Command line
-
hf download hf://apwic/nerugm-base-2/predict_results.json
-
curl -L -o predict_results.json https://huggingface.co/apwic/nerugm-base-2/resolve/main/predict_results.json
1.25 kB
| { | |
| "predict_LOCATION_f1": 0.9855072463768116, | |
| "predict_LOCATION_number": 207, | |
| "predict_LOCATION_precision": 0.9855072463768116, | |
| "predict_LOCATION_recall": 0.9855072463768116, | |
| "predict_ORGANIZATION_f1": 0.9767441860465116, | |
| "predict_ORGANIZATION_number": 172, | |
| "predict_ORGANIZATION_precision": 0.9767441860465116, | |
| "predict_ORGANIZATION_recall": 0.9767441860465116, | |
| "predict_PERSON_f1": 0.9931271477663229, | |
| "predict_PERSON_number": 290, | |
| "predict_PERSON_precision": 0.9897260273972602, | |
| "predict_PERSON_recall": 0.996551724137931, | |
| "predict_QUANTITY_f1": 0.99, | |
| "predict_QUANTITY_number": 100, | |
| "predict_QUANTITY_precision": 0.99, | |
| "predict_QUANTITY_recall": 0.99, | |
| "predict_TIME_f1": 0.9759036144578312, | |
| "predict_TIME_number": 82, | |
| "predict_TIME_precision": 0.9642857142857143, | |
| "predict_TIME_recall": 0.9878048780487805, | |
| "predict_loss": 0.037839509546756744, | |
| "predict_overall_accuracy": 0.9965746721471912, | |
| "predict_overall_f1": 0.9859320046893318, | |
| "predict_overall_precision": 0.9836257309941521, | |
| "predict_overall_recall": 0.9882491186839013, | |
| "predict_runtime": 0.8312, | |
| "predict_samples_per_second": 564.224, | |
| "predict_steps_per_second": 9.624 | |
| } |