Instructions to use THemidli/applied-ner-stage3-bert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage3-bert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage3-bert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage3-bert-tiny") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage3-bert-tiny", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "overall_precision": 0.9580712788259959, | |
| "overall_recall": 0.9725990955041235, | |
| "overall_f1": 0.9652805280528053, | |
| "overall_accuracy": 0.9956925601429952, | |
| "person_precision": 0.9903181189488244, | |
| "person_recall": 0.9972144846796658, | |
| "person_f1_score": 0.9937543372657877, | |
| "person_support": 718.0, | |
| "organization_precision": 0.9649484536082474, | |
| "organization_recall": 0.972972972972973, | |
| "organization_f1_score": 0.968944099378882, | |
| "organization_support": 481.0, | |
| "location_precision": 0.966804979253112, | |
| "location_recall": 0.9789915966386554, | |
| "location_f1_score": 0.9728601252609603, | |
| "location_support": 476.0, | |
| "timedate_precision": 0.9828767123287672, | |
| "timedate_recall": 0.9845626072041166, | |
| "timedate_f1_score": 0.9837189374464438, | |
| "timedate_support": 583.0, | |
| "product_precision": 0.9764705882352941, | |
| "product_recall": 0.9651162790697675, | |
| "product_f1_score": 0.9707602339181286, | |
| "product_support": 430.0, | |
| "workofart_precision": 0.9627791563275434, | |
| "workofart_recall": 0.9675810473815462, | |
| "workofart_f1_score": 0.9651741293532339, | |
| "workofart_support": 401.0, | |
| "job_precision": 0.8016997167138811, | |
| "job_recall": 0.9012738853503185, | |
| "job_f1_score": 0.8485757121439281, | |
| "job_support": 314.0, | |
| "amount_precision": 0.9584487534626038, | |
| "amount_recall": 0.9719101123595506, | |
| "amount_f1_score": 0.9651324965132496, | |
| "amount_support": 356.0 | |
| } | |