Instructions to use THemidli/applied-ner-stage4-bert-tiny-improved with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use THemidli/applied-ner-stage4-bert-tiny-improved with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="THemidli/applied-ner-stage4-bert-tiny-improved")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved") model = AutoModelForTokenClassification.from_pretrained("THemidli/applied-ner-stage4-bert-tiny-improved", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "overall_precision": 0.9539951573849879, | |
| "overall_recall": 0.9708781362007168, | |
| "overall_f1": 0.9623626068613301, | |
| "overall_accuracy": 0.9948478767829058, | |
| "person_precision": 0.9862857142857143, | |
| "person_recall": 0.9953863898500577, | |
| "person_f1_score": 0.9908151549942594, | |
| "person_support": 867.0, | |
| "organization_precision": 0.9662162162162162, | |
| "organization_recall": 0.9711375212224108, | |
| "organization_f1_score": 0.9686706181202371, | |
| "organization_support": 589.0, | |
| "location_precision": 0.9738562091503268, | |
| "location_recall": 0.9834983498349835, | |
| "location_f1_score": 0.9786535303776683, | |
| "location_support": 606.0, | |
| "timedate_precision": 0.978978978978979, | |
| "timedate_recall": 0.9834087481146304, | |
| "timedate_f1_score": 0.981188863807374, | |
| "timedate_support": 663.0, | |
| "product_precision": 0.9625984251968503, | |
| "product_recall": 0.9607072691552063, | |
| "product_f1_score": 0.9616519174041297, | |
| "product_support": 509.0, | |
| "workofart_precision": 0.9707207207207207, | |
| "workofart_recall": 0.9729119638826185, | |
| "workofart_f1_score": 0.971815107102593, | |
| "workofart_support": 443.0, | |
| "job_precision": 0.7661691542288557, | |
| "job_recall": 0.875, | |
| "job_f1_score": 0.8169761273209549, | |
| "job_support": 352.0, | |
| "amount_precision": 0.9527027027027027, | |
| "amount_recall": 0.9724137931034482, | |
| "amount_f1_score": 0.9624573378839592, | |
| "amount_support": 435.0 | |
| } | |