--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: validation args: conll2003 metrics: - name: Precision type: precision value: 0.9337737407101568 - name: Recall type: recall value: 0.9515314708852238 - name: F1 type: f1 value: 0.9425689755772277 - name: Accuracy type: accuracy value: 0.9867251427562254 --- # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0604 - Precision: 0.9338 - Recall: 0.9515 - F1: 0.9426 - Accuracy: 0.9867 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0869 | 1.0 | 1756 | 0.0717 | 0.9177 | 0.9293 | 0.9235 | 0.9815 | | 0.0341 | 2.0 | 3512 | 0.0625 | 0.9306 | 0.9498 | 0.9401 | 0.9860 | | 0.0179 | 3.0 | 5268 | 0.0604 | 0.9338 | 0.9515 | 0.9426 | 0.9867 | ### Framework versions - Transformers 4.26.1 - Pytorch 1.13.1+cu116 - Datasets 2.9.0 - Tokenizers 0.13.2