| --- |
| 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 |
| args: conll2003 |
| metrics: |
| - name: Precision |
| type: precision |
| value: 0.9295401918623883 |
| - name: Recall |
| type: recall |
| value: 0.9458094917536183 |
| - name: F1 |
| type: f1 |
| value: 0.9376042709376042 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9848413492670866 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # 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: 0.0673 |
| - Precision: 0.9295 |
| - Recall: 0.9458 |
| - F1: 0.9376 |
| - Accuracy: 0.9848 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.0846 | 1.0 | 1756 | 0.0660 | 0.9073 | 0.9344 | 0.9207 | 0.9820 | |
| | 0.0409 | 2.0 | 3512 | 0.0622 | 0.9230 | 0.9456 | 0.9342 | 0.9851 | |
| | 0.0202 | 3.0 | 5268 | 0.0673 | 0.9295 | 0.9458 | 0.9376 | 0.9848 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.19.2 |
| - Pytorch 1.11.0+cu113 |
| - Datasets 2.2.1 |
| - Tokenizers 0.12.1 |
|
|
