| | --- |
| | 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: 0.93732429303787 |
| | - name: Recall |
| | type: recall |
| | value: 0.9538875799394143 |
| | - name: F1 |
| | type: f1 |
| | value: 0.94553340562182 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9866809913463237 |
| | --- |
| | |
| | <!-- 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: 0.0998 |
| | - Precision: 0.9373 |
| | - Recall: 0.9539 |
| | - F1: 0.9455 |
| | - 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: 10 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.0878 | 1.0 | 1756 | 0.0694 | 0.9166 | 0.9288 | 0.9227 | 0.9819 | |
| | | 0.0366 | 2.0 | 3512 | 0.0718 | 0.9247 | 0.9467 | 0.9356 | 0.9850 | |
| | | 0.0247 | 3.0 | 5268 | 0.0727 | 0.9220 | 0.9435 | 0.9326 | 0.9844 | |
| | | 0.0153 | 4.0 | 7024 | 0.0746 | 0.9384 | 0.9532 | 0.9457 | 0.9860 | |
| | | 0.0107 | 5.0 | 8780 | 0.0874 | 0.9260 | 0.9475 | 0.9366 | 0.9847 | |
| | | 0.0043 | 6.0 | 10536 | 0.0898 | 0.9373 | 0.9517 | 0.9445 | 0.9863 | |
| | | 0.0041 | 7.0 | 12292 | 0.0984 | 0.9371 | 0.9507 | 0.9439 | 0.9858 | |
| | | 0.0031 | 8.0 | 14048 | 0.0982 | 0.9327 | 0.9515 | 0.9420 | 0.9856 | |
| | | 0.0014 | 9.0 | 15804 | 0.0987 | 0.9361 | 0.9544 | 0.9452 | 0.9860 | |
| | | 0.0006 | 10.0 | 17560 | 0.0998 | 0.9373 | 0.9539 | 0.9455 | 0.9867 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.26.1 |
| | - Pytorch 1.13.1+cu116 |
| | - Datasets 2.9.0 |
| | - Tokenizers 0.13.2 |
| |
|
