| | --- |
| | license: apache-2.0 |
| | base_model: bert-base-uncased |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - conll2003 |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: BERT-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: 0.9449011330815374 |
| | - name: Recall |
| | type: recall |
| | value: 0.9515605772457769 |
| | - name: F1 |
| | type: f1 |
| | value: 0.9482191628114375 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.987243236373457 |
| | --- |
| | |
| | <!-- 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-ner |
| |
|
| | This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0664 |
| | - Precision: 0.9449 |
| | - Recall: 0.9516 |
| | - F1: 0.9482 |
| | - Accuracy: 0.9872 |
| |
|
| | ## 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: 16 |
| | - eval_batch_size: 16 |
| | - 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 | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.0252 | 1.0 | 878 | 0.0652 | 0.9414 | 0.9419 | 0.9417 | 0.9854 | |
| | | 0.0121 | 2.0 | 1756 | 0.0615 | 0.9407 | 0.9498 | 0.9452 | 0.9867 | |
| | | 0.0079 | 3.0 | 2634 | 0.0664 | 0.9449 | 0.9516 | 0.9482 | 0.9872 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.31.0 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.13.1 |
| | - Tokenizers 0.13.3 |
| |
|
