| | --- |
| | 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: 0.9419583517944173 |
| | - name: Recall |
| | type: recall |
| | value: 0.9513368385725472 |
| | - name: F1 |
| | type: f1 |
| | value: 0.9466243668948628 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9864171445819498 |
| | --- |
| | |
| | <!-- 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: 0.0648 |
| | - Precision: 0.9420 |
| | - Recall: 0.9513 |
| | - F1: 0.9466 |
| | - Accuracy: 0.9864 |
| |
|
| | ## 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: 5 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.2234 | 1.0 | 878 | 0.0648 | 0.9110 | 0.9327 | 0.9217 | 0.9821 | |
| | | 0.0443 | 2.0 | 1756 | 0.0552 | 0.9345 | 0.9432 | 0.9388 | 0.9854 | |
| | | 0.0258 | 3.0 | 2634 | 0.0571 | 0.9385 | 0.9451 | 0.9418 | 0.9856 | |
| | | 0.0139 | 4.0 | 3512 | 0.0623 | 0.9413 | 0.9500 | 0.9456 | 0.9863 | |
| | | 0.0098 | 5.0 | 4390 | 0.0648 | 0.9420 | 0.9513 | 0.9466 | 0.9864 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.33.3 |
| | - Pytorch 2.0.1+cu118 |
| | - Datasets 2.14.5 |
| | - Tokenizers 0.13.3 |
| |
|
