| --- |
| license: apache-2.0 |
| base_model: google-bert/bert-base-uncased |
| tags: |
| - generated_from_trainer |
| datasets: |
| - conll2003 |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: test-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: 0.946619812583668 |
| - name: Recall |
| type: recall |
| value: 0.9520363513968361 |
| - name: F1 |
| type: f1 |
| value: 0.9493203557643899 |
| - name: Accuracy |
| type: accuracy |
| value: 0.9896226782446167 |
| --- |
| |
| <!-- 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. --> |
|
|
| # test-ner |
|
|
| This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the conll2003 dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0498 |
| - Precision: 0.9466 |
| - Recall: 0.9520 |
| - F1: 0.9493 |
| - Accuracy: 0.9896 |
|
|
| ## 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: 5e-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: 3.0 |
|
|
| ### Training results |
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|
| ### Framework versions |
|
|
| - Transformers 4.42.0.dev0 |
| - Pytorch 2.3.0+cu121 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
|
|
