| | --- |
| | license: apache-2.0 |
| | tags: |
| | - generated_from_trainer |
| | datasets: |
| | - conll2003 |
| | - autoevaluate/conll2003-sample |
| | metrics: |
| | - precision |
| | - recall |
| | - f1 |
| | - accuracy |
| | model-index: |
| | - name: entity-extraction |
| | results: |
| | - task: |
| | name: Token Classification |
| | type: token-classification |
| | dataset: |
| | name: conll2003 |
| | type: conll2003 |
| | args: conll2003 |
| | metrics: |
| | - name: Precision |
| | type: precision |
| | value: 0.8862817854414493 |
| | - name: Recall |
| | type: recall |
| | value: 0.9084908826490659 |
| | - name: F1 |
| | type: f1 |
| | value: 0.8972489227709645 |
| | - name: Accuracy |
| | type: accuracy |
| | value: 0.9774889986814304 |
| | --- |
| | |
| | <!-- 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. --> |
| |
|
| | # entity-extraction |
| |
|
| | This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 0.0808 |
| | - Precision: 0.8863 |
| | - Recall: 0.9085 |
| | - F1: 0.8972 |
| | - Accuracy: 0.9775 |
| |
|
| | ## 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: 1 |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | | 0.2552 | 1.0 | 878 | 0.0808 | 0.8863 | 0.9085 | 0.8972 | 0.9775 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - Transformers 4.19.2 |
| | - Pytorch 1.11.0+cu113 |
| | - Datasets 2.2.2 |
| | - Tokenizers 0.12.1 |
| |
|
