| license: mit | |
| tags: | |
| - generated_from_trainer | |
| datasets: | |
| - conll2003 | |
| metrics: | |
| - precision | |
| - recall | |
| - f1 | |
| - accuracy | |
| base_model: roberta-base | |
| model-index: | |
| - name: roberta-base-conll2003-pos | |
| results: | |
| - task: | |
| type: token-classification | |
| name: Token Classification | |
| dataset: | |
| name: conll2003 | |
| type: conll2003 | |
| args: conll2003 | |
| metrics: | |
| - type: precision | |
| value: 0.9308159300631375 | |
| name: Precision | |
| - type: recall | |
| value: 0.9300254761615917 | |
| name: Recall | |
| - type: f1 | |
| value: 0.9304205352266521 | |
| name: F1 | |
| - type: accuracy | |
| value: 0.9523967135236167 | |
| name: Accuracy | |
| <!-- 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. --> | |
| # roberta-base-conll2003-pos | |
| This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.1947 | |
| - Precision: 0.9308 | |
| - Recall: 0.9300 | |
| - F1: 0.9304 | |
| - Accuracy: 0.9524 | |
| ## 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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | |
| | 0.617 | 1.0 | 878 | 0.2189 | 0.9239 | 0.9210 | 0.9225 | 0.9470 | | |
| | 0.1667 | 2.0 | 1756 | 0.1947 | 0.9308 | 0.9300 | 0.9304 | 0.9524 | | |
| ### Framework versions | |
| - Transformers 4.18.0 | |
| - Pytorch 1.14.0.dev20221107 | |
| - Datasets 2.2.2 | |
| - Tokenizers 0.12.1 | |