| --- |
| library_name: transformers |
| license: other |
| base_model: IIC/RigoBERTa-Clinical |
| tags: |
| - generated_from_trainer |
| metrics: |
| - precision |
| - recall |
| - f1 |
| - accuracy |
| model-index: |
| - name: RigoBERTa-Clinical_es_symptom |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/svassileva/MultiClinAI-NER/runs/nsgeghla) |
| # RigoBERTa-Clinical_es_symptom |
|
|
| This model is a fine-tuned version of [IIC/RigoBERTa-Clinical](https://huggingface.co/IIC/RigoBERTa-Clinical) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.0049 |
| - Precision: 0.9854 |
| - Recall: 0.9908 |
| - F1: 0.9881 |
| - Accuracy: 0.9983 |
|
|
| ## 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: 64 |
| - eval_batch_size: 64 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: linear |
| - num_epochs: 10 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1790 | 1.0 | 739 | 0.0919 | 0.8265 | 0.8546 | 0.8403 | 0.9635 | |
| | 0.1143 | 2.0 | 1478 | 0.0648 | 0.8832 | 0.8979 | 0.8904 | 0.9751 | |
| | 0.0765 | 3.0 | 2217 | 0.0436 | 0.9203 | 0.9217 | 0.9210 | 0.9841 | |
| | 0.0631 | 4.0 | 2956 | 0.0294 | 0.9363 | 0.9532 | 0.9447 | 0.9902 | |
| | 0.0419 | 5.0 | 3695 | 0.0216 | 0.9517 | 0.9698 | 0.9607 | 0.9927 | |
| | 0.0342 | 6.0 | 4434 | 0.0165 | 0.9603 | 0.9774 | 0.9688 | 0.9943 | |
| | 0.0232 | 7.0 | 5173 | 0.0115 | 0.9757 | 0.9817 | 0.9787 | 0.9967 | |
| | 0.0202 | 8.0 | 5912 | 0.0081 | 0.9793 | 0.9890 | 0.9841 | 0.9976 | |
| | 0.0139 | 9.0 | 6651 | 0.0057 | 0.9847 | 0.9889 | 0.9868 | 0.9982 | |
| | 0.0120 | 10.0 | 7390 | 0.0049 | 0.9854 | 0.9908 | 0.9881 | 0.9983 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.4.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
| |
