| --- |
| 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_procedure |
| 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/aphugj5r) |
| # RigoBERTa-Clinical_es_procedure |
|
|
| 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: 0.0013 |
| - Precision: 0.9965 |
| - Recall: 0.9982 |
| - F1: 0.9973 |
| - Accuracy: 0.9997 |
|
|
| ## 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 | |
| |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
| | 0.1097 | 1.0 | 739 | 0.0422 | 0.8979 | 0.8993 | 0.8986 | 0.9833 | |
| | 0.0658 | 2.0 | 1478 | 0.0285 | 0.9237 | 0.9368 | 0.9302 | 0.9891 | |
| | 0.0382 | 3.0 | 2217 | 0.0198 | 0.9278 | 0.9653 | 0.9462 | 0.9925 | |
| | 0.0313 | 4.0 | 2956 | 0.0123 | 0.9652 | 0.9734 | 0.9693 | 0.9955 | |
| | 0.0182 | 5.0 | 3695 | 0.0078 | 0.9786 | 0.9813 | 0.9799 | 0.9973 | |
| | 0.0148 | 6.0 | 4434 | 0.0048 | 0.9850 | 0.9914 | 0.9882 | 0.9984 | |
| | 0.0083 | 7.0 | 5173 | 0.0037 | 0.9907 | 0.9918 | 0.9913 | 0.9988 | |
| | 0.0069 | 8.0 | 5912 | 0.0020 | 0.9944 | 0.9964 | 0.9954 | 0.9994 | |
| | 0.0040 | 9.0 | 6651 | 0.0016 | 0.9961 | 0.9964 | 0.9962 | 0.9995 | |
| | 0.0031 | 10.0 | 7390 | 0.0013 | 0.9965 | 0.9982 | 0.9973 | 0.9997 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 5.4.0 |
| - Pytorch 2.10.0+cu128 |
| - Datasets 4.8.4 |
| - Tokenizers 0.22.2 |
| |
