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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
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