---
language:
- es
license: cc-by-nc-4.0
tags:
- generated_from_trainer
datasets:
- jpherrerap/competencia2
model-index:
- name: ner-roberta-es-clinical-trials-ner
  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. -->

# ner-roberta-es-clinical-trials-ner

This model is a fine-tuned version of [lcampillos/roberta-es-clinical-trials-ner](https://huggingface.co/lcampillos/roberta-es-clinical-trials-ner) on the jpherrerap/competencia2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2661
- Body Part Precision: 0.7124
- Body Part Recall: 0.8173
- Body Part F1: 0.7612
- Body Part Number: 197
- Disease Precision: 0.7712
- Disease Recall: 0.7697
- Disease F1: 0.7704
- Disease Number: 521
- Family Member Precision: 0.8462
- Family Member Recall: 0.8462
- Family Member F1: 0.8462
- Family Member Number: 13
- Medication Precision: 0.8378
- Medication Recall: 0.8378
- Medication F1: 0.8378
- Medication Number: 37
- Procedure Precision: 0.6510
- Procedure Recall: 0.7239
- Procedure F1: 0.6855
- Procedure Number: 134
- Overall Precision: 0.7418
- Overall Recall: 0.7772
- Overall F1: 0.7591
- Overall Accuracy: 0.9238

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 13
- 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 | Body Part Precision | Body Part Recall | Body Part F1 | Body Part Number | Disease Precision | Disease Recall | Disease F1 | Disease Number | Family Member Precision | Family Member Recall | Family Member F1 | Family Member Number | Medication Precision | Medication Recall | Medication F1 | Medication Number | Procedure Precision | Procedure Recall | Procedure F1 | Procedure Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:--------------:|:-----------------------:|:--------------------:|:----------------:|:--------------------:|:--------------------:|:-----------------:|:-------------:|:-----------------:|:-------------------:|:----------------:|:------------:|:----------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3329        | 1.0   | 502  | 0.2561          | 0.6830              | 0.7766           | 0.7268       | 197              | 0.7718            | 0.7658         | 0.7688     | 521            | 0.9231                  | 0.9231               | 0.9231           | 13                   | 0.75                 | 0.8108            | 0.7792        | 37                | 0.6218              | 0.7239           | 0.6690       | 134              | 0.7274            | 0.7661         | 0.7462     | 0.9219           |
| 0.1699        | 2.0   | 1004 | 0.2661          | 0.7124              | 0.8173           | 0.7612       | 197              | 0.7712            | 0.7697         | 0.7704     | 521            | 0.8462                  | 0.8462               | 0.8462           | 13                   | 0.8378               | 0.8378            | 0.8378        | 37                | 0.6510              | 0.7239           | 0.6855       | 134              | 0.7418            | 0.7772         | 0.7591     | 0.9238           |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3