---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wl
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: roberta-clinical-wl-es-finetuned-ner
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: wl
      type: wl
      config: WL
      split: train
      args: WL
    metrics:
    - name: Precision
      type: precision
      value: 0.6865079365079365
    - name: Recall
      type: recall
      value: 0.7355442176870748
    - name: F1
      type: f1
      value: 0.7101806239737274
    - name: Accuracy
      type: accuracy
      value: 0.8267950260730044
---

<!-- 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-clinical-wl-es-finetuned-ner

This model is a fine-tuned version of [plncmm/roberta-clinical-wl-es](https://huggingface.co/plncmm/roberta-clinical-wl-es) on the wl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6227
- Precision: 0.6865
- Recall: 0.7355
- F1: 0.7102
- Accuracy: 0.8268

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.028         | 1.0   | 500  | 0.6870          | 0.6558    | 0.6855 | 0.6703 | 0.8035   |
| 0.5923        | 2.0   | 1000 | 0.6248          | 0.6851    | 0.7235 | 0.7038 | 0.8244   |
| 0.4928        | 3.0   | 1500 | 0.6227          | 0.6865    | 0.7355 | 0.7102 | 0.8268   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2