---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-clinical-plncmm-large-21
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-finetuned-ner-clinical-plncmm-large-21

This model is a fine-tuned version of [plncmm/beto-clinical-wl-es](https://huggingface.co/plncmm/beto-clinical-wl-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2547
- Precision: 0.7735
- Recall: 0.8321
- F1: 0.8017
- Accuracy: 0.9358

## 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: 3e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 400
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.236         | 1.0   | 1714 | 0.2390          | 0.7219    | 0.8106 | 0.7637 | 0.9257   |
| 0.156         | 2.0   | 3428 | 0.2498          | 0.7413    | 0.8271 | 0.7818 | 0.9306   |
| 0.096         | 3.0   | 5142 | 0.2547          | 0.7735    | 0.8321 | 0.8017 | 0.9358   |


### Framework versions

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