---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-clinical-plncmm-large-22
  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. -->

# bert-finetuned-ner-clinical-plncmm-large-22

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: 0.2380
- Precision: 0.7554
- Recall: 0.8271
- F1: 0.7896
- Accuracy: 0.9320

## 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: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5983        | 1.0   | 572  | 0.2405          | 0.7044    | 0.7964 | 0.7476 | 0.9227   |
| 0.1979        | 2.0   | 1144 | 0.2421          | 0.7296    | 0.8189 | 0.7717 | 0.9275   |
| 0.1406        | 3.0   | 1716 | 0.2380          | 0.7554    | 0.8271 | 0.7896 | 0.9320   |


### Framework versions

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