---
library_name: transformers
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-BETO-UNCASED-BIOBERT
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.9467966573816156
    - name: Recall
      type: recall
      value: 0.9626168224299065
    - name: F1
      type: f1
      value: 0.9546412020783598
    - name: Accuracy
      type: accuracy
      value: 0.9762832612799123
---

<!-- 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-finetuning-BETO-UNCASED-BIOBERT

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1165
- Precision: 0.9468
- Recall: 0.9626
- F1: 0.9546
- Accuracy: 0.9763

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3684        | 1.0   | 612  | 0.1093          | 0.9356    | 0.9523 | 0.9438 | 0.9708   |
| 0.1191        | 2.0   | 1224 | 0.1026          | 0.9388    | 0.9674 | 0.9529 | 0.9746   |
| 0.0842        | 3.0   | 1836 | 0.1011          | 0.9394    | 0.9690 | 0.9539 | 0.9754   |
| 0.0657        | 4.0   | 2448 | 0.0986          | 0.9468    | 0.9682 | 0.9574 | 0.9779   |
| 0.0437        | 5.0   | 3060 | 0.0988          | 0.9499    | 0.9649 | 0.9573 | 0.9772   |
| 0.0395        | 6.0   | 3672 | 0.1070          | 0.9446    | 0.9645 | 0.9545 | 0.9757   |
| 0.0311        | 7.0   | 4284 | 0.1110          | 0.9459    | 0.9673 | 0.9565 | 0.9766   |
| 0.0302        | 8.0   | 4896 | 0.1141          | 0.9449    | 0.9635 | 0.9541 | 0.9763   |
| 0.023         | 9.0   | 5508 | 0.1133          | 0.9485    | 0.9641 | 0.9563 | 0.9770   |
| 0.0198        | 10.0  | 6120 | 0.1165          | 0.9468    | 0.9626 | 0.9546 | 0.9763   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0