---
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: bert-base-spanish-wwm-uncased-finetuned-ner1
  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: 0.9483257314495495
    - name: Recall
      type: recall
      value: 0.9656754460492778
    - name: F1
      type: f1
      value: 0.9569219543681419
    - name: Accuracy
      type: accuracy
      value: 0.9766181574620958
---

<!-- 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-base-spanish-wwm-uncased-finetuned-ner

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: 0.1423
- Precision: 0.9483
- Recall: 0.9657
- F1: 0.9569
- Accuracy: 0.9766

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0179        | 1.0   | 612  | 0.1292          | 0.9547    | 0.9629 | 0.9588 | 0.9779   |
| 0.0133        | 2.0   | 1224 | 0.1574          | 0.9463    | 0.9684 | 0.9572 | 0.9766   |
| 0.0146        | 3.0   | 1836 | 0.1179          | 0.9500    | 0.9622 | 0.9561 | 0.9769   |
| 0.0238        | 4.0   | 2448 | 0.1388          | 0.9441    | 0.9677 | 0.9557 | 0.9759   |
| 0.0152        | 5.0   | 3060 | 0.1442          | 0.9430    | 0.9634 | 0.9531 | 0.9754   |
| 0.0155        | 6.0   | 3672 | 0.1401          | 0.9480    | 0.9641 | 0.9560 | 0.9760   |
| 0.0126        | 7.0   | 4284 | 0.1411          | 0.9468    | 0.9676 | 0.9571 | 0.9769   |
| 0.0131        | 8.0   | 4896 | 0.1427          | 0.9484    | 0.9657 | 0.9570 | 0.9767   |
| 0.0117        | 9.0   | 5508 | 0.1391          | 0.9485    | 0.9651 | 0.9567 | 0.9767   |
| 0.0116        | 10.0  | 6120 | 0.1423          | 0.9483    | 0.9657 | 0.9569 | 0.9766   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3