---
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-ner
  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: 0.9499079600602444
    - name: Recall
      type: recall
      value: 0.9645426224865478
    - name: F1
      type: f1
      value: 0.9571693552920016
    - name: Accuracy
      type: accuracy
      value: 0.977242282165256
---

<!-- 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: 0.1255
- Precision: 0.9499
- Recall: 0.9645
- F1: 0.9572
- Accuracy: 0.9772

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0412        | 1.0   | 612  | 0.1343          | 0.9401    | 0.9624 | 0.9512 | 0.9734   |
| 0.0632        | 2.0   | 1224 | 0.1082          | 0.9360    | 0.9654 | 0.9505 | 0.9746   |
| 0.0568        | 3.0   | 1836 | 0.1070          | 0.9469    | 0.9659 | 0.9563 | 0.9765   |
| 0.0486        | 4.0   | 2448 | 0.1104          | 0.9477    | 0.9669 | 0.9572 | 0.9771   |
| 0.0334        | 5.0   | 3060 | 0.1158          | 0.9425    | 0.9643 | 0.9533 | 0.9756   |
| 0.0311        | 6.0   | 3672 | 0.1238          | 0.9449    | 0.9644 | 0.9546 | 0.9753   |
| 0.0249        | 7.0   | 4284 | 0.1178          | 0.9473    | 0.9652 | 0.9561 | 0.9767   |
| 0.0245        | 8.0   | 4896 | 0.1244          | 0.9483    | 0.9656 | 0.9569 | 0.9772   |
| 0.0185        | 9.0   | 5508 | 0.1227          | 0.9492    | 0.9643 | 0.9567 | 0.9771   |
| 0.0165        | 10.0  | 6120 | 0.1255          | 0.9499    | 0.9645 | 0.9572 | 0.9772   |


### Framework versions

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