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bert-base-spanish-wwm-cased-t1

Token Classification
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bert-base-spanish-wwm-cased-t1
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---
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-spanish-wwm-cased-t1
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - name: Precision
      type: precision
      value: 0.7979376919701624
    - name: Recall
      type: recall
      value: 0.8357077205882353
    - name: F1
      type: f1
      value: 0.8163860830527497
    - name: Accuracy
      type: accuracy
      value: 0.9686647656831143
---

<!-- 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-cased-t1

This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1173
- Precision: 0.7979
- Recall: 0.8357
- F1: 0.8164
- Accuracy: 0.9687

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

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0773        | 1.0   | 1041 | 0.1173          | 0.7979    | 0.8357 | 0.8164 | 0.9687   |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2