---
tags:
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: bert-xnli-es-classifier
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: xnli
      type: xnli
      config: es
      split: validation
      args: es
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.827710843373494
---

<!-- 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-xnli-es-classifier

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 xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5109
- Accuracy: 0.8277

## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4401        | 1.0   | 6136  | 0.4733          | 0.8116   |
| 0.4245        | 2.0   | 12272 | 0.4667          | 0.8309   |
| 0.29          | 3.0   | 18408 | 0.5109          | 0.8277   |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3