---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_xlm_r_only_es
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: xnli
      type: xnli
      config: es
      split: train
      args: es
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8096385542168675
---

<!-- 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. -->

# xnli_xlm_r_only_es

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6941
- Accuracy: 0.8096

## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.64          | 1.0   | 3068  | 0.5431          | 0.7799   |
| 0.4998        | 2.0   | 6136  | 0.4786          | 0.8129   |
| 0.4318        | 3.0   | 9204  | 0.5105          | 0.8129   |
| 0.3769        | 4.0   | 12272 | 0.5106          | 0.8124   |
| 0.3287        | 5.0   | 15340 | 0.5425          | 0.8133   |
| 0.2866        | 6.0   | 18408 | 0.5863          | 0.8088   |
| 0.2518        | 7.0   | 21476 | 0.6087          | 0.8092   |
| 0.2211        | 8.0   | 24544 | 0.6393          | 0.8096   |
| 0.1987        | 9.0   | 27612 | 0.6537          | 0.8084   |
| 0.182         | 10.0  | 30680 | 0.6941          | 0.8096   |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1