---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_xlm_r_only_vi
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: xnli
      type: xnli
      config: vi
      split: train
      args: vi
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7562248995983936
---

<!-- 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_vi

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: 0.8671
- Accuracy: 0.7562

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6983        | 1.0   | 3068  | 0.6356          | 0.7486   |
| 0.5646        | 2.0   | 6136  | 0.5699          | 0.7731   |
| 0.5002        | 3.0   | 9204  | 0.5918          | 0.7622   |
| 0.4456        | 4.0   | 12272 | 0.6191          | 0.7711   |
| 0.3958        | 5.0   | 15340 | 0.6321          | 0.7755   |
| 0.3519        | 6.0   | 18408 | 0.7137          | 0.7586   |
| 0.3127        | 7.0   | 21476 | 0.7399          | 0.7614   |
| 0.2817        | 8.0   | 24544 | 0.7822          | 0.7622   |
| 0.256         | 9.0   | 27612 | 0.8316          | 0.7590   |
| 0.2362        | 10.0  | 30680 | 0.8671          | 0.7562   |


### Framework versions

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