---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_xlm_r_only_sw
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: xnli
      type: xnli
      config: sw
      split: train
      args: sw
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6903614457831325
---

<!-- 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_sw

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: 0.9651
- Accuracy: 0.6904

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8628        | 1.0   | 3068  | 0.7719          | 0.6659   |
| 0.7407        | 2.0   | 6136  | 0.7147          | 0.6944   |
| 0.6791        | 3.0   | 9204  | 0.7591          | 0.6940   |
| 0.6293        | 4.0   | 12272 | 0.7538          | 0.6968   |
| 0.5833        | 5.0   | 15340 | 0.7716          | 0.6988   |
| 0.5425        | 6.0   | 18408 | 0.8323          | 0.6956   |
| 0.5029        | 7.0   | 21476 | 0.8407          | 0.6948   |
| 0.4707        | 8.0   | 24544 | 0.8840          | 0.6908   |
| 0.4437        | 9.0   | 27612 | 0.9506          | 0.6880   |
| 0.4234        | 10.0  | 30680 | 0.9651          | 0.6904   |


### Framework versions

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