---
license: mit
tags:
- text-classification
- generated_from_trainer
datasets:
- xnli
metrics:
- accuracy
model-index:
- name: xnli_xlm_r_only_ur
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: xnli
      type: xnli
      config: ur
      split: train
      args: ur
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6526104417670683
---

<!-- 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_ur

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: 0.8165
- Accuracy: 0.6526

## 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: 1.5e-05
- train_batch_size: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0253        | 1.0   | 2046  | 0.8330          | 0.6382   |
| 0.9659        | 2.0   | 4092  | 0.8105          | 0.6530   |
| 0.9445        | 3.0   | 6138  | 0.7978          | 0.6558   |
| 0.9254        | 4.0   | 8184  | 0.7791          | 0.6594   |
| 0.9075        | 5.0   | 10230 | 0.7792          | 0.6614   |
| 0.8892        | 6.0   | 12276 | 0.7812          | 0.6554   |
| 0.8728        | 7.0   | 14322 | 0.7762          | 0.6538   |
| 0.8565        | 8.0   | 16368 | 0.8019          | 0.6494   |
| 0.8427        | 9.0   | 18414 | 0.8067          | 0.6558   |
| 0.8332        | 10.0  | 20460 | 0.8165          | 0.6526   |


### Framework versions

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