contradiction entailment neutral
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-finetuned-mnli
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: glue
      type: glue
      args: mnli
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8548888888888889
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# xlm-roberta-large-xnli-finetuned-mnli

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2542
- Accuracy: 0.8549

## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.7468        | 1.0   | 2250  | 0.8551          | 0.8348   |
| 0.567         | 2.0   | 4500  | 0.8935          | 0.8377   |
| 0.318         | 3.0   | 6750  | 0.9892          | 0.8492   |
| 0.1146        | 4.0   | 9000  | 1.2373          | 0.8446   |
| 0.0383        | 5.0   | 11250 | 1.2542          | 0.8549   |


### Framework versions

- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.0
- Tokenizers 0.12.1
