contradiction entailment neutral
---
license: mit
tags:
- generated_from_trainer
datasets:
- swiss_judgment_prediction
metrics:
- accuracy
model-index:
- name: xlm-roberta-large-xnli-finetuned-mnli-SJP
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: swiss_judgment_prediction
      type: swiss_judgment_prediction
      args: all_languages
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.7957142857142857
---

<!-- 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-SJP

This model is a fine-tuned version of [joeddav/xlm-roberta-large-xnli](https://huggingface.co/joeddav/xlm-roberta-large-xnli) on the swiss_judgment_prediction dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3456
- Accuracy: 0.7957

## 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: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 5    | 1.8460          | 0.7956   |
| No log        | 2.0   | 10   | 1.3456          | 0.7957   |
| No log        | 3.0   | 15   | 1.2799          | 0.7957   |
| No log        | 4.0   | 20   | 1.2866          | 0.7957   |
| No log        | 5.0   | 25   | 1.3162          | 0.7956   |


### Framework versions

- Transformers 4.20.0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
