---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: fine-tuned-NLI-multilingual-with-xlm-roberta-large
  results: []
---

<!-- 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. -->

# fine-tuned-NLI-multilingual-with-xlm-roberta-large

This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5146
- Accuracy: 0.8579
- F1: 0.8583

## 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.4787        | 0.5   | 1574  | 0.4285          | 0.8364   | 0.8358 |
| 0.4418        | 1.0   | 3148  | 0.4040          | 0.8494   | 0.8496 |
| 0.3942        | 1.5   | 4722  | 0.3971          | 0.8514   | 0.8505 |
| 0.3722        | 2.0   | 6296  | 0.3835          | 0.8579   | 0.8581 |
| 0.3206        | 2.5   | 7870  | 0.4139          | 0.8587   | 0.8586 |
| 0.3229        | 3.0   | 9444  | 0.4033          | 0.8600   | 0.8602 |
| 0.2616        | 3.5   | 11018 | 0.4457          | 0.8585   | 0.8591 |
| 0.2862        | 4.0   | 12592 | 0.4319          | 0.8619   | 0.8617 |
| 0.2261        | 4.5   | 14166 | 0.4859          | 0.8562   | 0.8570 |
| 0.2215        | 5.0   | 15740 | 0.4728          | 0.8592   | 0.8599 |
| 0.1874        | 5.5   | 17314 | 0.5146          | 0.8579   | 0.8583 |


### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu117
- Datasets 2.2.0
- Tokenizers 0.13.2