---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr-vi-nli
  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. -->

# xlmr-vi-nli

This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2354
- Accuracy: 0.9796
- F1 Macro: 0.9796

## 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: 8
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|
| 0.2665        | 0.9994 | 882  | 0.2531          | 0.9745   | 0.9745   |
| 0.2312        | 2.0    | 1765 | 0.2445          | 0.9756   | 0.9756   |
| 0.1965        | 2.9994 | 2647 | 0.2278          | 0.9807   | 0.9808   |
| 0.1829        | 3.9977 | 3528 | 0.2312          | 0.9807   | 0.9807   |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.6.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1