---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xlm-roberta-base-Mixed-aug_insert_vi
  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. -->

# xlm-roberta-base-Mixed-aug_insert_vi

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: 0.5221
- Accuracy: 0.79
- F1: 0.7737

## 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: 16
- eval_batch_size: 16
- 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 | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.9086        | 1.0   | 82   | 0.6185          | 0.79     | 0.7439 |
| 0.6351        | 2.0   | 164  | 0.4573          | 0.81     | 0.7858 |
| 0.4828        | 3.0   | 246  | 0.4669          | 0.79     | 0.7899 |
| 0.3772        | 4.0   | 328  | 0.4307          | 0.79     | 0.7676 |
| 0.2987        | 5.0   | 410  | 0.5221          | 0.79     | 0.7737 |


### Framework versions

- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3