---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: Vic_model2
  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. -->

# Vic_model2

This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2487
- Accuracy: 0.9657
- Precision: 0.9663
- Recall: 0.9657
- F1: 0.9654

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | Precision | Recall | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8139        | 1.0   | 1313  | 0.6269          | 0.83     | 0.8370    | 0.8300 | 0.8242 |
| 0.4671        | 2.0   | 2626  | 0.5028          | 0.8786   | 0.8837    | 0.8786 | 0.8757 |
| 0.343         | 3.0   | 3939  | 0.4058          | 0.8957   | 0.9038    | 0.8957 | 0.8965 |
| 0.222         | 4.0   | 5252  | 0.4109          | 0.9286   | 0.9295    | 0.9286 | 0.9274 |
| 0.1237        | 5.0   | 6565  | 0.3822          | 0.9357   | 0.9387    | 0.9357 | 0.9354 |
| 0.0629        | 6.0   | 7878  | 0.3639          | 0.9429   | 0.9459    | 0.9429 | 0.9433 |
| 0.0186        | 7.0   | 9191  | 0.2977          | 0.9557   | 0.9567    | 0.9557 | 0.9555 |
| 0.0104        | 8.0   | 10504 | 0.2487          | 0.9657   | 0.9663    | 0.9657 | 0.9654 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1