---
base_model: finiteautomata/beto-sentiment-analysis
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: beto-sentiment-analysis-finetuned-detests-wandb24
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# beto-sentiment-analysis-finetuned-detests-wandb24

This model is a fine-tuned version of [finiteautomata/beto-sentiment-analysis](https://huggingface.co/finiteautomata/beto-sentiment-analysis) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6204
- Accuracy: 0.8674
- F1-score: 0.7993
- Precision: 0.8225
- Recall: 0.7822
- Auc: 0.7822

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-score | Precision | Recall | Auc    |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|:------:|
| 0.393         | 1.0   | 77   | 0.3365          | 0.8592   | 0.7633   | 0.8424    | 0.7287 | 0.7287 |
| 0.1947        | 2.0   | 154  | 0.3843          | 0.8396   | 0.7845   | 0.7716    | 0.8023 | 0.8023 |
| 0.0597        | 3.0   | 231  | 0.5486          | 0.8740   | 0.8046   | 0.8398    | 0.7814 | 0.7814 |
| 0.0028        | 4.0   | 308  | 0.6204          | 0.8674   | 0.7993   | 0.8225    | 0.7822 | 0.7822 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1