---
license: apache-2.0
base_model: bert-base-uncased
tags:
- classification
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
model-index:
- name: clasificador-twitter-sentiment
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: test
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.929
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# clasificador-twitter-sentiment

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2242
- Accuracy: 0.929

## 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: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2641        | 1.0   | 2000 | 0.2359          | 0.9225   |
| 0.1416        | 2.0   | 4000 | 0.1932          | 0.9315   |
| 0.0976        | 3.0   | 6000 | 0.2242          | 0.929    |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2