---
license: apache-2.0
tags:
- classification
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
model-index:
- name: clasificador-glue
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: irony
      split: test
      args: irony
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6836734693877551
---

<!-- 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. -->

# clasificador-glue

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3017
- Accuracy: 0.6837

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 358  | 0.8826          | 0.6084   |
| 0.6268        | 2.0   | 716  | 0.6036          | 0.7079   |
| 0.3358        | 3.0   | 1074 | 1.3017          | 0.6837   |


### Framework versions

- Transformers 4.30.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3