transformers pytorch roberta text-classification classification generated_from_trainer dataset:tweet_eval license:mit model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- tweet_eval
license: mit
metrics:
- accuracy
tags:
- classification
- generated_from_trainer
model-index:
- name: clasificador-tweets-sentiment
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: tweet_eval
      type: tweet_eval
      config: hate
      split: test
      args: hate
    metrics:
    - type: accuracy
      value: 0.4986531986531986
      name: Accuracy
---

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# clasificador-tweets-sentiment

This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2588
- Accuracy: 0.4987

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4973        | 1.0   | 1125 | 1.2580          | 0.4502   |
| 0.4024        | 2.0   | 2250 | 1.9509          | 0.4832   |
| 0.3159        | 3.0   | 3375 | 2.2588          | 0.4987   |


### Framework versions

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