---
tags:
- generated_from_trainer
datasets:
- dataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: dccuchile-distilbert-base-spanish-uncased-finetuned-with-spanish-tweets-clf
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: dataset
      type: dataset
      config: 60-20-20
      split: dev
      args: 60-20-20
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.6620594333102972
    - name: F1
      type: f1
      value: 0.6612467747613665
    - name: Precision
      type: precision
      value: 0.6698857111668722
    - name: Recall
      type: recall
      value: 0.6593066578581652
---

<!-- 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. -->

# dccuchile-distilbert-base-spanish-uncased-finetuned-with-spanish-tweets-clf

This model is a fine-tuned version of [dccuchile/distilbert-base-spanish-uncased](https://huggingface.co/dccuchile/distilbert-base-spanish-uncased) on the dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5271
- Accuracy: 0.6621
- F1: 0.6612
- Precision: 0.6699
- Recall: 0.6593

## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.8515        | 1.0   | 543  | 0.7681          | 0.6565   | 0.6374 | 0.6491    | 0.6363 |
| 0.5505        | 2.0   | 1086 | 0.8494          | 0.6489   | 0.6431 | 0.6713    | 0.6357 |
| 0.3302        | 3.0   | 1629 | 1.2386          | 0.6662   | 0.6640 | 0.6667    | 0.6653 |
| 0.1675        | 4.0   | 2172 | 1.5271          | 0.6621   | 0.6612 | 0.6699    | 0.6593 |


### Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1
- Datasets 2.8.0
- Tokenizers 0.13.2