---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- dataset
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: distilbert-base-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: 0.5701451278507257
    - name: F1
      type: f1
      value: 0.5651604812495131
    - name: Precision
      type: precision
      value: 0.5665667380442541
    - name: Recall
      type: recall
      value: 0.5641613027059359
---

<!-- 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. -->

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

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0580
- Accuracy: 0.5701
- F1: 0.5652
- Precision: 0.5666
- Recall: 0.5642

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.0643        | 1.0   | 543  | 1.0457          | 0.4423   | 0.2761 | 0.5104    | 0.3712 |
| 0.9754        | 2.0   | 1086 | 0.9700          | 0.5155   | 0.4574 | 0.5190    | 0.4712 |
| 0.8145        | 3.0   | 1629 | 0.9691          | 0.5556   | 0.5544 | 0.5616    | 0.5506 |
| 0.6318        | 4.0   | 2172 | 1.0580          | 0.5701   | 0.5652 | 0.5666    | 0.5642 |


### Framework versions

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