---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- recall
model-index:
- name: distilbert-base-uncased-fine-tuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9255
    - name: F1
      type: f1
      value: 0.9254141326182981
    - name: Recall
      type: recall
      value: 0.9255
---

<!-- 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-fine-tuned-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2156
- Accuracy: 0.9255
- F1: 0.9254
- Recall: 0.9255

## Model description

This is the resuls of fine-tuning a distilbert-base-uncased trained on a NVIDIA GeForce GTX 1650, using a WSL with 7 gb of ram on windows 11.

The fine-tuning was obtained by following the book **Natural Language Processing with Tranformers: Building Languaje Applications with Hugging Fabe, By Lewis Tunstall, Leandro von Werra & Thomas Wolf**

Labels are associated to:
1.  *LABEL_0* is **sadness**
2.  *LABEL_1* is **joy**
3.  *LABEL_2* is **love**
4.  *LABEL_3* is **anger**
5.  *LABEL_4* is **fear**
6.  *LABEL_5* is **surprise**


## 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| 0.7838        | 1.0   | 250  | 0.2995          | 0.906    | 0.9039 | 0.906  |
| 0.237         | 2.0   | 500  | 0.2156          | 0.9255   | 0.9254 | 0.9255 |


### Framework versions

- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.13.2
- Tokenizers 0.12.1