---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: emotion
      type: emotion
      config: split
      split: train[:2000]
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.89
    - name: F1
      type: f1
      value: 0.8909727258350819
---

<!-- 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-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: 0.6248
- Accuracy: 0.89
- Balanced accuracy: 0.8764
- F1: 0.8910

## 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: 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: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Balanced accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
| 0.0269        | 1.0   | 25   | 0.4880          | 0.905    | 0.8890            | 0.9058 |
| 0.0204        | 2.0   | 50   | 0.5177          | 0.89     | 0.8934            | 0.8896 |
| 0.009         | 3.0   | 75   | 0.4983          | 0.89     | 0.8787            | 0.8911 |
| 0.0089        | 4.0   | 100  | 0.5681          | 0.895    | 0.8724            | 0.8947 |
| 0.0048        | 5.0   | 125  | 0.5800          | 0.88     | 0.8662            | 0.8819 |
| 0.0023        | 6.0   | 150  | 0.5706          | 0.89     | 0.8959            | 0.8917 |
| 0.0035        | 7.0   | 175  | 0.6086          | 0.895    | 0.8760            | 0.8955 |
| 0.006         | 8.0   | 200  | 0.6522          | 0.88     | 0.9011            | 0.8811 |
| 0.0017        | 9.0   | 225  | 0.5806          | 0.89     | 0.8715            | 0.8907 |
| 0.0014        | 10.0  | 250  | 0.5809          | 0.885    | 0.9001            | 0.8868 |
| 0.0011        | 11.0  | 275  | 0.5942          | 0.885    | 0.8729            | 0.8864 |
| 0.001         | 12.0  | 300  | 0.5997          | 0.895    | 0.8826            | 0.8963 |
| 0.0009        | 13.0  | 325  | 0.6006          | 0.89     | 0.8791            | 0.8912 |
| 0.001         | 14.0  | 350  | 0.6135          | 0.885    | 0.9013            | 0.8857 |
| 0.0009        | 15.0  | 375  | 0.6199          | 0.885    | 0.8740            | 0.8858 |
| 0.0008        | 16.0  | 400  | 0.6257          | 0.885    | 0.8740            | 0.8858 |
| 0.0007        | 17.0  | 425  | 0.6254          | 0.885    | 0.8740            | 0.8858 |
| 0.0007        | 18.0  | 450  | 0.6273          | 0.885    | 0.8740            | 0.8858 |
| 0.0007        | 19.0  | 475  | 0.6248          | 0.885    | 0.8740            | 0.8858 |
| 0.0007        | 20.0  | 500  | 0.6248          | 0.89     | 0.8764            | 0.8910 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2