---
license: apache-2.0
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
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9345
    - name: F1
      type: f1
      value: 0.9343606042371106
---

<!-- 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: 0.2145
- Accuracy: 0.9345
- F1: 0.9344

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1703        | 1.0   | 250  | 0.1791          | 0.932    | 0.9308 |
| 0.1183        | 2.0   | 500  | 0.1599          | 0.936    | 0.9358 |
| 0.103         | 3.0   | 750  | 0.1645          | 0.9345   | 0.9348 |
| 0.0827        | 4.0   | 1000 | 0.1698          | 0.9335   | 0.9326 |
| 0.0671        | 5.0   | 1250 | 0.1648          | 0.931    | 0.9308 |
| 0.0535        | 6.0   | 1500 | 0.1843          | 0.936    | 0.9354 |
| 0.0453        | 7.0   | 1750 | 0.2021          | 0.935    | 0.9350 |
| 0.0342        | 8.0   | 2000 | 0.2042          | 0.939    | 0.9390 |
| 0.0296        | 9.0   | 2250 | 0.2120          | 0.9345   | 0.9344 |
| 0.0239        | 10.0  | 2500 | 0.2145          | 0.9345   | 0.9344 |


### Framework versions

- Transformers 4.16.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2