---
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: validation
      args: split
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9455
    - name: F1
      type: f1
      value: 0.9455317055605632
---

<!-- 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: 0.1789
- Accuracy: 0.9455
- F1: 0.9455

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.799         | 1.0   | 250  | 0.2667          | 0.92     | 0.9206 |
| 0.2043        | 2.0   | 500  | 0.1661          | 0.9345   | 0.9341 |
| 0.1359        | 3.0   | 750  | 0.1580          | 0.938    | 0.9387 |
| 0.1028        | 4.0   | 1000 | 0.1517          | 0.943    | 0.9435 |
| 0.0838        | 5.0   | 1250 | 0.1485          | 0.9385   | 0.9384 |
| 0.0691        | 6.0   | 1500 | 0.1514          | 0.94     | 0.9402 |
| 0.0578        | 7.0   | 1750 | 0.1854          | 0.9345   | 0.9338 |
| 0.0488        | 8.0   | 2000 | 0.1707          | 0.9405   | 0.9406 |
| 0.0414        | 9.0   | 2250 | 0.1822          | 0.944    | 0.9441 |
| 0.0355        | 10.0  | 2500 | 0.1789          | 0.9455   | 0.9455 |


### Framework versions

- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1