---
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: 0.9395
    - name: F1
      type: f1
      value: 0.9393105000343236
---

<!-- 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: 0.3355
- Accuracy: 0.9395
- F1: 0.9393

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0251        | 1.0   | 250  | 0.2793          | 0.9375   | 0.9377 |
| 0.0187        | 2.0   | 500  | 0.3246          | 0.931    | 0.9313 |
| 0.0147        | 3.0   | 750  | 0.3264          | 0.9365   | 0.9367 |
| 0.0116        | 4.0   | 1000 | 0.3252          | 0.938    | 0.9381 |
| 0.0097        | 5.0   | 1250 | 0.3036          | 0.9365   | 0.9366 |
| 0.0086        | 6.0   | 1500 | 0.3190          | 0.9395   | 0.9394 |
| 0.0063        | 7.0   | 1750 | 0.3181          | 0.939    | 0.9390 |
| 0.0042        | 8.0   | 2000 | 0.3493          | 0.938    | 0.9378 |
| 0.004         | 9.0   | 2250 | 0.3350          | 0.9405   | 0.9402 |
| 0.0025        | 10.0  | 2500 | 0.3355          | 0.9395   | 0.9393 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1