---
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: 0.895
    - name: F1
      type: f1
      value: 0.8961058726378275
---

<!-- 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: 0.7264
- Accuracy: 0.895
- Balanced accuracy: 0.8746
- F1: 0.8961

## 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     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------------:|:------:|
| 0.001         | 1.0   | 25   | 0.7713          | 0.89     | 0.8807            | 0.8915 |
| 0.0069        | 2.0   | 50   | 0.7734          | 0.905    | 0.8906            | 0.9070 |
| 0.0019        | 3.0   | 75   | 0.8670          | 0.88     | 0.8749            | 0.8819 |
| 0.0012        | 4.0   | 100  | 0.7387          | 0.895    | 0.8806            | 0.8953 |
| 0.0002        | 5.0   | 125  | 0.7841          | 0.885    | 0.8649            | 0.8858 |
| 0.0002        | 6.0   | 150  | 0.7415          | 0.9      | 0.8753            | 0.9001 |
| 0.0002        | 7.0   | 175  | 0.7378          | 0.895    | 0.8719            | 0.8955 |
| 0.0002        | 8.0   | 200  | 0.7452          | 0.89     | 0.8711            | 0.8910 |
| 0.0002        | 9.0   | 225  | 0.7555          | 0.89     | 0.8787            | 0.8908 |
| 0.0001        | 10.0  | 250  | 0.7541          | 0.895    | 0.8822            | 0.8959 |
| 0.0001        | 11.0  | 275  | 0.7536          | 0.9      | 0.8857            | 0.9009 |
| 0.0001        | 12.0  | 300  | 0.7530          | 0.9      | 0.8857            | 0.9009 |
| 0.0001        | 13.0  | 325  | 0.7542          | 0.9      | 0.8857            | 0.9009 |
| 0.0001        | 14.0  | 350  | 0.7532          | 0.895    | 0.8746            | 0.8957 |
| 0.0002        | 15.0  | 375  | 0.8554          | 0.88     | 0.8424            | 0.8803 |
| 0.0001        | 16.0  | 400  | 0.7700          | 0.9      | 0.8867            | 0.9011 |
| 0.0001        | 17.0  | 425  | 0.7302          | 0.895    | 0.8746            | 0.8961 |
| 0.0001        | 18.0  | 450  | 0.7304          | 0.895    | 0.8746            | 0.8961 |
| 0.0001        | 19.0  | 475  | 0.7284          | 0.895    | 0.8746            | 0.8961 |
| 0.0001        | 20.0  | 500  | 0.7264          | 0.895    | 0.8746            | 0.8961 |


### Framework versions

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