---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AdamCodd/emotion-balanced
metrics:
- accuracy
- f1
- recall
- precision
widget:
- text: Your actions were very caring.
  example_title: Test sentence
base_model: distilbert-base-uncased
model-index:
- name: distilbert-base-uncased-finetuned-emotion-balanced
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: emotion
      type: emotion
      args: default
    metrics:
    - type: accuracy
      value: 0.9521
      name: Accuracy
    - type: loss
      value: 0.1216
      name: Loss
    - type: f1
      value: 0.9520944952964783
      name: F1
---

<!-- 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-emotion

This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [emotion balanced dataset](https://huggingface.co/datasets/AdamCodd/emotion-balanced).
It achieves the following results on the evaluation set:
- Loss: 0.1216
- Accuracy: 0.9521

## Model description

This emotion classifier has been trained on 89_754 examples split into train, validation and test. Each label was perfectly balanced in each split.

## 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: 3e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 1270
- optimizer: AdamW with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 1
- weight_decay: 0.01

### Training results

              precision    recall  f1-score   support

     sadness     0.9882    0.9485    0.9679      1496
         joy     0.9956    0.9057    0.9485      1496
        love     0.9256    0.9980    0.9604      1496
       anger     0.9628    0.9519    0.9573      1496
        fear     0.9348    0.9098    0.9221      1496
    surprise     0.9160    0.9987    0.9555      1496

    accuracy                         0.9521      8976
    macro avg    0.9538    0.9521    0.9520      8976
    weighted avg 0.9538    0.9521    0.9520      8976
    
    test_acc:     0.9520944952964783
    test_loss:    0.121663898229599

### Framework versions

- Transformers 4.33.1
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3