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

<!-- 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 dataset.
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: Adam 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 metric        ┃       DataLoader 0        ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│         test_acc          │    0.9520944952964783     │
│         test_loss         │     0.121663898229599     │
└───────────────────────────┴───────────────────────────┘

### Framework versions

- Transformers 4.33.1
- Pytorch lightning 2.0.8
- Tokenizers 0.13.3