metadata

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-detector-from-text
    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: 0.9345
          - name: F1
            type: f1
            value: 0.9346813045403889

distilbert-base-uncased-finetuned-emotion-detector-from-text

This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set:

Loss: 0.1628
Accuracy: 0.9345
F1: 0.9347

Model description

This model is trained on english tweets and can classify emotions in text files.

Intended uses & limitations

More information needed

Training and evaluation data

16,000 train samples 2,000 validation samples 2,000 test samples

Training procedure

Finetunning distilbert-base-uncased

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1
0.1038	1.0	250	0.1757	0.9325	0.9329
0.094	2.0	500	0.1628	0.9345	0.9347

Framework versions

Transformers 4.35.0
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1