metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - emotion
metrics:
  - accuracy
model-index:
  - name: autoevaluate/multi-class-classification
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: emotion
          type: emotion
          config: default
          split: test
        metrics:
          - type: accuracy
            value: 0.9185
            name: Accuracy
            verified: true
            verifyToken: >-
              eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZDNlY2I5ZTZkOTBmMTQwMjA0ZTFhYzE4NTdiZGIzYmNiOTkyMjBjMDY0MDJhNGY4ZTBiMGJiZjdhNzk3ODFkZCIsInZlcnNpb24iOjF9.JQbXPplJfzTCXHZrnbQLcSKZJrxvqYQh-BlpYlVMsWL6SSAKAlCb7Srqeoxr6u7byLm7QtufHYUKda7b1dKECw

multi-class-classification

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: 0.2009
Accuracy: 0.928

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: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2643	1.0	1000	0.2009	0.928

Framework versions

Transformers 4.19.2
Pytorch 1.11.0+cu113
Datasets 2.2.2
Tokenizers 0.12.1