results

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: 0.2147
  • Accuracy: 0.925
  • F1: 0.9251

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: 2

Training results

Training Loss Epoch Step Validation Loss Accuracy F1
0.8221 1.0 250 0.3106 0.9125 0.9102
0.2537 2.0 500 0.2147 0.925 0.9251

Framework versions

  • Transformers 4.11.3
  • Pytorch 1.9.1+cu102
  • Datasets 1.13.0
  • Tokenizers 0.10.3
Downloads last month
9
Hosted inference API
Text Classification
Examples
Examples
Mask token: [MASK]
This model can be loaded on the Inference API on-demand.
Evaluation results