dream_classifier / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: dream_classifier
    results: []

dream_classifier

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6082
  • Accuracy: 0.8661

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: 20

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 17 0.6040 0.8125
No log 2.0 34 0.4455 0.8482
No log 3.0 51 0.3701 0.875
No log 4.0 68 0.4277 0.8304
No log 5.0 85 0.4477 0.8393
No log 6.0 102 0.3747 0.8929
No log 7.0 119 0.4383 0.9018
No log 8.0 136 0.5102 0.875
No log 9.0 153 0.5384 0.8661
No log 10.0 170 0.6888 0.8571
No log 11.0 187 0.5788 0.8661
No log 12.0 204 0.5933 0.8571
No log 13.0 221 0.6214 0.8661
No log 14.0 238 0.6246 0.8661
No log 15.0 255 0.6033 0.8661
No log 16.0 272 0.6066 0.8661
No log 17.0 289 0.6092 0.8661
No log 18.0 306 0.6079 0.8661
No log 19.0 323 0.6080 0.8661
No log 20.0 340 0.6082 0.8661

Framework versions

  • Transformers 4.30.2
  • Pytorch 2.0.1+cu118
  • Datasets 2.13.0
  • Tokenizers 0.13.3