Wiebke/distilbert-base-uncased-finetuned-artificial

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distilbert-base-uncased-finetuned-artificial

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

• —eval_loss: 1.3241
• —eval_accuracy: 0.7816
• —eval_f1: 0.7801
• —eval_runtime: 87.2407
• —evalsamplesper_second: 57.313
• —evalstepsper_second: 3.588
• —epoch: 1.07
• —step: 8000

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
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 2

Framework versions

• —Transformers 4.37.1
• —Pytorch 2.1.0+cu121
• —Datasets 2.16.1
• —Tokenizers 0.15.1