asemane/results

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results

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

• —Loss: 1.1484
• —Accuracy: 0.5890
• —F1: 0.5479
• —Precision: 0.5381
• —Recall: 0.5890

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: 5e-05
• —trainbatchsize: 16
• —evalbatchsize: 16
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_steps: 500
• —num_epochs: 5

Training results

Framework versions

• —Transformers 4.41.2
• —Pytorch 2.1.2
• —Datasets 2.19.2
• —Tokenizers 0.19.1