ernlavr/distilbert-base-uncased-xsum-factuality

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Distilbert-base-uncased-xsum-factuality

This model is a fine-tuned version of distilbert-base-uncased on the XSum-Factuality dataset. You can view more implementation details as part of this GitHub repository. It achieves the following results on the evaluation set:

• —Loss: 0.6850
• —Accuracy: 0.6332
• —F1: 0.6212
• —Precision: 0.6526
• —Recall: 0.6332

Weights and Biases Documentation

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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: 1e-06
• —trainbatchsize: 2
• —evalbatchsize: 2
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —lrschedulerwarmup_ratio: 0.1
• —num_epochs: 7

Training results

Framework versions

• —Transformers 4.35.0
• —Pytorch 2.0.1
• —Datasets 2.14.6
• —Tokenizers 0.14.1