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
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall
0.6904	6.93	1040	0.6850	0.6332	0.6212	0.6526	0.6332

Framework versions

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