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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

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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
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Finetuned from

Dataset used to train ernlavr/distilbert-base-uncased-xsum-factuality