bert-base-uncased-finetuned-squad

This model is a fine-tuned version of bert-base-uncased on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available here. It achieves the following results on the evaluation set:

  • Loss: 1.0780

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. They are equal to the ones used to fine-tune distilbert-base-uncased for QA:

  • learning_rate: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 3

Training results

Training Loss Epoch Step Validation Loss
1.0706 1.0 5533 1.0250
0.7899 2.0 11066 1.0356
0.5991 3.0 16599 1.0780

Validation results

EM F1
80.3690 88.0110

Framework versions

  • Transformers 4.9.2
  • Pytorch 1.9.0+cu102
  • Datasets 1.11.0
  • Tokenizers 0.10.3
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Select AutoNLP in the “Train” menu to fine-tune this model automatically.

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