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covid_qa_distillbert

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covid_qa_distillbert / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - covid_qa_deepset
widget:
  - text: What is COVID-19?
    context: >-
      Coronavirus disease 2019 (COVID-19) is a contagious disease caused by
      severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first
      known case was identified in Wuhan, China, in December 2019.[7] The
      disease has since spread worldwide, leading to an ongoing pandemic.
  - text: Where was COVID-19 first discovered?
    context: >-
      The first known infections from SARS-CoV-2 were discovered in Wuhan,
      China. The original source of viral transmission to humans remains
      unclear, as does whether the virus became pathogenic before or after the
      spillover event.
  - text: What is post-COVID 19 syndrome?
    context: >-
      Post-COVID conditions are a wide range of new, returning, or ongoing
      health problems people can experience four or more weeks after first being
      infected with the virus that causes COVID-19. Even people who did not have
      COVID-19 symptoms in the days or weeks after they were infected can have
      post-COVID conditions. These conditions can present as different types and
      combinations of health problems for different lengths of time. These
      post-COVID conditions may also be known as long COVID, long-haul COVID,
      post-acute COVID-19, long-term effects of COVID, or chronic COVID. CDC and
      experts around the world are working to learn more about short- and
      long-term health effects associated with COVID-19, who gets them, and why.
model-index:
  - name: distilbert-base-uncased-finetuned-squad

distilbert-base-uncased-finetuned-squad

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

  • Loss: 0.0976

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: 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
0.2502 1.0 3880 0.1824
0.2007 2.0 7760 0.1250
0.1338 3.0 11640 0.0976

Framework versions

  • Transformers 4.13.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.16.1
  • Tokenizers 0.10.3