readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - squad
  model_index:
  - name: bert-base-uncased-finetuned-squad
    results:
    - task:
        name: Question Answering
        type: question-answering
      dataset:
        name: squad
        type: squad
        args: plain_text
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # bert-base-uncased-finetuned-squad
  
  This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the SQuAD1.1 dataset. It was trained through Transformers' example Colab notebook on Question Answering, available [here](https://colab.research.google.com/github/huggingface/notebooks/blob/master/examples/question_answering.ipynb).
  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](https://huggingface.co/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
  