readme: ---
  language: 
    - en
  tags:
  - QA
  - Question Answering
  - SQuAD
  license: "mit"
  datasets:
  - squad
  metrics:
  - squad
  
  model-index:
  - name: distilroberta-base
    results:
    - task:
        type: question-answering             # Required. Example: automatic-speech-recognition
        name: Question Answering             # Optional. Example: Speech Recognition
      dataset:
        type: squad          # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
        name: SQuAD          # Required. A pretty name for the dataset. Example: Common Voice (French)
        split: validation        # Optional. Example: test
       
      metrics:
        - type: squad         # Required. Example: wer. Use metric id from https://hf.co/metrics
          value: 76.37653736991486       # Required. Example: 20.90
          name: SQuAD EM         # Optional. Example: Test WER
          config: exact_match     # Optional. The name of the metric configuration used in `load_metric()`. Example: bleurt-large-512 in `load_metric("bleurt", "bleurt-large-512")`. See the `datasets` docs for more info: https://huggingface.co/docs/datasets/v2.1.0/en/loading#load-configurations
        - type: squad         # Required. Example: wer. Use metric id from https://hf.co/metrics
          value: 84.5528918750732       # Required. Example: 20.90
          name: SQuAD F1         # Optional. Example: Test WER
          config: F1
         
  ---
  
  distilroberta-base fined-tuned on SQuAD (https://huggingface.co/datasets/squad)
  
  Hyperparameters:
  - epochs: 1
  - lr: 1e-5
  - train batch sie: 16
  - optimizer: adamW
  - lr_scheduler: linear
  - num warming steps: 0
  - max_length: 512
  
  Results on the dev set:
  - 'exact_match': 76.37653736991486
  - 'f1': 84.5528918750732
  
  
  It took 1h 20 min to train on Colab.