readme: ---
  language: en
  license: cc-by-4.0
  tags:
  - question-answering
  datasets:
  - squad_v2
  metrics:
  - f1
  - exact
  widget:
  - context: DeBERTa improves the BERT and RoBERTa models using disentangled attention
      and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa
      on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further
      improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient
      Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly
      improves the model performance on downstream tasks. You can find more technique
      details about the new model from our paper. Please check the official repository
      for more implementation details and updates.
    example_title: DeBERTa v3 Q1
    text: How is DeBERTa version 3 different than previous ones?
  - context: DeBERTa improves the BERT and RoBERTa models using disentangled attention
      and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa
      on a majority of NLU tasks with 80GB training data. In DeBERTa V3, we further
      improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient
      Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly
      improves the model performance on downstream tasks. You can find more technique
      details about the new model from our paper. Please check the official repository
      for more implementation details and updates.
    example_title: DeBERTa v3 Q2
    text: Where do I go to see new info about DeBERTa?
  model-index:
  - name: DeBERTa v3 xsmall squad2
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: SQuAD2.0
        type: question-answering
      metrics:
      - type: f1
        value: 81.5
        name: f1
      - type: exact
        value: 78.3
        name: exact
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - type: exact_match
        value: 78.5341
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZTk0ZGQ1YjU1YmQ5NTc2M2RmNjg2OGViYjcyODZkOTc1MDBkNmI5MDc0MzEyMzZmNDg3Yzc4ZTA3ZjAwM2M5ZiIsInZlcnNpb24iOjF9.ewKF-UetUoxKDeXgnM6vqy8nBC9c3qh7dLZhdQlgSxPut3LjAhpCh2fJGir-OVcfzWzxsPhcZQEpdnxR8oZnAA
      - type: f1
        value: 81.6408
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTQwZDdjY2ZlOGVhM2E5NGM3OGNkNTk2NWFkYTg1Y2Q0YWFlYWJmMGIyZWM5ZjMyYTYyODUzMDA0NWU0ZGVkZCIsInZlcnNpb24iOjF9.BHJNhS1YisUIkjcpIMdwXurTewak9dkkpGXC2vHvUB4qUEuk_p3V-orhmeFyTxzLaWRwrZVGVz-NSfqFr4n1Ag
      - type: total
        value: 11870
        name: total
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzNiZDQ3MDAyNzljMDI4NTRlYzZiZjE4ODJhZDhmZWE2ZjcwNjg2ZWJmNjUyMTUzZDk4ODNjNDExYTk1YWNlOCIsInZlcnNpb24iOjF9.3BlfmMvbV86Ua39ToqnMmgpGS0ZTew0UFFYWGyTkS3u7jaAXCfYkFkNJXw806f2uFFkKr1hqlzzKfivV0wUjCg
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad
        type: squad
        config: plain_text
        split: validation
      metrics:
      - type: exact_match
        value: 84.1741
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYTA0MDVlYWI5NzdiNjllM2NmZTYwYmQ5YzE0ODgwOTA3MWZjZDkxNDFmZDM1OTQzMzgwNWI4NDc5NThhM2VhZSIsInZlcnNpb24iOjF9.lc2nUBxSu2_0_a5lyVsV51UAmkE8WHDTwGHvt3n9zvCbcJ1ylOg2xovF0_j0hZS16lv1DEw5XV8EW_ZS7mfvBg
      - type: f1
        value: 91.0771
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODQxMjkxOWJlZTc2MmE5YzVmMjNhOTkwNDdiMDBhNWUwMDU3MDI1MmJiNDY4MjczYjIwM2U1NDhlYmZlZWQwMSIsInZlcnNpb24iOjF9.x_axHiBX5d3UIi1UbJT3kVbdX4kX9XFLQSg-l16-AAK9tiyutT-yaYJOi8LSb2lR4677tJpf3itu4eriJRU2Cg
  ---
  
  
  
  # DeBERTa v3 xsmall SQuAD 2.0
  
  [Microsoft reports that this model can get 84.8/82.0](https://huggingface.co/microsoft/deberta-v3-xsmall#fine-tuning-on-nlu-tasks) on f1/em on the dev set. 
  
  I got 81.5/78.3 but I only did one run and I didn't use the official squad2 evaluation script. I will do some more runs and show the results on the official script soon.
  