readme: ---
  language:
  - en
  license: mit
  tags:
  - generated_from_trainer
  datasets:
  - glue
  metrics:
  - matthews_correlation
  widget:
  - text: They represented seriously to the dean Mary as a genuine linguist.
  model-index:
  - name: deberta-v3-small
    results:
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: GLUE COLA
        type: glue
        args: cola
      metrics:
      - type: matthews_correlation
        value: 0.6333205721749096
        name: Matthews Correlation
    - task:
        type: text-classification
        name: Text Classification
      dataset:
        name: glue
        type: glue
        config: cola
        split: validation
      metrics:
      - type: accuracy
        value: 0.8494726749760306
        name: Accuracy
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjJjOTM0MTEzMzBlZWJlMWYwNzgzZmI3M2NiZWVjMDQ5ZDA1MWY0NGY3NjU1NTlmZWE3N2JjZWEzODE0ZTNkNSIsInZlcnNpb24iOjF9.Kt-3jnDTp3-Te5zMHVgG_5hpB5UMCkAMP7fmjx46QDWJfFHpyRgBlf-qz_fw5saFPAQ5G6QNq3bjEJ6mY2lhAw
      - type: precision
        value: 0.8455882352941176
        name: Precision
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiODAxMzNkZGEwNGNmYjk4NWRhZDk4OWE4MzA5Y2NiNjQyNTdkOWRmYjU0ZjY0YzQzYmE4ZmI3MjQ4OTk4OWIwNCIsInZlcnNpb24iOjF9.YBFnePtD5-HX15aST39xpPLroFYBgqEn5iLyVaClh62j0M7HQbB8aaGEbgaTIUIr-qz12gVfIQ7UZZIHxby_BQ
      - type: recall
        value: 0.957004160887656
        name: Recall
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjRjMTVhN2E4YjNlOWY2MWRhODZiM2FhZDVjNzYwMjIyNWUyYTMxMWFlZjkwNzVhYjNmMjQxYjk2MTFmMzYyYiIsInZlcnNpb24iOjF9.40GYlU9Do74Y_gLmbIKR2WM8okz5fm-QUwJAsoIyM1UtQ71lKd-FV5Yr9CdAh3fyQYa3SMYe6tm9OByNMMw_AA
      - type: auc
        value: 0.9167413271767129
        name: AUC
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYzVjYmMyZDkyMzM0ZTQ1MTk0ZmY4MWUwZmIxMGRlOWMyMjJmNDRiZGNkMGZlZDZmY2I5OWI2NDYzMGQ2YzhiNSIsInZlcnNpb24iOjF9.setZF_g9x-aknFXM1k0NxrOWMJcmpNi6z7QlyfL0i6fTPJOj6SbKJ1WQb3J1zTuabgx9cOc5xgHtBH3IA7fkDQ
      - type: f1
        value: 0.8978529603122967
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNmQ1NmNiMDhmNTU2Y2UxMzU0ODRmYmZmZTFkYjI4MzczMWUwYWQ4OTk2NGJlY2MzNmViYTA4MTRkODJhMTU1MyIsInZlcnNpb24iOjF9.GUIRxsYKgjYK63JS2rd9vCLHHmCiB4H68Xo5GxMaITfyzcUcdNc6l62njmQGrOoUidlTt1F7DzGP2Cu_Gz8HDg
      - type: loss
        value: 0.4050811529159546
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNjBjNjg0OTFjOTc5Mzc2MWQ1ZDIyYmM5MmIzZDVlY2JjYzBlZjMyN2IwOWU4YzNlMDcwZmM0NTMxYjExY2I0MiIsInZlcnNpb24iOjF9.xayLZc97iUW0zNqG65TiW9BXoqzV-tqF8g9qGCYQ1ZGuSDSjLlK7Y4og7-wqPEiME8JtNyVxl6-ZcWnF1t8cDg
  ---
  
  <!-- 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. -->
  
  # DeBERTa-v3-small fine-tuned on CoLA
  
  This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the GLUE COLA dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4051
  - Matthews Correlation: 0.6333
  
  ## Model description
  
  [DeBERTa](https://arxiv.org/abs/2006.03654) 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. 
  
  Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
  
  In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we replaced the MLM objective with the RTD(Replaced Token Detection) objective introduced by ELECTRA for pre-training, as well as some innovations to be introduced in our upcoming paper. Compared to DeBERTa-V2,  our V3 version significantly improves the model performance in downstream tasks.  You can find a simple introduction about the model from the appendix A11 in our original [paper](https://arxiv.org/abs/2006.03654),  but we will provide more details in a separate write-up.
  
  The DeBERTa V3 small model comes with 6 layers and a hidden size of 768. Its total parameter number is 143M since we use a vocabulary containing 128K tokens which introduce 98M parameters in the Embedding layer.  This model was trained using the 160GB data as DeBERTa V2.
  
  ## Intended uses & limitations
  
  More information needed
  
  ## Training and evaluation data
  
  
  The Corpus of Linguistic Acceptability (CoLA) in its full form consists of 10657 sentences from 23 linguistics publications, expertly annotated for acceptability (grammaticality) by their original authors. The public version provided here contains 9594 sentences belonging to training and development sets, and excludes 1063 sentences belonging to a held out test set.
  
  
  ## Training procedure
  
  ### Training hyperparameters
  
  The following hyperparameters were used during