readme: ---
  language: en
  license: mit
  tags:
  - exbert
  datasets:
  - squad_v2
  thumbnail: https://thumb.tildacdn.com/tild3433-3637-4830-a533-353833613061/-/resize/720x/-/format/webp/germanquad.jpg
  model-index:
  - name: deepset/tinybert-6l-768d-squad2
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - type: exact_match
        value: 73.8248
        name: Exact Match
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMGFmZmFiN2E5ODZkOTkyMjQ1NTUzMmQwMjc0M2RlYzVlNmM4YTFlNzA4YzIwY2JkY2EyNDg2ZTY3OTdjZTVlZiIsInZlcnNpb24iOjF9.ZZ6c2OI3lzeNhuSWTh28j00zk-sPrqkTvdVBZv2wJc1D4YnR-xOj72haybT6MV_xeYqTg3-x9L8PsWSS20NaDw
      - type: f1
        value: 77.1684
        name: F1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzAxMDk1YzI5ZjA2N2ZmMzAxNjgxYzJiNzAzYmI1ZWU5ZDRmYWY3OWJmMjlmNDcyMGE0YWY5NjNhZTk4YWY5ZSIsInZlcnNpb24iOjF9.rF3raNGUSYv5D2xzWLZztD99vwDKvWb22LG32RomrDGP6XKTbCVqZzAw5UFw93jKb0VoLApbQQ-AOGxLj3U_Cg
  ---
  
  ## Overview
  **Language model:** deepset/tinybert-6L-768D-squad2   
  **Language:** English  
  **Training data:** SQuAD 2.0 training set x 20 augmented + SQuAD 2.0 training set without augmentation  
  **Eval data:** SQuAD 2.0 dev set  
  **Infrastructure**: 1x V100 GPU  
  **Published**: Dec 8th, 2021
  
  ## Details
  - haystack's intermediate layer and prediction layer distillation features were used for training (based on [TinyBERT](https://arxiv.org/pdf/1909.10351.pdf)). deepset/bert-base-uncased-squad2 was used as the teacher model and huawei-noah/TinyBERT_General_6L_768D was used as the student model.
  
  ## Hyperparameters
  ### Intermediate layer distillation
  ```
  batch_size = 26
  n_epochs = 5
  max_seq_len = 384
  learning_rate = 5e-5
  lr_schedule = LinearWarmup
  embeds_dropout_prob = 0.1
  temperature = 1
  ```
  ### Prediction layer distillation 
  ```
  batch_size = 26
  n_epochs = 5
  max_seq_len = 384
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  embeds_dropout_prob = 0.1
  temperature = 1
  distillation_loss_weight = 1.0
  ```
  ## Performance
  ```
  "exact": 71.87736882001179
  "f1": 76.36111895973675
  ```
  
  ## Authors
  - Timo Möller: `timo.moeller [at] deepset.ai`
  - Julian Risch: `julian.risch [at] deepset.ai`
  - Malte Pietsch: `malte.pietsch [at] deepset.ai`
  - Michel Bartels: `michel.bartels [at] deepset.ai`
  ## About us
  ![deepset logo](https://workablehr.s3.amazonaws.com/uploads/account/logo/476306/logo)
  We bring NLP to the industry via open source!  
  Our focus: Industry specific language models & large scale QA systems.  
    
  Some of our work: 
  - [German BERT (aka "bert-base-german-cased")](https://deepset.ai/german-bert)
  - [GermanQuAD and GermanDPR datasets and models (aka "gelectra-base-germanquad", "gbert-base-germandpr")](https://deepset.ai/germanquad)
  - [FARM](https://github.com/deepset-ai/FARM)
  - [Haystack](https://github.com/deepset-ai/haystack/)
  
  Get in touch:
  [Twitter](https://twitter.com/deepset_ai) | [LinkedIn](https://www.linkedin.com/company/deepset-ai/) | [Discord](https://haystack.deepset.ai/community/join) | [GitHub Discussions](https://github.com/deepset-ai/haystack/discussions) | [Website](https://deepset.ai)
  
  By the way: [we're hiring!](http://www.deepset.ai/jobs)