readme: ---
  language: en
  datasets:
  - squad_v2
  license: cc-by-4.0
  ---
  
  # roberta-base-squad2 for QA on COVID-19
  
  ## Overview
  **Language model:** deepset/roberta-base-squad2  
  **Language:** English  
  **Downstream-task:** Extractive QA  
  **Training data:** [SQuAD-style CORD-19 annotations from 23rd April](https://github.com/deepset-ai/COVID-QA/blob/master/data/question-answering/200423_covidQA.json)  
  **Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/01_basic_qa_pipeline)        
  **Infrastructure**: Tesla v100
  
  ## Hyperparameters
  ```
  batch_size = 24
  n_epochs = 3
  base_LM_model = "deepset/roberta-base-squad2"
  max_seq_len = 384
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  warmup_proportion = 0.1
  doc_stride = 128
  xval_folds = 5
  dev_split = 0
  no_ans_boost = -100
  ```
  ---
  license: cc-by-4.0
  ---
  
  ## Performance
  5-fold cross-validation on the data set led to the following results:  
  
  **Single EM-Scores:**   [0.222, 0.123, 0.234, 0.159, 0.158]  
  **Single F1-Scores:**   [0.476, 0.493, 0.599, 0.461, 0.465]  
  **Single top\\_3\\_recall Scores:**   [0.827, 0.776, 0.860, 0.771, 0.777]  
  **XVAL EM:**   0.17890995260663506  
  **XVAL f1:**   0.49925444207319924  
  **XVAL top\\_3\\_recall:**   0.8021327014218009
  
  This model is the model obtained from the **third** fold of the cross-validation.
  
  ## Usage
  
  ### In Haystack
  For doing QA at scale (i.e. many docs instead of single paragraph), you can load the model also in [haystack](https://github.com/deepset-ai/haystack/):
  ```python
  reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2-covid")
  # or 
  reader = TransformersReader(model="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2-covid")
  ```
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  
  model_name = "deepset/roberta-base-squad2-covid"
  
  # a) Get predictions
  nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
  QA_input = {
      'question': 'Why is model conversion important?',
      'context': 'The option to convert models between FARM and transformers gives freedom to the user and let people easily switch between frameworks.'
  }
  res = nlp(QA_input)
  
  # b) Load model & tokenizer
  model = AutoModelForQuestionAnswering.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  ```
  
  ## Authors
  **Branden Chan:** branden.chan@deepset.ai      
  **Timo Möller:** timo.moeller@deepset.ai    
  **Malte Pietsch:** malte.pietsch@deepset.ai      
  **Tanay Soni:** tanay.soni@deepset.ai    
  **Bogdan Kostić:** bogdan.kostic@deepset.ai      
  
  ## About us
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  [deepset](http://deepset.ai/) is the company behind the open-source NLP framework [Haystack](https://haystack.deepset.ai/) which is designed to help you build production ready NLP systems that use: Question answering, summarization, ranking etc.
  
  
  Some of our other work: 
  - [Distilled roberta-base-squad2 (aka "tinyroberta-squad2")]([https://huggingface.co/deepset/tinyroberta-squad2)
  - [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)
  
  ## Get in touch and join the Haystack community
  
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