readme: ---
  language: en
  datasets:
  - squad_v2
  license: cc-by-4.0
  ---
  
  # roberta-base for QA 
  
  > Note: this is a clone of [`roberta-base-squad2`](https://huggingface.co/deepset/roberta-base-squad2) for internal testing.
  
  This is the [roberta-base](https://huggingface.co/roberta-base) model, fine-tuned using the [SQuAD2.0](https://huggingface.co/datasets/squad_v2) dataset. It's been trained on question-answer pairs, including unanswerable questions, for the task of Question Answering. 
  
  
  ## Overview
  **Language model:** roberta-base  
  **Language:** English  
  **Downstream-task:** Extractive QA  
  **Training data:** SQuAD 2.0  
  **Eval data:** SQuAD 2.0  
  **Code:**  See [an example QA pipeline on Haystack](https://haystack.deepset.ai/tutorials/first-qa-system)  
  **Infrastructure**: 4x Tesla v100
  
  ## Hyperparameters
  
  ```
  batch_size = 96
  n_epochs = 2
  base_LM_model = "roberta-base"
  max_seq_len = 386
  learning_rate = 3e-5
  lr_schedule = LinearWarmup
  warmup_proportion = 0.2
  doc_stride=128
  max_query_length=64
  ``` 
  
  ## Using a distilled model instead
  Please note that we have also released a distilled version of this model called [deepset/tinyroberta-squad2](https://huggingface.co/deepset/tinyroberta-squad2). The distilled model has a comparable prediction quality and runs at twice the speed of the base model.
  
  ## Usage
  
  ### In Haystack
  Haystack is an NLP framework by deepset. You can use this model in a Haystack pipeline to do question answering at scale (over many documents). To load the model in [Haystack](https://github.com/deepset-ai/haystack/):
  ```python
  reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2")
  # or 
  reader = TransformersReader(model_name_or_path="deepset/roberta-base-squad2",tokenizer="deepset/roberta-base-squad2")
  ```
  For a complete example of ``roberta-base-squad2`` being used for  Question Answering, check out the [Tutorials in Haystack Documentation](https://haystack.deepset.ai/tutorials/first-qa-system)
  
  ### In Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  model_name = "deepset/roberta-base-squad2"
  
  # 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)
  ```
  
  ## Performance
  Evaluated on the SQuAD 2.0 dev set with the [official eval script](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/).
  
  ```
  "exact": 79.87029394424324,
  "f1": 82.91251169582613,
  
  "total": 11873,
  "HasAns_exact": 77.93522267206478,
  "HasAns_f1": 84.02838248389763,
  "HasAns_total": 5928,
  "NoAns_exact": 81.79983179142137,
  "NoAns_f1": 81.79983179142137,
  "NoAns_total": 5945
  ```
  
  Using the official [question answering notebook](https://github.com/huggingface/notebooks/blob/main/examples/question_answering.ipynb) from `transformers` yields:
  
  ```
  {'HasAns_exact': 77.93522267206478,
   'HasAns_f1': 83.93715663402219,
   'HasAns_total': 5928,
   'NoAns_exact': 81.90075693860386,
   'NoAns_f1': 81.90075693860386,
   'NoAns_total': 5945,
   'best_exact': 79.92082877116145,
   'best_exact_thresh': 0.0,
   'best_f1': 82.91749890730902,
   'best_f1_thresh': 0.0,
   'exact': 79.92082877116145,
   'f1': 82.91749890730917,
   'total': 11873}
  ```
    
  which is consistent with the officially reported results. Using the question answering `Evaluator` from `evaluate` gives:
   
  ```
   {'HasAns_exact': 77.91835357624831,
   'HasAns_f1': 84.07820736158186,
   'HasAns_total': 5928,
   'NoAns_exact': 81.91757779646763,
   'NoAns_f1': 81.91757779646763,
   'NoAns_total': 5945,
   'best_exact': 79.92082877116145,
   'best_exact_thresh': 0.996823787689209,
   'best_f1': 82.99634576260925,
   'best_f1_thresh': 0.996823787689209,
   'exact': 79.92082877116145,
   'f1': 82.9963457626089,
   'latency_in_seconds': 0.016523243643392558,
   'samples_per_second': 60.52080460605492,
   'total': 11873,
   'total_time_in_seconds': 196.18047177799986}
  ```
    
  which is also consistent with the officially reported results.
  
  
  ## 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 
  
  ## About us
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           <img alt="" src="https://huggingface.co/spaces/deepset/README/resolve/main/haystack-logo-colored.svg" class="w-40"/>
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