readme: ---
  datasets:
  - squad_v2
  language: en
  license: mit
  pipeline_tag: question-answering
  tags:
  - electra
  - question-answering
  ---
  # Electra base model for QA (SQuAD 2.0)
  
  This model uses [electra-base](https://huggingface.co/google/electra-base-discriminator).
  
  ## Training Data
  The models have been trained on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
  
  It can be used for question answering task.
  
  ## Usage and Performance
  The trained model can be used like this:
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  
  # Load model & tokenizer
  electra_model = AutoModelForQuestionAnswering.from_pretrained('navteca/electra-base-squad2')
  electra_tokenizer = AutoTokenizer.from_pretrained('navteca/electra-base-squad2')
  
  # Get predictions
  nlp = pipeline('question-answering', model=electra_model, tokenizer=electra_tokenizer)
  
  result = nlp({
      'question': 'How many people live in Berlin?',
      'context': 'Berlin had a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.'
  })
  
  print(result)
  
  #{
  #  "answer": "3,520,031"
  #  "end": 36,
  #  "score": 0.99983448,
  #  "start": 27,
  #}
  ```
  