readme: ---
  language: English
  task: extractive question answering
  datasets: SQuAD 2.0
  tags:
  - bert-base
  
  ---
  
  # Model Description
  
  This model is for English extractive question answering. It is based on the [bert-base-cased](https://huggingface.co/bert-base-uncased) model, and it is case-sensitive: it makes a difference between english and English.
  
  # Training data
  
  [English SQuAD v2.0](https://rajpurkar.github.io/SQuAD-explorer/) 
  
  # How to use
  
  You can use it directly from the [🤗 Transformers](https://github.com/huggingface/transformers) library with a pipeline:
  
  ``` python
  >>> from transformers.pipelines import pipeline
  >>> from transformers import AutoTokenizer, AutoModelForQuestionAnswering
  
  >>> tokenizer = AutoTokenizer.from_pretrained("zhufy/squad-en-bert-base")
  >>> model = AutoModelForQuestionAnswering.from_pretrained("zhufy/squad-en-bert-base")
  >>> nlp = pipeline("question-answering", model=model, tokenizer=tokenizer)
  
  >>> context = "A problem is regarded as inherently difficult if its 
                solution requires significant resources, whatever the 
                algorithm used. The theory formalizes this intuition, 
                by introducing mathematical models of computation to 
                study these problems and quantifying the amount of 
                resources needed to solve them, such as time and storage.
                Other complexity measures are also used, such as the 
                amount of communication (used in communication complexity),
                the number of gates in a circuit (used in circuit 
                complexity) and the number of processors (used in parallel
                computing). One of the roles of computational complexity
                theory is to determine the practical limits on what 
                computers can and cannot do."
                
  >>> question = "What are two basic primary resources used to 
                guage complexity?"
  
  >>> inputs = {"question": question, 
              "context":context }
              
  >>> nlp(inputs)
  
  {'score': 0.8589141368865967,
   'start': 305,
   'end': 321,
   'answer': 'time and storage'}
  
  ```