readme: ---
  language: en
  datasets:
  - squad_v2
  license: cc-by-4.0
  model-index:
  - name: plm_qa
    results:
    - task:
        type: question-answering
        name: Question Answering
      dataset:
        name: squad_v2
        type: squad_v2
        config: squad_v2
        split: validation
      metrics:
      - name: Exact Match
        type: exact_match
        value: 0
        verified: false
      - name: F1
        type: f1
        value: 0
        verified: false
      - name: total
        type: total
        value: 11869
        verified: false
  ---
  
  # roberta-base for QA finetuned over community safety domain data
  
  We fine-tuned the roBERTa-based model (https://huggingface.co/deepset/roberta-base-squad2) over LiveSafe community safety dialogue data for event argument extraction with the objective of question-answering. 
  
  
  ### Using model in Transformers
  ```python
  from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
  model_name = "yirenl2/plm_qa"
  # a) Get predictions
  nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
  QA_input = {
      'question': 'What is the location of the incident?',
      'context': 'I was attacked by someone in front of the bus station.'
  }
  res = nlp(QA_input)
  # b) Load model & tokenizer
  model = AutoModelForQuestionAnswering.from_pretrained(model_name)
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  ```