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  # roberta-base for QA finetuned over community safety domain data
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  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.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # roberta-base for QA finetuned over community safety domain data
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  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.
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+ ### Using model in Transformers
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+ ```python
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+ from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
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+ model_name = "yirenl2/plm_qa"
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+ # a) Get predictions
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+ nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
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+ QA_input = {
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+ 'question': 'What is the location of the incident?',
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+ 'context': 'I was attacked by someone in front of the bus station.'
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+ }
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+ res = nlp(QA_input)
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+ # b) Load model & tokenizer
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+ model = AutoModelForQuestionAnswering.from_pretrained(model_name)
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ ```