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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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```
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