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README.md
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Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset).
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Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabraham/Covid-QA)
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Covid 19 question answering data obtained from [covid_qa_deepset](https://huggingface.co/datasets/covid_qa_deepset).
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Repository for the fine tuning, inference and evaluation scripts can be found [here](https://github.com/abhijithneilabraham/Covid-QA).
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```
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import torch
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering
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tokenizer = AutoTokenizer.from_pretrained("abhijithneilabraham/longformer_covid_qa")
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model = AutoModelForQuestionAnswering.from_pretrained("abhijithneilabraham/longformer_covid_qa")
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text = "Huggingface has democratized NLP. Huge thanks to Huggingface for this."
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question = "What has Huggingface done ?"
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encoding = tokenizer(question, text, return_tensors="pt")
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input_ids = encoding["input_ids"]
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# default is local attention everywhere
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# the forward method will automatically set global attention on question tokens
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attention_mask = encoding["attention_mask"]
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start_scores, end_scores = model(input_ids, attention_mask=attention_mask)
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all_tokens = tokenizer.convert_ids_to_tokens(input_ids[0].tolist())
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answer_tokens = all_tokens[torch.argmax(start_scores) :torch.argmax(end_scores)+1]
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answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
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# output => democratized NLP
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```
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