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# T5 for abstractive question-answering
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This is T5-base model fine-tuned for abstractive QA using text-to-text approach
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## Model training
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This model was trained on colab TPU with 35GB RAM for 2 epochs
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## Model in Action 🚀
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```
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from transformers import AutoModelWithLMHead, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("tuner007/t5_abs_qa")
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model = AutoModelWithLMHead.from_pretrained("tuner007/t5_abs_qa")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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def get_answer(question, context):
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input_text = "context: %s <question for context: %s </s>" % (context,question)
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features = tokenizer([input_text], return_tensors='pt')
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out = model.generate(input_ids=features['input_ids'].to(device), attention_mask=features['attention_mask'].to(device))
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return tokenizer.decode(out[0])
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```
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#### Example 1: Answer available
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```
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context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
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question = "What is Valhalla?"
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get_answer(question, context)
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# output: 'It is a hall of worship ruled by Odin.'
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```
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#### Example 2: Answer not available
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```
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context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin."
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question = "What is Asgard?"
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get_answer(question, context)
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# output: 'No answer available in context.'
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```
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> Created by Arpit Rajauria
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[](https://twitter.com/arpit_rajauria)
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