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--- |
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license: afl-3.0 |
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--- |
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# Question generation using T5 transformer trained on SQuAD |
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<h2> <i>Input format: context: "..." answer(optional): "..." </i></h2> |
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Import the pretrained model as well as tokenizer: |
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``` |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad_v2') |
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tokenizer = T5Tokenizer.from_pretrained('AbhilashDatta/T5_qgen-squad_v2') |
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``` |
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Then use the tokenizer to encode/decode and model to generate: |
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``` |
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input = "context: My name is Abhilash Datta. answer: Abhilash" |
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batch = tokenizer(input, padding='longest', max_length=512, return_tensors='pt') |
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inputs_batch = batch['input_ids'][0] |
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inputs_batch = torch.unsqueeze(inputs_batch, 0) |
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ques_id = model.generate(inputs_batch, max_length=100, early_stopping=True) |
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ques_batch = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in ques_id] |
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print(ques_batch) |
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``` |
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Output: |
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``` |
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['what is my name'] |
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``` |
