T5_qgen-squad_v2

AbhilashDatta/T5_qgen-squad_v2

T5_qgen-squad_v2 is a machine learning model from AbhilashDatta. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as afl-3.0.

<h2 <iInput format: context: "..." answer(optional): "..." </i</h2

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At a glance

Library: transformers
License: afl-3.0
Model type: t5
Access: Public
Created: May 31, 2022
Updated: May 31, 2022
SHA: b3c4358d

Model card

From the Hugging Face model README

Question generation using T5 transformer trained on SQuAD

<h2> <i>Input format: context: "..." answer(optional): "..." </i></h2>

Import the pretrained model as well as tokenizer:

from transformers import T5ForConditionalGeneration, T5Tokenizer

model = T5ForConditionalGeneration.from_pretrained('AbhilashDatta/T5_qgen-squad_v2') 
tokenizer = T5Tokenizer.from_pretrained('AbhilashDatta/T5_qgen-squad_v2')

Then use the tokenizer to encode/decode and model to generate:

input = "context: My name is Abhilash Datta. answer: Abhilash"
batch = tokenizer(input, padding='longest', max_length=512, return_tensors='pt')
inputs_batch = batch['input_ids'][0]
inputs_batch = torch.unsqueeze(inputs_batch, 0)

ques_id = model.generate(inputs_batch, max_length=100, early_stopping=True)
ques_batch = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in ques_id]

print(ques_batch)

Output:

['what is my name']

Library: transformers
Type: t5
License: afl-3.0
Created: May 31, 2022
Updated: May 31, 2022