How do I make a paraphrase generation using BERT/ GPT-2 - nlp

Question

I am trying hard to understand how to make a paraphrase generation using BERT/GPT-2. I cannot understand how do I make it. Could you please provide me with any resources where I will be able to make a paraphrase generation model?
"The input would be a sentence and the output would be a paraphrase of the sentence"

Answer 1

Here is my recipe for training a paraphraser:
Instead of BERT (encoder only) or GPT (decoder only) use a seq2seq model with both encoder and decoder, such as T5, BART, or Pegasus. I suggest using the multilingual T5 model that was pretrained for 101 languages. If you want to load embeddings for your own language (instead of using all 101), you can follow this recipe.
Find a corpus of paraphrases for your language and domain. For English, ParaNMT, PAWS, and QQP are good candidates. A corpus called Tapaco, extracted from Tatoeba, is a paraphrasing corpus that covers 73 languages, so it is a good starting point if you cannot find a paraphrase corpus for your language.
Fine-tune your model on this corpus. The code can be something like this:
import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer
# use here a backbone model of your choice, e.g. google/mt5-base
backbone_model = 'cointegrated/rut5-base-multitask'
model = T5ForConditionalGeneration.from_pretrained(backbone_model)
tokenizer = T5Tokenizer.from_pretrained(backbone_model)
model.cuda();
optimizer = torch.optim.Adam(params=[p for p in model.parameters() if p.requires_grad], lr=1e-5)
# todo: load the paraphrasing corpus and define the get_batch function
for i in range(100500):
xx, yy = get_batch(mult=mult)
x = tokenizer(xx, return_tensors='pt', padding=True).to(model.device)
y = tokenizer(yy, return_tensors='pt', padding=True).to(model.device)
# do not force the model to predict pad tokens
y.input_ids[y.input_ids==0] = -100
loss = model(
input_ids=x.input_ids,
attention_mask=x.attention_mask,
labels=y.input_ids,
decoder_attention_mask=y.attention_mask,
return_dict=True
).loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
model.save_pretrained('my_paraphraser')
tokenizer.save_pretrained('my_paraphraser')
A more complete version of this code can be found in this notebook.
After the training, the model can be used in the following way:
from transformers import pipeline
pipe = pipeline(task='text2text-generation', model='my_paraphraser')
print(pipe('Here is your text'))
# [{'generated_text': 'Here is the paraphrase or your text.'}]
If you want your paraphrases to be more diverse, you can control the generation procress using arguments like
print(pipe(
'Here is your text',
encoder_no_repeat_ngram_size=3, # make output different from input
do_sample=True, # randomize
num_beams=5, # try more options
max_length=128, # longer texts
))
Enjoy!

Answer 2

you can use T5 paraphrasing for generating paraphrases