T5-small for paraphrase generation

Google's T5 small fine-tuned on TaPaCo dataset for paraphrasing.

Model in Action 🚀

from transformers import T5ForConditionalGeneration, T5Tokenizer

tokenizer = T5Tokenizer.from_pretrained("hetpandya/t5-small-tapaco")
model = T5ForConditionalGeneration.from_pretrained("hetpandya/t5-small-tapaco")

def get_paraphrases(sentence, prefix="paraphrase: ", n_predictions=5, top_k=120, max_length=256,device="cpu"):
 text = prefix + sentence + " </s>"
 encoding = tokenizer.encode_plus(
 text, pad_to_max_length=True, return_tensors="pt"
 )
 input_ids, attention_masks = encoding["input_ids"].to(device), encoding[
 "attention_mask"
 ].to(device)

 model_output = model.generate(
 input_ids=input_ids,
 attention_mask=attention_masks,
 do_sample=True,
 max_length=max_length,
 top_k=top_k,
 top_p=0.98,
 early_stopping=True,
 num_return_sequences=n_predictions,
 )

 outputs = []
 for output in model_output:
 generated_sent = tokenizer.decode(
 output, skip_special_tokens=True, clean_up_tokenization_spaces=True
 )
 if (
 generated_sent.lower() != sentence.lower()
 and generated_sent not in outputs
 ):
 outputs.append(generated_sent)
 return outputs

paraphrases = get_paraphrases("The house will be cleaned by me every Saturday.")

for sent in paraphrases:
 print(sent)

Output

The house is cleaned every Saturday by me.
The house will be cleaned on Saturday.
I will clean the house every Saturday.
I get the house cleaned every Saturday.
I will clean this house every Saturday.

Model fine-tuning

Please find my guide on fine-tuning the model here:

https://towardsdatascience.com/training-t5-for-paraphrase-generation-ab3b5be151a2

Created by Het Pandya/@hetpandya | LinkedIn

Made with ♥ in India