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)
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.
Please find my guide on fine-tuning the model here:
Created by Het Pandya/@hetpandya | LinkedIn
Made with ♥ in India
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