| Blog post with more details as well as easy to use Google Colab link: https://towardsdatascience.com/high-quality-sentence-paraphraser-using-transformers-in-nlp-c33f4482856f | |
| !pip install transformers==4.10.2 | |
| !pip install sentencepiece==0.1.96 | |
| ``` | |
| from transformers import AutoTokenizer, AutoModelForSeq2SeqLM | |
| model = AutoModelForSeq2SeqLM.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") | |
| tokenizer = AutoTokenizer.from_pretrained("ramsrigouthamg/t5-large-paraphraser-diverse-high-quality") | |
| import torch | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| print ("device ",device) | |
| model = model.to(device) | |
| # Beam Search | |
| context = "Once, a group of frogs were roaming around the forest in search of water." | |
| text = "paraphrase: "+context + " </s>" | |
| encoding = tokenizer.encode_plus(text,max_length =128, padding=True, return_tensors="pt") | |
| input_ids,attention_mask = encoding["input_ids"].to(device), encoding["attention_mask"].to(device) | |
| model.eval() | |
| beam_outputs = model.generate( | |
| input_ids=input_ids,attention_mask=attention_mask, | |
| max_length=128, | |
| early_stopping=True, | |
| num_beams=15, | |
| num_return_sequences=3 | |
| ) | |
| print ("\n\n") | |
| print ("Original: ",context) | |
| for beam_output in beam_outputs: | |
| sent = tokenizer.decode(beam_output, skip_special_tokens=True,clean_up_tokenization_spaces=True) | |
| print (sent) | |
| ``` | |
| **Output from the above code** | |
| ``` | |
| Original: Once, a group of frogs were roaming around the forest in search of water. | |
| paraphrasedoutput: A herd of frogs were wandering around the woods in search of water. | |
| paraphrasedoutput: A herd of frogs was wandering around the woods in search of water. | |
| paraphrasedoutput: A herd of frogs were wandering around the forest in search of water at one time. | |
| ``` |