get_paraphrased_sentences(model, tokenizer, sentence, num_beams=10, num_return_sequences=10)
get_paraphrased_sentences(model, tokenizer, "To paraphrase a source, you have to rewrite a passage without changing the meaning of the original text.", num_beams=10, num_return_sequences=10)
tokenizer = AutoTokenizer.from_pretrained("Vamsi/T5_Paraphrase_Paws")
model = AutoModelForSeq2SeqLM.from_pretrained("Vamsi/T5_Paraphrase_Paws")
get_paraphrased_sentences(model, tokenizer, "paraphrase: " + "One of the best ways to learn is to teach what you've already learned")
!pip install git+https://github.com/PrithivirajDamodaran/Parrot_Paraphraser.git
from parrot import Parrot
parrot = Parrot()
phrases = [
sentence,
"One of the best ways to learn is to teach what you've already learned",
"Paraphrasing is the process of coming up with someone else's ideas in your own words"
]
for phrase in phrases:
print("-"*100)
print("Input_phrase: ", phrase)
print("-"*100)
paraphrases = parrot.augment(input_phrase=phrase)
for paraphrase in paraphrases:
print(paraphrase)
import warnings
warnings.filterwarnings("ignore")
from parrot import Parrot
parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5", use_gpu=False)
phrases = ["Can you recommed some upscale restaurants in Rome?"]
for phrase in phrases:
print("-" * 100)
print(phrase)
print("-" * 100)
para_phrases = parrot.augment(input_phrase=phrase,
diversity_ranker="levenshtein",
do_diverse=False,
max_return_phrases=10,
max_length=32,
adequacy_threshold=0.99,
fluency_threshold=0.90)
for para_phrase in para_phrases:
print(para_phrases)