Is it possible to keep the capitalization?

[danielvoelk]

All my results are in lowercase. The API test on Huggingface has upper case. How do I enable that? :)

[PrithivirajDamodaran]

HF test runs inference on one model, the paraphrase model. With parrot it uses 3 models. Fx, Text is lowerred for adequacy and other models to give precise scores. You can't compare the outputs directly. Read the documentation to understand, how the framework works :)

Question is: why you need the capitalisation during augmentation ?

[danielvoelk]

Thanks for the quick reply. Ok, I understand.

What model tag do I have to use the paraphrase model and keep the capitalisation on? I didn't find this in the parrot documentation. Or is there another one I'm missing? :)

[PrithivirajDamodaran]

prithivida/parrot_paraphraser_on_T5

[danielvoelk]

I'm using that one, but I still get multiple answers and without capitalization. What do I have to adjust to get the paraphrase model, like on HF?

[PrithivirajDamodaran]

what problem are you trying to solve and why you need the caps? Did you see my question above ?

[danielvoelk]

Ah no, I missed that.

I just googled Python Paraphraser and found Parrot. I'm not solving any real problem, I just need a paraphraser to paraphrase some text in a python code with capitalization.

[PrithivirajDamodaran]

prithivida/parrot_paraphraser_on_T5

Ok, Use this model but not the parrot framework.

[danielvoelk]

How can I use it without the parrot framework?

[PrithivirajDamodaran]

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/parrot_paraphraser_on_T5")
tokenizer = AutoTokenizer.from_pretrained("rprithivida/parrot_paraphraser_on_T5")
model.to(device)
model.eval()

sentence = "What are the famous places we should not miss in Russia?"

encoding = tokenizer.encode("paraphrase: " + sentence, return_tensors="pt")
input_ids = encoding["input_ids"].to(device)

paraphrases = model.generate(
    input_ids=input_ids,
    max_length=128,
    early_stopping=True,
    num_beams=5,
    num_return_sequences=5

)

print ("Original phrase:", sentence)
for paraphrase in paraphrases:
    print(tokenizer.decode(paraphrase, skip_special_tokens=True,clean_up_tokenization_spaces=True))