How to create paraphrase api free with unlimited words and credits
Paraphrasing paragraphs will pass 100% plagiarism checker and create unique content with same meaning. There is lots of tool is available but doesn’t give free api with unlimited words. So here we shared a process to create paraphrase api free and unlimited.
The api works same as like quillbot api which recently has been closed. Lots of alternatives available on internet but not free. Even they doesn’t give unlimited access on paid plans.
Only 5 to 10 minutes takes to create own paraphrase tool. No server requires or anything else. Its completely free and private.
No credits requires and subscription. Create free account on hugging face and deploy the below codes file and your api is ready to paraphrase words. After integrate to your project and make it automatic.
Create paraphrase api free [Unlimited]
How to create paraphrasing api free like quillbot ?
1. Visit Huggingface.co and create new account on it. Open source AI community to deploy python projects.
2. Enter Email , Password and on next page username and complete the sign up process. Verify email by click on confirmation link.
3. Click on Right corner >> Profile icon and select “New Space” option. Enter space name , leave license field , select the space sdk as gradio, Private it and Create new space.
4. Space it created successfully. Now need to deploy application files. Select “Files & versions” tab on space page.
5. Add file >> Create new file >> Enter name as “requirements.txt“. Under “Edit” text field copy paste below code and commit changes.
transformers torch sentence-splitter SentencePiece yake
6. Create one more new file and enter name as “app.py“. Paste below code and commit changes.
import torch from transformers import PegasusForConditionalGeneration, PegasusTokenizer model_name = 'tuner007/pegasus_paraphrase' torch_device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = PegasusTokenizer.from_pretrained(model_name) model = PegasusForConditionalGeneration.from_pretrained(model_name).to(torch_device) def get_response(input_text,num_return_sequences): batch = tokenizer.prepare_seq2seq_batch([input_text],truncation=True,padding='longest',max_length=60, return_tensors="pt").to(torch_device) translated = model.generate(**batch,max_length=60,num_beams=10, num_return_sequences=num_return_sequences, temperature=1.5) tgt_text = tokenizer.batch_decode(translated, skip_special_tokens=True) return tgt_text from sentence_splitter import SentenceSplitter, split_text_into_sentences splitter = SentenceSplitter(language='en') def paraphraze(text): sentence_list = splitter.split(text) paraphrase =  for i in sentence_list: a = get_response(i,1) paraphrase.append(a) paraphrase2 = [' '.join(x) for x in paraphrase] paraphrase3 = [' '.join(x for x in paraphrase2) ] paraphrased_text = str(paraphrase3).strip('').strip("'") return paraphrased_text import gradio as gr def summarize(text): paraphrased_text = paraphraze(text) return paraphrased_text gr.Interface(fn=summarize, inputs=gr.inputs.Textbox(lines=7, placeholder="Enter text here"), outputs=[gr.outputs.Textbox(label="Paraphrased Text")],examples=[["This Api is the best quillbot api alternative with no words limit." ]]).launch(inline=False)
7. See logs for process. It takes upto 1 minute for deploy and build. Once it success application will go in running state. Click on “App” tab and test the application.
8. Below App. Click on “View api“. Copy the api url and send post request with text content. Read the docs for more details.
This is the complete details to create paraphrase api free without any limitations. Will work same as like quillbot and easy to integrate in your project.
Need more help then comment in below section.
2 thoughts on “Create paraphrase api free & no limit [Quillbot api alternative]”
Sup, I’ve implemented your method, but when I send POST request it gives me just HTML data that contains nothing more than just code of error 404 page. Please help me with it,
Thx in advance
Above method is for huggingface and there is no post method require in whole steps.