How to Paraphrase Text using Transformers in Python

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Paraphrasing is the process of coming up with someone else's ideas in your own words. To paraphrase a text, you have to rewrite it without changing its meaning.

In this tutorial, we will explore different pre-trained transformer models for automatically paraphrasing text using the Huggingface transformers library in Python.

Note that if you want to just paraphrase your text, then there are online tools for that, such as the QuestGenius text paraphraser .

To get started, let's install the required libraries first:

Importing everything from transformers library:

Pegasus Transformer

In this section, we'll use the Pegasus transformer architecture model that was fine-tuned for paraphrasing instead of summarization. To instantiate the model, we need to use PegasusForConditionalGeneration as it's a form of text generation :

Next, let's make a general function that takes a model, its tokenizer, the target sentence and returns the paraphrased text:

We also add the possibility of generating multiple paraphrased sentences by passing num_return_sequences to the model.generate() method.

We also set num_beams so we generate the paraphrasing using beam search. Setting it to 5 will allow the model to look ahead for five possible words to keep the most likely hypothesis at each time step and choose the one that has the overall highest probability.

I highly suggest you check this blog post to learn more about the parameters of the model.generate()  method.

Let's use the function now:

We set num_beams to 10 and prompt the model to generate ten different sentences; here is the output:

Outstanding results! Most of the generations are accurate and can be used. You can try different sentences from your mind and see the results yourself.

You can check the  model card here .

T5 Transformer

This section will explore the T5 architecture model that was fine-tuned on the PAWS dataset . PAWS consists of 108,463 human-labeled and 656k noisily labeled pairs. Let's load the model and the tokenizer:

Let's use our previously defined function:

These are promising results too. However, if you get some not-so-good paraphrased text, you can append the input text with "paraphrase: " , as T5 was intended for multiple text-to-text NLP tasks such as machine translation , text summarization , and more. It was pre-trained and fine-tuned like that. 

You can check the model card here .

Parrot Paraphraser

Finally, let's use a fine-tuned T5 model architecture called Parrot . It is an augmentation framework built to speed-up training NLU models. The author of the fine-tuned model did a small library to perform paraphrasing. Let's install it:

Importing it and initializing the model:

This will download the models' weights and the tokenizer, give it some time, and it'll finish in a few seconds to several minutes, depending on your Internet connection.

This library uses more than one model. It uses one model for paraphrasing, one for calculating adequacy, another for calculating fluency, and the last for diversity.

Let's use the previous sentences and another one and see the results:

With this library, we simply use the parrot.augment() method and pass the sentence in a text form, it returns several candidate paraphrased texts. Check the output:

The number accompanied with each sentence is the diversity score. The higher the value, the more diverse the sentence from the original.

You can check the Parrot Paraphraser repository here .

Alright! That's it for the tutorial. Hopefully, you have explored the most valuable ways to perform automatic text paraphrasing using transformers and AI in general.

You can get the complete code here or the Colab notebook here .

Here are some other related NLP tutorials:

  • Named Entity Recognition using Transformers and Spacy in Python .
  • Fine-tuning BERT for Semantic Textual Similarity with Transformers in Python .
  • Speech Recognition using Transformers in Python .
  • Text Generation with Transformers in Python .

Happy learning ♥

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How to Perform Text Summarization using Transformers in Python

How to Perform Text Summarization using Transformers in Python

Learn how to use Huggingface transformers and PyTorch libraries to summarize long text, using pipeline API and T5 transformer model in Python.

Text Generation with Transformers in Python

Text Generation with Transformers in Python

Learn how you can generate any type of text with GPT-2 and GPT-J transformer models with the help of Huggingface transformers library in Python.

Speech Recognition using Transformers in Python

Speech Recognition using Transformers in Python

Learn how to perform speech recognition using wav2vec2 and whisper transformer models with the help of Huggingface transformers library in Python.

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How to Paraphrase Text in Python Using NLP Libraries

In python, transformers are the deep learning models that are used for nlp and paraphrasing. this guide uses the online paraphrasing tool to paraphrase python text..

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Python is a robust object-oriented programming  (OOP) language that finds a lot of use in the field of artificial intelligence. It is so useful that mega tech companies like Google have made libraries such as Tensorflow to help people to leverage powerful machine learning algorithms and models for various purposes.

People have made ‘sign language’ interpreters, Motorcyclist helmet detectors, and item identifiers using Python and its free libraries.

NLP (natural language processing) is the blanket term for all artificial intelligence activities related to understanding and manipulating natural languages. In Python, there are machine learning models called “ Transformers ” that can be used to take some text, break it down into components and identify which parts hold more significance.

This is later used to paraphrase the text. Transformers are “ Deep learning ” models.

How to Paraphrase Text in Python Using Transformer Libraries?

To get started, you need to have a Google account. We are going to use the Google Colab notebook, which is a cloud service that allows you to work in Python with different people.

This saves us the hassle of installing Python, its dependencies, and an IDE (integrated development environment) on our own computer. AI libraries are generally very large and have multiple dependencies, which increase their size even more. Using a cloud environment allows you to save your own hard drive space.

1. Install the Required Libraries

We need to install four libraries before we can get started. Open your Colab notebook and type out the following in the first code cell:

Let’s understand these commands a little before moving on.

“Transformers,” as we know, are deep learning models that can be used to paraphrase the text.

“Torch” provides deep learning algorithms while “Sentencepeice” is used to ‘tokenize’ (component breakdown) the text. Lastly, “Newspaper3k” is a web scraping library that is used to import articles from the internet.

Your notebook should look like this at this point.

Your notebook should look like this at this point.

2. Import the Article

To import the article, you have to provide its URL. Then you need to input the commands to download and parse it so that we can later tokenize it.

The commands for downloading the article are shown in the image below:

The commands for downloading the article are shown in the image.

Once this is done, we will move to step 3.

3. Tokenize the Article

From the transforms library, import the auto tokenizer, and then use the T5 model (T5 is a machine learning model used for text-to-text transformations; in this case: paraphrasing) to generate our paraphrased text.

This is the code that you need to input to get that effect.

This is the code that you need to input to get that effect.

4. Paraphrase the Article

To paraphrase the article, you need to create a specific function. This function accepts the tokenized article and then paraphrases each sentence individually. Then before output, it joins the sentences back together.

This function accepts the tokenized article and then paraphrases each sentence individually.

The output of the paraphrased text is shown like this: 

The output of the paraphrased text is shown like this.

You can manually copy it into a text file to get a better look.

This was one way of paraphrasing text using Python and NLP (transformers). However, you can tell that this was quite a convoluted and confusing way, especially for those who are not well versed in AI and Python.

Fortunately for them, there are many paraphrasing tools online that can do the same but without all the hassle.

Tools That You Can Use to Paraphrase Online for Free

Prepostseo  has many tools available for various purposes. The paraphrasing tool is quite good. It is free to use, and you don’t need any kind of account to get started. You can start using it without any major hitches.

When using this tool, you have three modes that you can use for free. They are:

  • Simple mode
  • Advanced mode
  • Fluency mode

In Simple mode, the tool only does some light synonymizing. A few words are just replaced with some synonyms.

Advanced mode changes more than just words and at the end of the paraphrase. You can see the changes that were made and replace them with other synonyms if you don’t like them.

Fluency mode changes not just words but phrases, sentence structure, and tone as well. However, there is no option to edit the output.

Fluency and Advanced modes are the most effective modes of this tool.

To import your content, you can upload your document that needs to be paraphrased or just copy-paste the text directly in the input field. Once the process is complete, you can download the output as well.

The only things that are bad about this tool are the ads that are present on the webpage.

Linguix is another free paraphraser that you can use without registering. It also does not have any advertisements on the webpage. This makes it very user-friendly.

Linguix does not have multiple modes on offer. However, when you paraphrase a sentence, you get multiple suggestions instead of just one. All the suggestions have different changes that affected the given text, and you can choose the one you like the best.

Its operating method is simple. You just need to write your text in the input box and then select (highlight) it. Upon selecting the text, suggestions will start popping up sentence by sentence.

The only real downside to this tool is that you can only paraphrase five sentences at once.

Paraphraser

Paraphraser.io  is also an online toolkit that has many content optimization tools. As its name suggests, its main tool is the paraphrasing tool. 

This tool is free to use and does not require registration. As is standard with these kinds of free tools, you get riddled with advertisements but nothing too annoying (such as intrusive pop-ups that interrupt what you are doing). The ads stay in their space and don’t bother you much.

You get access to two free modes: Standard mode and Fluency mode.

The Standard mode only replaces some words with their synonyms, and the overall sentence structure remains the same.

Fluency mode replaces both words and phrases while also changing up the sentence structure. It also makes the text more readable.

The other downside with this tool (apart from the ads) is that you can only paraphrase up to 500 words at once. 

This is how you can paraphrase text using python and NLP. In artificial intelligence, NLP is the field related to understanding natural languages. In Python, transformers are the deep learning models that are used for NLP and paraphrasing.

You used Google Colab for programming due to it being a cloud service. This allows you to leverage the power of Google’s servers to do these processing-heavy tasks. After everything was said and done, the paraphrasing quality was alright. 

A better alternative, however, is to use online tools that can paraphrase for you. Some of these tools have multiple modes which rewrite the text in different ways.  These tools are available for free, and most of them don’t require registration either.

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Build a Text Paraphraser Using Python with Pegasus Transformer for NLP

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Table of Content

  • What is the Pegasus transformer model?

How to Build a Text Paraphraser Using Python with Pegasus Transformer for NLP

A text paraphrasing program comes in handle for numerous purposes, including rewriting a block of sentences in an article, post, or email.

The task of paraphrasing a text usually requires building and training a Natural Language Processing (NLP) model.

NLP is tasking not only because language is a complex structure, but also the amount of data required to train an NLP model to carry out tasks such as paraphrasing sentences impacts the model performance heavily.

Hence, if it is not properly trained, you get funny outputs.

Also, the process of acquiring and labeling additional observations for an NLP can be expensive and very time-consuming.

One common approach to building a text paraphraser, especially in Python, has been to apply data augmentation to the labeled text data and rewrite the text using back translation, e.g. (en -> de -> en).

What is the Pegasus transformer Model?

Google’s research team introduced a world-class summarization model called PEGASUS . It expands Pre-training with Extracted Gap-sentences for Abstractive Summarization.

We can adopt this summarization model to paraphrase text or a sentence using seq2seq transformer models.

Additionally, seq2seq transformer models make it easy to rewrite a text without using the back translation process.

This post does not in any way promote stealing content from other websites using a method popularly called article spinning. It is solely intended for research and testing purposes.

NB: Running this program will download some files. One of which is the model is about 2 GB or more in size.

Adopting this model for paraphrasing text means that we fine-tune the Google Pegasus model for paraphrasing tasks and convert TF checkpoints to PyTorch using this script on transformer’s library by Huggingface.

Install the Dependencies

The first step would be to install the required dependencies for our paraphrasing model.

We use PyTorch and the transformers package to work with the PEGASUS model.

Also, we use the sentence-splitter package to split our paragraphs into sentences and the SentencePiece package to encode and decode sentences.

Set Up the PEGASUS Model

Next, we will set up our PEGASUS transformer model, import the dependencies, make the required settings such as maximum length of sentences, and more.

Access the Model

Test the model.

Paraphrase a single sentence:

The output:

We got ten different paraphrased sentences by the model because we set the number of responses to 10. Paraphrase a paragraph: The model works efficiently on a single sentence. Hence, we have to break a paragraph into single sentences. The code below takes the input paragraph and splits it into a list of sentences. Then we apply a loop operation and paraphrase each sentence in the iteration.

Combine the separated lists into a paragraph:

You learned how to create a Text Paraphrase model by using NLP methods. You also learned about the PEGASUS transformer model and explored its main components for NLP and how it simplifies the process.

You may use the following resources to learn more PEGASUS model research white paper , Paraphrase model using HuggingFace , User Guide to PEGASUS .

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sentence paraphrasing python code

How to Paraphrase Text Using Python with the Help of AI Tools

Paraphrasing is a technique for expressing ideas with different words to achieve clarity and uniqueness. Paraphrasing can be done manually or by using paraphrasing tools that are fueled with Python and AI on the backend.

Paraphrasing is mainly used to alter a text to make it look more distinctive than the original text while also ensuring that the original meaning remains.

Paraphrasing, when done by a human, is considered to be more accurate when compared with AI-based tools, but this is not always the case.

In this post, I will discuss how you can take advantage of  Python  and AI-based paraphrasing tools to paraphrase any text you want and how paraphrasing tools can help you swiftly rewrite any text in minutes.

What Is Python? How Is It Used in Paraphrasing Tools?

Many of you may be familiar with  Python as a programming language  but don’t know much about it. Right?

Well, it is an ideal coding language with lively semantics that deals with data within the application. Nowadays, Python is widely used in paraphrasing tools and other text editors.

Developers mainly use Python to reduce the response time between a keystroke and a machine. This decrease in response is down to the light nature of the code.

This is why most developers prefer to use Python when lots of data is involved on the user’s end. Common examples of Python-based tools include paraphrasing tools, plagiarism checkers, word counters, and grammar checkers.

Paraphrasing using Python is generally done with fine-tuned transformers. To give you an insight into how this works, we will use a T5 transformer that comprises an architecture model called Parrot.

Parrot is an augmentation framework that aims to speed up training models based on natural language understanding (NLU). To get started, you need to install a fine-tuned model to do the paraphrasing. You can install it with these steps:

Depending on your internet speed, downloading the model’s weight and tokenizer may take a few seconds or a few minutes.

The Parrot library comprises multiple libraries, and each has its own function: one model performs paraphrasing, one analyzes and calculates fluency, one checks adequacy, and one looks for diversity.

Let's look at a quick example with a sentence as the input.

In this library, the sentence is passed in a text form, and Parrot augment is used to produce different paraphrased texts. Here is the result:

('Many of you are probably familiar with Python as a programming language, but know very little about it', 27)

('Many of you may be familiar with Python as a programming language, but don't know much about it', 13)

The number at the end of each result is the diversity score. These values define how diverse the resulting sentence is from the input text.

Now you can see how Python helps to generate diverse, readable, and clear content.

Here is the Parrot Paraphraser repository .

Note: other training models are available to use with transformers in a paraphrasing tool to ensure quick and accurate results.

Difference Between Conventional Paraphrasing Tools and AI/Python in Paraphrasing Tools

Paraphrasing tools have been around for quite a while but have gained significant popularity since the involvement of AI and Python.

At first, they were just text spinners that changed all the words in a text with their counter synonyms, but this made the text unreadable.

For instance, if you use a conventional paraphrasing tool that isn’t backed by AI or Python with a phrase like: “I am making dinner for my family”, it will change this to something like: “ I am constructing feed for my household.”

This sentence isn’t readable or reader-friendly at all. But if you use the exact phrase in a paraphrasing tool like  paraphrasingtool.ai  (which uses Python and AI algorithms), it will generate different results.

Let’s take a look at a small example with this same sentence.

sentence paraphrasing python code

Asad Shehzad is the founder of Paraphrasingtool.ai, a website that helps people paraphrase their essays and papers. He is also a researcher for AI Projects, where he studies the feasibility of artificial intelligence in various industries. Asad is an avid learner and loves to explore new things. He is also a fitness enthusiast and likes to stay in shape.

Disclosure: Hackr.io is supported by its audience. When you purchase through links on our site, we may earn an affiliate commission.

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SentenceTransformers Documentation ¶

SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks .

You can use this framework to compute sentence / text embeddings for more than 100 languages. These embeddings can then be compared e.g. with cosine-similarity to find sentences with a similar meaning. This can be useful for semantic textual similarity , semantic search , or paraphrase mining .

The framework is based on PyTorch and Transformers and offers a large collection of pre-trained models tuned for various tasks. Further, it is easy to fine-tune your own models .

Installation ¶

You can install it using pip:

We recommend Python 3.8 or higher, and at least PyTorch 1.11.0 . See installation for further installation options, especially if you want to use a GPU.

The usage is as simple as:

Performance ¶

Our models are evaluated extensively and achieve state-of-the-art performance on various tasks. Further, the code is tuned to provide the highest possible speed. Have a look at Pre-Trained Models for an overview of available models and the respective performance on different tasks.

Contact person: Nils Reimers, info @ nils-reimers . de

https://www.ukp.tu-darmstadt.de/

Don’t hesitate to send us an e-mail or report an issue, if something is broken (and it shouldn’t be) or if you have further questions.

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

Citing & Authors ¶

If you find this repository helpful, feel free to cite our publication Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks :

@inproceedings { reimers-2019-sentence-bert , title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks" , author = "Reimers, Nils and Gurevych, Iryna" , booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing" , month = "11" , year = "2019" , publisher = "Association for Computational Linguistics" , url = "https://arxiv.org/abs/1908.10084" , }

If you use one of the multilingual models, feel free to cite our publication Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation :

@inproceedings { reimers-2020-multilingual-sentence-bert , title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation" , author = "Reimers, Nils and Gurevych, Iryna" , booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing" , month = "11" , year = "2020" , publisher = "Association for Computational Linguistics" , url = "https://arxiv.org/abs/2004.09813" , }

If you use the code for data augmentation , feel free to cite our publication Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks :

@inproceedings { thakur-2020-AugSBERT , title = "Augmented {SBERT}: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks" , author = "Thakur, Nandan and Reimers, Nils and Daxenberger, Johannes and Gurevych, Iryna" , booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies" , month = jun , year = "2021" , address = "Online" , publisher = "Association for Computational Linguistics" , url = "https://www.aclweb.org/anthology/2021.naacl-main.28" , pages = "296--310" , }
  • Install SentenceTransformers
  • Install PyTorch with CUDA-Support
  • Comparing Sentence Similarities
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  • Model Overview
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  • Input Sequence Length
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  • Example Scripts
  • Pre-trained Bi-Encoders (Retrieval)
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  • Agglomerative Clustering
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  • Paraphrase Mining
  • Marging Based Mining
  • Bi-Encoder vs. Cross-Encoder
  • When to use Cross- / Bi-Encoders?
  • Cross-Encoders Usage
  • Combining Bi- and Cross-Encoders
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  • Installation
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  • Creating Networks from Scratch
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  • Loading Custom SentenceTransformer Models
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  • Adding Special Tokens
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  • Performance
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  • Data Format
  • Loading Training Datasets
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  • Speed - Performance Trade-Off
  • Dimensionality Reduction
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  • Extend to your own datasets
  • Methodology
  • Scenario 1: Limited or small annotated datasets (few labeled sentence-pairs)
  • Scenario 2: No annotated datasets (Only unlabeled sentence-pairs)
  • Datasets on the Hugging Face Hub

Training Examples

  • Training data
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  • Cross-Encoder Knowledge Distillation

Unsupervised Learning

  • CT (In-Batch Negative Sampling)
  • Masked Language Model (MLM)
  • Performance Comparison
  • Domain Adaptation vs. Unsupervised Learning
  • Adaptive Pre-Training
  • GPL: Generative Pseudo-Labeling

Package Reference

  • SentenceTransformer
  • cross_encoder

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How Paraphrasing Tools use NLP Libraries in Python to Generate Text?

Paraphrasing is a common technique to express the meaning of an existing idea or text in a more translucent way to make it more understandable. There are variety of different ways of doing paraphrasing that a person needs to learn to get the best results out of it.

However, on the other hand, this technique consumes a lot of time too and to counter this issue, technology came in to help.

There are certain automatic tools available that can do the paraphrasing job for you in a much more efficient way. But do you know how they work? And how you can paraphrase any text using NLP libraries in Python?

In this blog post, we will delve into the world of natural language processing (NLP) and explore how to paraphrase content using NLP libraries in Python. We will cover the basics of NLP and how it works, as well as the various libraries and frameworks that are available for paraphrasing text in Python.

Throughout the post, we will provide detailed examples and code snippets to help you get started with paraphrasing text using NLP libraries in Python. Whether you are a beginner or an experienced programmer, this post will provide you with the knowledge and skills you need to paraphrase content using NLP libraries in Python effectively.

Before jumping into the details, let's first discuss the Python programming language.

How paraphrasing tools use NLP libraries in Python to Generate Text

What is Python language: An Overview

Python is a high-level, general-purpose programming language that was first released in 1991. It is known for its simplicity and lightweight, making it an ideal choice for programmers.

One of the critical features of Python is its extensive standard library , which includes a wide range of modules and functions for tasks such as data manipulation, file I/O, and networking. This makes it easy for developers to build robust and efficient applications without additional libraries or frameworks.

Python is also highly versatile and can be used for various projects, from simple scripts to large-scale applications like paraphrasing. It is supported by a wide range of platforms and operating systems and can be used in conjunction with other languages and technologies, such as C/C++ and Java.

Python is a popular and versatile programming language that is well-suited for various tasks and projects, including natural language processing (NLP) and text paraphrasing.

What is Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence that is concerned with enabling computers to process, analyze, and generate human language. It is a complex field that encompasses a diverse range of techniques and technologies, including machine learning, linguistics, and computer science.

One of the primary goals of NLP is to enable computers to process and analyze large volumes of unstructured text data in a way that is meaningful and useful to humans. This includes tasks such as text classification, sentiment analysis, language translation, and information extraction.

Paraphrasing is a specific type of NLP task that involves generating a new version of text that conveys the same information in a different way. This can be done using a variety of techniques and technologies, including transformer models and machine learning algorithms which we will discuss in the next section.

Now, after the basics, let's step by step discuss how to paraphrase using NLP libraries in python.

How To Paraphrase text in Python using Pre-trained NLP Models?

To paraphrase text using transformers in Python, we can utilize the BERT transformer model and its associated tokenizer. BERT, or Bidirectional Encoder Representations from Transformers, is a powerful transformer model that has achieved top-notch results on a wide range of natural language processing tasks.

Here's an example of how to use BERT to paraphrase text in Python that is used by many online paraphrasing tools:

First, we need to install the necessary libraries by running the following command in the terminal:

Next, we can import the BERT model and tokenizer in our Python script:

Now, let's say we want to paraphrase the following sentence: "The quick brown fox jumps over the lazy dog." Here's how we can do it using the BERT model:

This will generate a new version of the input text that conveys the same information in a different way. For example, the output text could be something like "The rapid brown fox leaped over the sluggish dog."

As you can see, using transformers in Python to paraphrase text is a straightforward and efficient process. With just a few lines of code, we were able to generate a high-quality paraphrase of the input text using the powerful BERT model.

Additionally, because BERT is a bidirectional model, it can understand the context and meaning of the input text more nuancedly, resulting in more accurate paraphrases. You can also use other transformers with the same procedure to paraphrase text, like GPT2, GPT3, Pytorch, etc.

Now let's discuss a real-life example of a tool that uses NLP models in python to generate text that you can use to paraphrase any text quickly without needing to run codes.

Paraphrasing Tool: Paraphrase Text Online

Paraphrasing is a complex task that requires a deep understanding of the English language and the ability to generate text that conveys the same information in a different way. This is why it is often difficult to paraphrase text manually, especially if you are not a native English speaker.

However, there are many online paraphrasing tools that can help you paraphrase text quickly and easily. These tools use advanced NLP models and technologies to generate high-quality paraphrases of your text.

1. Paraphrasingtool.ai

When looking for an online paraphrasing tool, you will always find Paraphrasingtool.ai in the list of top reputable tools, and there are several reasons for this. The tool's popularity is mainly because it generates excellent results at a rapid pace.

The tool mainly relies on AI, NLP, and ML techniques to ensure near-human text that is readable, unique, and compatible for use anywhere the user want. The tool is programmed using Python language with NLP libraries like the one we discussed above, ensuring accurate results.

As paraphrasing is done using different techniques, the tool is equipped with several techniques that can alter existing content in many different ways to make it more understandable, remove plagiarism from it, and so on. It offers eight different modes crafted to meet several user needs.

You can use this tool for your regular paraphrasing needs like generating text for emails, blogs, articles, and so on. Also, for complex needs like removing plagiarism from the text, you can use premium modes that are faster and more efficient. Overall, the tool is an excellent resource for writers and other people dealing with text daily.

Here is an example of how this tool paraphrases text:

Paraphrasingtool.ai Screenshot

2. Plagiarism-changer.com

Plagiarism is a great concern and you may have heard about the consequences it brings along to your reputation and the work you have done. Paraphrasing is commonly used to eliminate plagiarism.

So, to counter issues of self and unintentional plagiarism, this tool was developed to help students, bloggers, writers or anyone that may face the problem of plagiarism. This tool is used as an example because it used certain libraries in python to make certain changes in the text which ensures no plagiarism is left.

This tool is a great option to avoid the need of doing all the technical stuff and to help you eliminate plagiarism.

Plagiarism-changer.com Screenshot

3. Rephrase.info

As we have previously mentioned, plagiarism is seriously bad for writers. With Rephrase.info's paraphrasing tool, you can easily and effortlessly reduce the chances of plagiarism from your work. This can help bloggers, students, teachers, and even professional journalists from submitting work with accidental or coincidental plagiarism in it.

Rephrase has five paraphrasing modes to work with and all of these modes operate by using Python libraries and algorithms. Different algorithms provide different results and that's how all the modes differ from each other.

The best feature of this tool is that is freemium. So, you can use some of its features for free and without registration. In this way, the bar for entry is very low. The fact that it also supports 20-plus languages makes it very accessible to a large audience. It is also very easy to use due to its minimalistic UI.

rephrase.info Screenshot

4. Paraphrasetool.ai

ParaphraseTool.ai is an online tool that offers free paraphrasing with around 6+ AI modes.

It utilizes advanced natural language processing algorithms to paraphrase sentences, paragraphs, or entire documents while retaining the originality of content.

The tool is designed to assist individuals who need to paraphrase text for various purposes, such as self-learning, academic writing, content creation, or avoiding plagiarism.

Users can input their text into the tool's interface and receive a paraphrased version quickly with just a single click.

ParaphraseTool.ai aims to enhance writing productivity and promote creativity by providing a convenient and reliable paraphrasing solution.

You can select from different paraphrasing modes such as formal, creative, anti-plagiarism, SEO, fluency, and academic.

The tool has grammar-checking and summarizing features to help you check content for potential grammatical errors and convert large text into brief ones.

Paraphrasetool.ai Screenshot

Final Words

Python is a great programming language that can be used to solve everyday problems if used in the right ways. Paraphrasing, if done manually, can be a daunting task that you can easily do by following a simple step-by-step process.

However, if you do not want to get into the technical side, you can use AI paraphrasing tools to help you do all these processes automatically.

sentence paraphrasing python code

How To Paraphrase Your Text Using Python? |3 Suggested paraphrasing Tools

sentence paraphrasing python code

Artificial intelligence has come a long way. Its applications in text processing and natural language processing have eased the lives of authors and writers everywhere.

When we talk about artificial intelligence, how can we leave out Python? The two are almost synonymous at this point. 

Python is an object-oriented programming (OOP) language that is simultaneously very powerful and much easier to use than other OOP languages. 

Python has many libraries and pre-defined functions that allow a programmer to efficiently write code. In particular, the libraries related to mathematical functions and artificial intelligence are very fleshed out.

What is Paraphrasing?

Paraphrasing is the act of writing some text using different words, phrases, and sentence structures without changing its meaning. Paraphrasing is an important technique utilized in writing.

When a writer has to explain the ideas and concepts of another person in their own words, then they need to resort to paraphrasing. The need may arise because the original wording might be too difficult for readers to grasp.

Artificial intelligence is used to automate the process of paraphrasing. There exist many paraphrase tools that can paraphrase text automatically. However, programmers can also directly create a Python program to paraphrase text.

In this article, we are going to check out both methods of paraphrasing, a) by using Python, and b) by using paraphrasing tools .

How to Paraphrase Your Text Using Python?

There are plenty of different ways to create a paraphrasing program using Python. However, currently one of the most popular methods is to use transformers.

Transformers are artificial neural networks that are able to learn the context of a text. This means they can understand its meaning. This makes them ideal to use for paraphrasing as you can count on them to not butcher the meaning of the text.

In the example we are going to follow, we will be using the Pegasus model. It is a transformer that uses an encode-decoder model for sequence-to-sequence learning. You can learn more about the Pegasus model by reading its documentation.

Pegasus stands for “Pre-training with Extracted Gap-Sentences for Abstractive Summarization”. Don’t be confused by this. Abstractive summaries are basically paraphrasing as well. An abstract summary summarizes a text by rewriting all of its main points in a concise form using different wording. 

Now that we have covered the basics, let’s see how we can use Pegasus for paraphrasing.

  • Setup Your Environment

If you are an experienced Python user, then you probably already have an IDE (Integrated Development Environment) setup already. But if you are a new user then fear not, simply use the “Google Collaboratory”. It is a free notebook that lets you run code without any setup required. It is a cloud service, so you need the internet to use it.

So, step 1, is to install all the dependencies required. That is very simple to do, you only need to run the following three commands.

Setup Your Environment paraphrasing tools

In text form (so that you can copy it for yourself)

!pip install sentence-splitter

!pip install transformers

!pip install SentencePiece

This will prompt the system to download and install these dependencies on the cloud computer allocated to you (or on your system if you are not using Collaboratory).

The output on Collaboratory looks something like this.

Setup Your Environment result of paraphrasing tools

In this screenshot, there is more text, because the system in question already has most of the dependencies installed already. So, the “requirement already satisfied” messages have increased the amount of text.

  • Setting Up Pegasus 

The next thing we need to do is to import the Pegasus model and set it up because we need it to do the paraphrasing. Without it, things will become more difficult. 

 To set up Pegasus, we need to install Pytorch first. Pytorch is a Python package that provides high-level features of tensor computation and deep neural networks. It is the underlying framework that powers Pegasus.

The commands to install these packages are given below.

Setting Up Pegasus of paraphrasing tools

In text form:

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_defvice)

Running this code should download a bunch of files that look like this. The Pytorch model bin is a pretty large file, so don’t worry if it takes a bit of time to download it.

 test if the model is working or not

Then we need to test if the model is working or not. There is a simple way to do that. Just input the following commands and have some sample text handy.

output of 5 sentences that are slightly paraphrased. 

Enter your sample text in inverted commas. It should go in place of the orange text in the above-given picture. After running the “get response (text, 5)” text, there will be an output of 5 sentences that are slightly paraphrased. 

The output will look like this.

If you get an output with some paraphrased sentences then the model is working properly

If you get an output with some paraphrased sentences then the model is working properly.

  • Break the Text into Individual Sentences

The next step in paraphrasing is to break the provided sample text into sentences. This is because it is easier to paraphrase one sentence rather than an entire paragraph. 

To do that you have to first input your text. Do that by using the following lines of code.

Break the Text into Individual Sentences

Your text should go in front of the “=” sign in inverted commas. If you haven’t made any mistakes then it should be orange in color.

The output should be simply the entire text you have inputted. On Collaboratory it should show up beneath the cell you used to input the text.

sentence paraphrasing python code

Once the text is in the system, then it needs to be split into separate sentences. Well basically, it will be a list of sentences. The code used for it is given below.

sentence paraphrasing python code

from sentence_splitter import SentenceSplitter, split_text_into_sentences

splitter = SentenceSplitter (language= ‘en’)

sentence_list  = splitter.split(context)

sentence-list

The output of this command will generate a list of all the sentences in the paragraph. It will look like this;

output of this command will generate a list of all the sentences in the paragraph

  • Paraphrase the Paragraph

This step basically involves using a loop to iteratively go through the list and rephrase each sentence. Then we will do it once more so that we have two paraphrased versions of each tool. 

Then finally we will combine them together to make a coherent paragraph. The next part only involves coding, so just follow along and there is not much explanation needed.

First, we have to create a loop that will go through the list of sentences we generated in the previous step. 

The code for that is as follows:

For i in sentence_lists: a = get_response(i, 1) paraphrase.append (a)

This will generate the following output, which is just another list but the sentences are rephrased.

Paraphrase the Paragraph

Then we need to create a second split using the following code.

paraphrase2 = [‘ ‘.join(x) for x in paraphrase]

paraphrase2

This will have a similar output as the first time. Then finally we will combine these split lists into a paragraph. The code for doing that is:

sentence paraphrasing python code

paraphrase3 = [‘ ‘.join(x for x in paraphrase2) ]

paraphrased_text =str(paraphrase3).strip(‘[ ]’).strip(“’”)

paraphrased_text

The output will be a paragraph (not a list of sentences). This paragraph is our final paraphrased version. 

We can use a simple command to write both the original and rephrased paragraph together so that we can compare them.

sentence paraphrasing python code

Simply print the ‘context’ variable which contains the original text as well as print the ‘paraphrased_text’ variable whose name is very self-explanatory.

The output should look like this:

sentence paraphrasing python code

And that is how you can use Python to paraphrase text. The upside of this approach is that there are no word limits. So, you can rewrite entire articles and essays without an issue. However, processing too long of a text will take a lot of time.

(Freely available code , courtesy of Viktor Dey.)

3 Suggested Paraphrasing Tools

We just looked at how you can use Python for paraphrasing. However, even with the help of tools such as Google Collaboratory, it was inconvenient and relatively time-consuming.

However, there is a way to avoid that hassle altogether. You can simply get help from an online paraphrasing tool . Most of these tools are free, and not the kind of free where you have to give your credit card info. They don’t even require an account.

So, you can get assistance from an online paraphrasing tools with no strings attached. Obviously, they do come with some drawbacks such as ads and word limits, but that is an acceptable compromise.

Now we will look at some reliable paraphrasing tools that you can utilize.

  • Paraphraser

The paraphrasing tools by Paraphraser.io is a free tool. It also does not require registration for usage. Hence it is an extremely accessible tool that anyone with an internet connection can use.

Accessibility features aside, the tool itself is also pretty good. It comes with multiple modes, two of which are free. They are called:

  • Standard mode
  • Fluency mode

Standard mode utilizes the synonym exchange technique to replace a few words with their synonyms. It does not make many changes to the text, but the output is still visibly different.

 Suggested Paraphrasing Tools

The other mode in this paraphraser is called Fluency mode. It changes the text considerably compared to the Standard mode. The highlight, however, is its ability to make the text more readable. The synonyms it uses are easier to read, and it replaces and restructures sentences that are confusing.

The other two modes in this paraphrasing tools are called:

Both of these modes require the user to upgrade to premium. The only other downside (practicality-wise) is that only 1,000 words can be rephrased in one session.

  • Linguix.com

The paraphrasing tool by Linguix.com is another great entry on this list. This is a tool that is more suited to learning how to paraphrase, as it requires more user input than most tools. 

It is free to use, but it does require creating an account before the user can access its full features. It does not come with fancy modes, but it does provide many variations of each sentence. It is up to the user to choose which variation they want to use. 

The paraphrasing tool by Linguix.com

The method of using it is quite simple. Once the text has been uploaded/written in the tool’s interface, the user has to select it using either the mouse or the “Ctrl+A” shortcut. Then they have to click the small icon at the end of the text. 

This opens a drop-down menu under the first selected sentence. This menu contains different paraphrased versions of the text. The user can select from these versions and move on to the next sentence.

Obviously, the drawback here is that the tool is not fully automatic, but the end result is much more personalized compared to other tools.

One great thing about this tool is that it does not have word limits. So, users can paraphrase very large documents if they like.

The final tool in our list is the paraphraser by Editpad.org.  This tool is also free and does not require registration. But unlike the other two tools on this list, it does not have any premium features nor any options to upgrade its functionality. 

The final tool in our list is the paraphraser by Editpad.org

The tool comes with four modes, they are called:

The Smooth mode in this paraphrasing tool increases the readability of the text. It makes the text smoother to read.

The Formal mode rephrases the text to change its tone and make it sound more official and respectful.

Smarter mode is kind of a mixed bag, it tries to make the text more unique. To do that it adds some minor, non-existent details which change the text considerably.

Improver mode does literally just that, it improves the text by making sentence structure better and reducing unnecessary contractions. 

Four free modes are a pretty good deal and the only downside to this tool is that it only lets users rephrase 1,000 words per session.

Python is a powerful programming language and it has found much success in the field of artificial intelligence. In this article, we looked at how Python and the Pegasus transformer can be used to paraphrase text. 

The method was overall quite straightforward and there was not any complicated programming involved. Nevertheless, it was inconvenient because of the setup and installation processes involved.

Thus, we looked at the alternative as well, which was to use online paraphrasers. Three free tools were discussed along with their features such as multiple modes. With the exception of Linguix, the tools had 1,000-word limits which are quite limiting.

It is up to user discretion which tools they want to use. People interested in programming and artificial intelligence can try the Python method as well.

sentence paraphrasing python code

Pramod Kumar Yadav is from Janakpur Dham, Nepal. He was born on December 23, 1994, and has one elder brother and two elder sisters. He completed his education at various schools and colleges in Nepal and completed a degree in Computer Science Engineering from MITS in Andhra Pradesh, India. Pramod has worked as the owner of RC Educational Foundation Pvt Ltd, a teacher, and an Educational Consultant, and is currently working as an Engineer and Digital Marketer.

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python paraphrase

Answered on: Monday 10 July, 2023 / Duration: 14 min read

Programming Language: Python , Popularity : 3/10

Python Programming on www.codeease.net

Solution 1:

Python paraphrase is a technique used to rewrite or rephrase a given piece of code in a different way while maintaining its functionality. This can be done by using different syntax, alternative functions, or applying different programming concepts.

Here are a few examples of Python paraphrases with proper code examples and outputs:

In this paraphrase, the original code's function calculate_square is rewritten using a lambda function instead of a regular function.

In this paraphrase, the original code's for loop is replaced with a list comprehension, achieving the same result in a more concise way.

In this paraphrase, the original code's function is_even is rewritten using a lambda function, and the for loop with an if condition is replaced with a list comprehension.

These examples demonstrate how Python code can be paraphrased using different techniques and achieve the same desired output. Paraphrasing can help improve code readability, conciseness, or adapt it to different programming styles.

Solution 2:

Sure, here is an in-depth solution for Python paraphrase in Python with proper code examples and outputs.

**1. Installing the required libraries**

The first step is to install the required libraries. We need the following libraries:

We can install these libraries using the following command:

**2. Importing the libraries**

Once the libraries are installed, we can import them into our Python script.

**3. Tokenizing the text**

The next step is to tokenize the text that we want to paraphrase. This means breaking the text down into individual words or phrases. We can do this using the nltk library.

**4. Paraphrasing the text**

Now that the text is tokenized, we can paraphrase it using the transformers library. We will use the Bart model for this example.

The generate() function takes a few parameters:

* return_dict : A boolean flag that specifies whether to return a dictionary with the generated text and other metadata.

The generate() function returns a dictionary with the generated text and other metadata. The generated text is stored in the output_tokens key.

**5. Printing the paraphrased text**

We can print the paraphrased text using the following code:

The output of the code will be a list of tokens that represent the paraphrased text. We can then join the tokens together to get the paraphrased text as a string.

**6. Example code**

Here is an example code that shows how to paraphrase text in Python:

**7. Output**

The output of the code will be a list of tokens that represent the paraphrased text. We can then join the tokens together to get the paraphrased text as a string. For example, the output of the code above is the following string:

As you can see, the paraphrased text is still semantically equivalent to the original text, but it is expressed in a different way. This is because the Bart model has learned to paraphrase text by identifying different ways to express the same meaning.

**8. Conclusion**

This is just a basic example of how to paraphrase text in Python. There are many other ways to paraphrase text, and you can experiment with different techniques to see what works best for you.

Solution 3:

Paraphrasing in Python typically involves using Natural Language Processing () libraries to understand the context a with different words task can be quite challenging as semantic the output is for complex sentences.

The paraphrase library for paraphrasing tasks. However, let's use transformers by HuggingFace, which provides pre-trained models for many NLP tasks, including text generation.

First, you should install the requisite libraries by using pip:

tokenizer = T.from_pretrained('t5-base')

paraphrase_output = model.generate max_length=100, num_return_sequences=1, temperature=)

paraphrase_sentence = tokenizer.decode(paraphrase_output[0], skip_special_tokens=True)

return paraphrase_sentence

print(f"Paraphrasedparaphrase(sentence)}")

Unfortunately, I can show you the output but here is an example of aPython popular programmingaphrasedPythonPlease remember that the output depends on various parameters, like (which controls the randomness of predictions by scaling the logits before applying softmax), the model's and the quality of the data fed into it. Different the same sentence might yield different results.

Also note that running this kind task requires a certain amount of computational resources on your specifications, it might take some time or might not run at all if required hardware.

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paraphrase-generation

Here are 40 public repositories matching this topic..., prithivirajdamodaran / parrot_paraphraser.

A practical and feature-rich paraphrasing framework to augment human intents in text form to build robust NLU models for conversational engines. Created by Prithiviraj Damodaran. Open to pull requests and other forms of collaboration.

  • Updated Jan 7, 2024

vsuthichai / paraphraser

Sentence paraphrase generation at the sentence level

  • Updated Dec 7, 2022

iamaaditya / neural-paraphrase-generation

Neural Paraphrase Generation

  • Updated Jul 26, 2022

FranxYao / dgm_latent_bow

Implementation of NeurIPS 19 paper: Paraphrase Generation with Latent Bag of Words

  • Updated Oct 9, 2021

kuutsav / llm-toys

Small(7B and below) finetuned LLMs for a diverse set of useful tasks

  • Updated Jul 26, 2023

wyu-du / Reinforce-Paraphrase-Generation

This repository contains the data and code for the paper "An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation" (EMNLP2019).

  • Updated Apr 1, 2020

malllabiisc / SGCP

TACL 2020: Syntax-Guided Controlled Generation of Paraphrases

  • Updated Dec 8, 2022

malllabiisc / DiPS

NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation

IBM / quality-controlled-paraphrase-generation

Quality Controlled Paraphrase Generation (ACL 2022)

  • Updated Nov 2, 2022

FranxYao / Gumbel-CRF

Implementation of NeurIPS 20 paper: Latent Template Induction with Gumbel-CRFs

  • Updated Dec 1, 2020

dev-chauhan / PQG-pytorch

Paraphrase Generation model using pair-wise discriminator loss

  • Updated May 28, 2021

uclanlp / synpg

Code for our EACL-2021 paper "Generating Syntactically Controlled Paraphrases without Using Annotated Parallel Pairs".

  • Updated May 10, 2022

L0Z1K / para-Kor

Create paraphrasing korean sentence with GPT-3

  • Updated Jan 30, 2023

L-Zhe / CoRPG

Code for paper Document-Level Paraphrase Generation with Sentence Rewriting and Reordering by Zhe Lin, Yitao Cai and Xiaojun Wan. This paper is accepted by Findings of EMNLP'21.

  • Updated Nov 10, 2021

wyu-du / GP-VAE

This repository contains the data and code for the paper "Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process Priors" (SPNLP@ACL2022)

  • Updated Jun 27, 2022

BH-So / unsupervised-paraphrase-generation

"Unsupervised Paraphrase Generation using Pre-trained Language Model."

  • Updated Aug 28, 2020

vincent9514 / Text-Variant-Generation

📄Neural Sentential Paraphrase Generation to Augment Chatbot Training Dataset

avidale / dependency-paraphraser

A sentence paraphraser based on dependency parsing and word embedding similarity.

  • Updated Sep 21, 2021

gibbsbravo / ParaPhrasee

Paraphrase Generation Using Deep Reinforcement Learning - MSc Thesis

  • Updated Jun 10, 2020

crujzo / Para-Phrase

Python project to create paraphrase of any text content

  • Updated May 2, 2019

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paraphraser 0.0.1

pip install paraphraser Copy PIP instructions

Released: Sep 16, 2020

A python module which returns list of sentences with same contextual meaning as given sentence

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License: GNU General Public License v2 or later (GPLv2+)

Author: Shashank Jaiswal

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  • OSI Approved :: GNU General Public License v2 or later (GPLv2+)
  • OS Independent
  • Python :: 2.7
  • Python :: 3
  • Python :: 3.6
  • Python :: 3.7

Project description

Paraphraser.

This is a python library which generates list of sentences with similar contextual meaning as given input sentence.

Installation

Run the following snippet to install the library in your system

Developing Paraphraser

To install paraphraser along with the tools you need to develop and run tests, run the following in command in your system:

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Sep 16, 2020

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sentence paraphrasing python code

How to Use AI Tools to Paraphrase Text Using Python

Using alternative words to explain thoughts in order to make them clear and distinctive is a technique called paraphrasing. One can paraphrase manually or with the aid of tools that are powered by Python and AI on the backend.

In order to make a text stand out from the original while maintaining the original meaning, paraphrasing is frequently done.

When performed by a human as opposed to AI-based techniques, paraphrasing is generally thought to be more accurate.

Machine learning is a wonderful example of how AI is used in technology. It can be used in paraphrasing tool for attaining more accuracy and efficiency.

Each word has a unique meaning thanks to the analysis of countless words written on innumerable topics by machines employing cutting-edge technologies. 

Paraphrasing the text is not any easier. Strong writing skills are necessary for paraphrasing in addition to subject understanding. As a result, correct paraphrasing can be challenging for even experienced writers who are suffering from writer’s block.

To save time, many authors use paraphrase software. They frequently feature eliminating plagiarism, altering the tone of content, and swiftly rewriting it among their qualities and applications. 

What is a paraphraser?

The main goal of a paraphraser is to make writing easier. You can rephrase the information at the level of phrases, sentences, and paragraphs. The diction and syntactic structures are changed, resulting in fully original and error-free content. 

The most accurate rewriting of the information is done by the paraphrasing tool. It neither alters the context nor lowers the standard of the information. You can use a paraphrase tool to run whatever form of text you are working on.

How did Python assist AI?

How did Python assist AI?

Let me give you a quick introduction to Python if you’re not familiar with it.

Python is a well-liked programming language that is revolutionising AI. It was created to be straightforward and simple to write while still being capable of supporting the creation of sophisticated software programmes.

Python was developed in 1991 by Guido Van Rossum, and it has grown in popularity ever since. The language has grown in popularity to the point where it is currently one of the most widely spoken languages worldwide!

Python is a general-purpose programming language, which means that practically any application may be written in it. It is mostly employed in the creation of online applications, data science, and machine learning.

It is the ideal option for creating machine learning and artificial intelligence codes because of its capacity to handle complicated programmes and algorithms in an effective manner. This is due to artificial intelligence and machine learning.

Your writing habits and styles are evaluated by the keyboard. It keeps track of the words you use frequently as well as your usage pattern. Now, it provides suggestions based on this data and pre-set AI rules as you type.

How is Python used in AI tools to paraphrase text?

Here is the Example code for paraphrase text using python.

Python is a computer language that many of you may be familiar with, though you may not know much about it.

You must have a Google account to get started. You can collaborate on Python projects using this service.

Let’s now go into the process of applying AI technologies with Python to paraphrase text. These actions will enable you to use artificial intelligence to produce excellent paraphrases.

1. Installing the Required Libraries

Make sure your Python environment has the required libraries installed before you begin.

You may use well-known natural language processing libraries for this, like NLTK, SpaCy, or Transformers. Using pip or conda, install the necessary libraries. Four libraries need to be installed before we can begin.

You can start the paraphrasing process after the installation is finished. Because the required libraries will already be installed in your Python environment.

2. Load text to paraphrase

Use a file or user input to load the text you want to paraphrase into your Python script. You must specify the article’s URL in order to import it. You must next enter the commands to download and parse the data so that we can tokenize it later.

3. Choose the paraphrasing tool.

Choose from a number of choices the AI-powered paraphrase tool you want to use. When making your decision, take into account the specific capabilities, performance, and simplicity of Python integration.

4. Make the article tokenized.

Import the auto tokenizer from the transformations library, and then use the T5 model to produce paraphrased text. A machine learning model called T5 is employed for text-to-text conversions.

5.Article Rephrasing

You must construct a certain function, to paraphrase the article. This method takes an article that has been tokenized as input and then paraphrases each sentence separately. The sentences are then rejoined before output.

6. Review the rephrased text.

Analyse the appropriateness and quality of the created phrases. Think about things like coherence, fluency, and maintaining the original meaning. To get the intended result, the paraphrased text might need to be adjusted or revised.

This was one method of utilising Python and NLP to paraphrase text. But it’s obvious that this was a very complicated and perplexing process, especially for individuals who are not familiar with Python or AI.  

Avoid using paraphrasing tools without Python and AI.

These tools produce text that is not understandable, interesting, relevant, or helpful anywhere. Whether you are delivering it to your teacher or an online audience,

The following are the primary drawbacks of paraphrase tools devoid of Python and AI:

  • They are unable to discern the text’s purpose or context.
  • They are unable to figure out the text’s true meaning because they only have access to individual words in a passage and not phrases, sentences, or even paragraphs.
  • They employ ineffective synonyms, which reduce or eliminate readability.

Final Thoughts

You can gain a competitive edge by using Python-based AI-powered technologies to paraphrase text. The objective is to create content that resonates with your target audience. You may improve the quality of your content, increase organic visibility, and promote sustainable long-term growth for your website by integrating AI-powered paraphrasing into your content creation process. 

These technologies are improved by Python and AI, enabling them to handle any kind of content. Additionally, if you have any doubts, you can test out other tools to see how they differ. With paraphrasing.io, you can complete your entire task in a matter of seconds.

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IMAGES

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COMMENTS

  1. How to Paraphrase Text using Python

    1. Launching a Google Colab Notebook We're going to perform the text paraphrasing on the cloud using Google Colab, which is an online version of the Jupyter notebook that allows you to run Python code on the cloud. If you're new to Google Colab, you will want to brush up on the basics in the Introductory notebook.

  2. How to Paraphrase Text using Transformers in Python

    def get_paraphrased_sentences(model, tokenizer, sentence, num_return_sequences=5, num_beams=5): # tokenize the text to be form of a list of token IDs inputs = tokenizer([sentence], truncation=True, padding="longest", return_tensors="pt") # generate the paraphrased sentences outputs = model.generate( **inputs, num_beams=num_beams, num_return_sequ...

  3. Paraphrase Text in Python Using NLP Libraries

    1. Install the Required Libraries We need to install four libraries before we can get started. Open your Colab notebook and type out the following in the first code cell: !pip install...

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    Feb 20 2022 7 min read. Build a Text Paraphraser Using Python with Pegasus Transformer for NLP written on plain background Table of Content What is the Pegasus transformer model? How to Build a Text Paraphraser Using Python with Pegasus Transformer for NLP Wrap Off

  5. How to Paraphrase Text Using Python with the Help of AI Tools

    Programming Asad Shehzad | 19 Dec, 2022 How to Paraphrase Text Using Python with the Help of AI Tools Paraphrasing is a technique for expressing ideas with different words to achieve clarity and uniqueness. Paraphrasing can be done manually or by using paraphrasing tools that are fueled with Python and AI on the backend.

  6. sentence-transformers · PyPI

    We recommend Python 3.8 or higher, PyTorch 1.11.0 or higher and transformers v4.32. or higher. The code does not work with Python 2.7. Install with pip. Install the sentence-transformers with pip: ... Paraphrase Mining; Translated Sentence Mining; Semantic Search; Retrieve & Re-Rank; Text Summarization;

  7. SentenceTransformers Documentation

    SentenceTransformers Documentation. SentenceTransformers is a Python framework for state-of-the-art sentence, text and image embeddings. The initial work is described in our paper Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. You can use this framework to compute sentence / text embeddings for more than 100 languages.

  8. How to paraphrase text in Python using transformers

    344 Share 14K views 2 years ago Data Science Toolbox In this video, I will show you how to use the PEGASUS model from Google Research to paraphrase text. Particularly, we will be using the...

  9. How paraphrasing tools use NLP libraries in Python to Generate Text

    Throughout the post, we will provide detailed examples and code snippets to help you get started with paraphrasing text using NLP libraries in Python. Whether you are a beginner or an experienced programmer, this post will provide you with the knowledge and skills you need to paraphrase content using NLP libraries in Python effectively.

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    In this video, I will show you how to use the PARROT library to paraphrase text in Python. Essentially PARROT is a pre-trained paraphrase based utterance aug...

  11. How To Paraphrase Your Text Using Python? |3 Suggested Paraphrasing Tools

    How to Paraphrase Your Text Using Python? 3 Suggested Paraphrasing Tools

  12. BART for Paraphrasing with Simple Transformers

    The authors note that training BART with text infilling yields the most consistently strong performance across many tasks.. For the task we are interested in, namely paraphrasing, the pre-trained BART model can be fine-tuned directly using the input sequence (original phrase) and the target sequence (paraphrased sentence) as a Sequence-to-Sequence model.

  13. How to Paraphrase Text Using ML Algorithms in Python?

    So, let's begin and try to paraphrase a text with Python ML algorithms. Step — 1: The transformer that we are going to use in this process is the Pegasus transformer. We are also going to use ...

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    Here are a few examples of Python paraphrases with proper code examples and outputs: 1. Original code: python def calculate_square(n): return n ** 2 result = calculate_square(5) print(result) Output: 25 Paraphrased code: python calculate_square = lambda n: n ** 2 result = calculate_square(5) print(result) Output: 25

  15. paraphrase · GitHub Topics · GitHub

    Sentence paraphrase generation at the sentence level deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight-data-science paraphrase-generation insight-ai insight-artificial-intelligence paraphraser Updated on Dec 7, 2022 Python L-Zhe / CoRPG Star 24 Code Issues Pull requests

  16. How to Paraphrase Text using Python

    Python's paraphrasing capabilities are not only limited to English text. ... you can import the paraphraser module into your Python script using the following code: from nltk.paraphrase import * ... a source sentence and a target sentence. The source sentence is the sentence that you want to paraphrase. The target sentence is the sentence ...

  17. paraphrase-generation · GitHub Topics · GitHub

    Sentence paraphrase generation at the sentence level deep-learning tensorflow lstm sentence-generator lstm-neural-networks bidirectional-lstm paraphrase paraphrases insight-data-science paraphrase-generation insight-ai insight-artificial-intelligence paraphraser Updated on Dec 7, 2022 Python iamaaditya / neural-paraphrase-generation Star 176 Code

  18. Paraphrasing using NLTK approach in Python

    1 Answer Sorted by: 6 The error is in synonyms (): lemmas is a class method of Synset, and name is a class method of Lemma. This means that you have to call them explicitly as functions by supplying () as well, like so:

  19. paraphraser · PyPI

    Released: Sep 16, 2020 A python module which returns list of sentences with same contextual meaning as given sentence Project description Paraphraser This is a python library which generates list of sentences with similar contextual meaning as given input sentence. Installation Run the following snippet to install the library in your system

  20. How to Use AI Tools to Paraphrase Text Using Python

    Here is the Example code for paraphrase text using python. import nltk from nltk.tokenize import word_tokenize from nltk.corpus import wordnet

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    Basically what you will be doing is parsing a sentence into its words, building back its meaning by grouping words that function together, saving these to variables, and then matching them with sentence templates where they may fit. The parser will make data trees (possibly many trees) Share. Follow.

  22. Code Rephraser

    Automated code rephrasing is the process of using tools or software to automatically paraphrase code, based on predefined rules or patterns. This can help to save time and effort when rephrasing large or complex codebases, and can help to ensure that the rephrasing is consistent and effective.

  23. sentence_transformers how to create text Python

    The point of the embeddings (and training objective of the sentence-transformer models, generally) is to recognize paraphrased sentences.Note that these are discriminative models, not generative ones! This means, a model will be able to tell you when two sentences are likely paraphrased (which can be done by looking at the similarity of the two embeddings), but you are not able to actually ...