19 best Python NLP libraries in 2024

Natural language processing is critical for developing intelligent systems. If you want to train your model faster and make it more relevant for users, developers must use data gathered from the real world.

And for that, NLP libraries in Python are the most obvious choice. Python is one of the hottest programming languages across the globe because of its flexibility and features, and its ability to integrate with other languages. It is also highly acclaimed in the AI community and has grown to become one of the most sought-after languages for NLP (which, being a part of AI, relies heavily on machine learning). 

So, without any further ado, let’s take a look at some of the best Python libraries for natural language processing. Spacy is a professional-grade Python library for advanced NLP. Built on top of Python and Cython, it’s your no-frills go-to library for large-scale information extraction. Gensim is, again, one of the best Python libraries for natural language processing in terms of Topic Modelling. It offers memory-independent implementation capabilities and excels at retrieving information. With more than 47k stars on Github, Transformers offers thousands of pre-trained models to be implemented on texts for classification, translation, extraction, question answering, and summarizing in more than 100 languages. You can quickly download the APIs and start using them on any given text.

transformers:  

• It helps to capture contextual information in a sequence of words.  
• It provides thousands of pre-trained models to perform tasks on different modalities. Those are such as text, vision, and audio.  
• This has improved tasks like text classification, language translation, and sentiment analysis.

bert:  

• It enhances various tasks like text classification, named entity recognition, and sentiment analysis.  
• It is a new method of pre-training language representations.   
• It obtains state-of-the-art results on a wide array of NLP tasks.

spaCy:  

• SpaCy is crucial in the NLP landscape due to its efficiency, accuracy, and design.  
• It excels in processing large volumes of text. It offers robust tokenization, part-of-speech tagging, named entity recognition, and dependency parsing.  
• Its pre-trained models, like en_core_web_sm, provide a solid foundation for various NLP tasks.

NLP-progress:  

• It is crucial for advancing language understanding in various applications.  
• It enables more accurate sentiment analysis, improved machine translation, and enhanced chatbot interactions.  
• Continuous development empowers researchers and developers to create innovative solutions. It makes language technology more accessible and effective.

Paddle:  

• Paddle is a deep-learning platform that includes a library for NLP.  
• PaddleNLP, the NLP library within Paddle, offers pre-trained models and efficient training pipelines.  
• It supports various NLP tasks. Also, it contributes to the ease and effectiveness of NLP research and applications.

rasa:  

• It is an open-source framework that helps with building conversational AI.  
• Being open source, Rasa encourages collaboration and community contributions.  
• It allows developers to customize and extend the framework according to their needs.

gensim:  

• It provides efficient tools for topic modeling, document similarity analysis, and other tasks.  
• It focuses on scalability and performance. This makes it suitable for handling large text corpora and valuable for apps.  
• It supports popular algorithms like Word2Vec, Doc2Vec, and LDA.

flair:  

• It refers to a combination of techniques and models.   
• It enhances the performance of various NLP tasks.  
• Flair's contextual embeddings help address some of the limitations of traditional word embeddings.

pix2code:  

• It converts graphical user interface (GUI) designs into executable code.  
• It uses computer vision techniques to interpret screenshots of GUIs.  
• It generates Code from a Graphical User Interface Screenshot.  

allennlp:  

• It provides a powerful and flexible framework.  
• With pre-built components and models, it simplifies the implementation of complex architectures.  
• Its modular design encourages the development of custom models and extensions.

nltk:  

• It provides tools and resources for working with human language data.  
• NLTK includes a collection of diverse corpora and lexical resources.  
• NLTK is often used as an educational tool in NLP courses.

numpy-ml:  

• It helps in Artificial Intelligence, Machine Learning and Deep Learning.  
• It is also used in Pytorch, Tensorflow, Neural Network applications.  
• You can install it using 'pip install numpy-ml' or download it from GitHub, PyPI.

fast-style-transfer:  

• Associated with computer vision tasks, image processing and deep learning.  
• It could refer to altering the writing style of a given text while preserving its content.  
• This is useful in generating diverse outputs for chatbots.  

neural-doodle:  

• It helps in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications.  
• It helps to turn your two-bit doodles into fine artworks with deep neural networks.  
• It generates seamless textures from photos. It then transfers style from one image to another.  

bert-as-service:  

• BERT is an NLP model developed by Google. It helps with pre-training language representations.  
• It uses an enormous amount of plain text data available on the web.  
• Trained in an unsupervised manner.

sonnet:  

• Sonnet is a deep learning library.  
• It helps to construct neural networks for many different purposes.  
• Sonnet is also simple to understand.

Keras-GAN:  

• Keras-GAN generates realistic and coherent text.  
• Keras-GAN helps with style transfer in text.  
• It enables the transformation of the style of one piece of text to mimic the style of another. 

pattern:  

• Patterns play a crucial role in NLP libraries.  
• It helps us to identify structures, relationships, and trends within language data.  
• NLP often involves analyzing and understanding text, and patterns help extract meaningful information.

TextBlob:  

• It is important in NLP because it simplifies natural language processing tasks.  
• It provides a simple API for common NLP operations. Those operations are like part-of-speech tagging, noun phrase extraction, and more.  
• Its ease of use allows quick prototyping for NLP applications.

FAQ  

1. What is Natural Language Processing (NLP)?  

NLP is a field of artificial intelligence. This focuses on the interaction between computers and humans through natural language.  

2. What does an NLP library do?  

An NLP library provides tools and functions. Those are for processing and analyzing human language. It includes tasks like text classification, sentiment analysis, and named entity recognition.  

3. Which programming languages to use with NLP libraries?  

Python is used for NLP. Also used in popular libraries include NLTK, SpaCy, and the Natural Language Toolkit.  

4. What is the role of part-of-speech tagging in NLP?  

It assigns a grammatical category to each word in a sentence. This aids in syntactic analysis.  

5. Can NLP libraries handle many languages?  

Many NLP libraries support many languages. It supports many languages with varying degrees of skill depending on the library.