Overview
In this tutorial, we will look at some of the top NLP Frameworks used for developing NLP AI applications. NLP stands for Natural Language Processing.
Top NLP Frameworks
NLP frameworks are essential tools for developing applications and systems that involve the understanding and generation of human language. These frameworks provide pre-built modules and functionalities to process and analyze textual data.
Some of the popular NLP frameworks are as follows:
- spaCy
- Transformers (Hugging Face)
- Stanford NLP
- AllenNLP
spaCy
spaCy is an open-source Python library for Natural Language Processing. It is designed specifically for production use, focusing on efficiency and ease of use.
- Official website: https://spacy.io
Transformers (Hugging Face)
Transformers is an open-source library by Hugging Face that provides pre-trained models for various NLP tasks, such as text classification, named entity recognition, translation, and more. It is built on top of PyTorch and TensorFlow.
Transformers library provides APIs and tools to easily download and train state-of-the-art pre-trained models like BERT, GPT, etc.
- Official website: https://huggingface.co/
Stanford NLP
Stanford NLP provides a suite of NLP tools for various tasks, including part-of-speech tagging, named entity recognition, sentiment analysis, and coreference resolution.
- Official website: https://nlp.stanford.edu/software/
Allen NLP
AllenNLP is a deep-learning library for NLP research built on top of PyTorch. It provides pre-built models and components for various NLP tasks and flexibility for custom model development.
- Official website: https://allenai.org/allennlp