Blind-App-Reviews VS awesome-nlp

Compare Blind-App-Reviews vs awesome-nlp and see what are their differences.

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP) (by keon)
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Blind-App-Reviews awesome-nlp
1 3
14 16,074
- -
10.0 4.3
about 2 years ago 6 months ago
MIT License Creative Commons Zero v1.0 Universal
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Blind-App-Reviews

Posts with mentions or reviews of Blind-App-Reviews. We have used some of these posts to build our list of alternatives and similar projects.

awesome-nlp

Posts with mentions or reviews of awesome-nlp. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-09-29.

What are some alternatives?

When comparing Blind-App-Reviews and awesome-nlp you can also consider the following projects:

awesome-sentiment-analysis - Repository with all what is necessary for sentiment analysis and related areas

awesome-self-reference - A curated list of examples of self-reference in art, science, and technology

R-text-data - List of textual data sources to be used for text mining in R

lxml - The lxml XML toolkit for Python

financial-news-dataset - Reuters and Bloomberg

OpenCV - Open Source Computer Vision Library

wikipron - Massively multilingual pronunciation mining

nlp_course - YSDA course in Natural Language Processing

astropy - Astronomy and astrophysics core library

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

nlp-tutorial - Natural Language Processing Tutorial for Deep Learning Researchers

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python