cheatsheets VS NLTK

Compare cheatsheets vs NLTK and see what are their differences.

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cheatsheets NLTK
126 64
7,219 12,929
0.9% 1.4%
7.1 8.3
23 days ago 9 days ago
Python Python
BSD 2-clause "Simplified" License Apache License 2.0
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.

cheatsheets

Posts with mentions or reviews of cheatsheets. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-25.

NLTK

Posts with mentions or reviews of NLTK. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-01-13.
  • Building a local AI smart Home Assistant
    11 projects | news.ycombinator.com | 13 Jan 2024
    alternatively, could we not simply split by common characters such as newlines and periods, to split it within sentences? it would be fragile with special handling required for numbers with decimal points and probably various other edge cases, though.

    there are also Python libraries meant for natural language parsing[0] that could do that task for us. I even see examples on stack overflow[1] that simply split text into sentences.

    [0]: https://www.nltk.org/

  • Sorry if this is a dumb question but is the main idea behind LLMs to output text based on user input?
    2 projects | /r/LocalLLaMA | 11 Dec 2023
    Check out https://www.nltk.org/ and work through it, it'll give you a foundational understanding of how all this works, but very basically it's just a fancy auto-complete.
  • Best Portfolio Projects for Data Science
    3 projects | dev.to | 19 Sep 2023
    NLTK Documentation
  • Can an average person learn how to build a LLM model?
    2 projects | /r/deeplearning | 23 Apr 2023
    But if you want to learn start with NLTK, it's a free course and book in python that will give you a solid foundation in NLP.
  • Show HN: SiteGPT – Create ChatGPT-like chatbots trained on your website content
    7 projects | news.ycombinator.com | 1 Apr 2023
    Not to go full "Dropbox in a weekend", but if you're technical enough to self-host, this is something you can build for yourself

    Everyone is going straight to embeddings, but it'd be easy enough to use old school NLP summarization from NLTK (https://www.nltk.org/)

    Hook that up a web scraping library like https://scrapy.org/ and get a summary of each page.

    Then embed a site map in your system prompt and use langchain (https://github.com/hwchase17/langchain) to allow GPT to query for a specific page's summary.

    -

    The point of this isn't to say that's how OP did it, but there might be people seeing stuff like this and wondering how on earth to get into it: This is something you could build in a weekend with pretty much no understanding of AI

  • Learn more about spell checkers
    2 projects | /r/nlp_knowledge_sharing | 18 Mar 2023
    Books: a. "Speech and Language Processing" by Daniel Jurafsky and James H. Martin (3rd Edition) - This book covers various aspects of natural language processing, including a section on spelling correction that provides a comprehensive introduction to the topic. b. "Foundations of Statistical Natural Language Processing" by Christopher D. Manning and Hinrich Schütze - This book provides an overview of statistical approaches in NLP, including a chapter on spelling correction. Articles: a. "How to Write a Spelling Corrector" by Peter Norvig - This article demonstrates the development of a simple spelling corrector using statistical algorithms. It's a great starting point for understanding the basics of spell checkers. (Link: https://norvig.com/spell-correct.html) b. "The Design of a Proofreading Software Service" by Michael D. Garris and James L. Blue - This article presents the design and implementation of a spelling correction system that can be integrated into various applications. (Link: https://www.nist.gov/system/files/documents/itl/iad/89403123.pdf) c. "A Fast and Flexible Spellchecker" by Atkinson, K. (2006) - This article details the design of a spell checker that uses a combination of rule-based and statistical approaches for improved performance. (Link: https://aspell.net/0.60.6.1/aspell-0.60.6.1.pdf) Online Resources: a. The Natural Language Toolkit (NLTK) - This is a popular Python library for natural language processing. It includes a spell checker module and various examples of how to use it. (Link: https://www.nltk.org/) b. SymSpell - This is an open-source spell checking library that uses a Symmetric Delete spelling correction algorithm for high performance and accuracy. The GitHub repository includes a detailed description of the algorithm and examples of how to use it. (Link: https://github.com/wolfgarbe/SymSpell) These resources should provide a solid foundation for understanding the design, algorithms, and usage of spell checkers. Happy learning!
  • 10 Coding Projects to Impress Employers and Land Your Dream Job 😎
    13 projects | dev.to | 18 Feb 2023
    Natural Language Toolkit (NLTK) - a popular library for working with human language data in Python
  • Unprompted [txt2mask] now works with Inpaint Sketch mode, can generate synonyms/antonyms, and even build custom GUIs! 🤯
    3 projects | /r/StableDiffusion | 28 Jan 2023
    Next, there's a bunch of new natural language processing features. With the power of NLTK and the Moby Thesaurus, you can now find synonyms, antonyms, hypernyms, and hyponyms for any text. Once the word databases are downloaded to your machine, an internet connection is not required to use these features.
  • Training on BERT without any 'context' just questions/answer tuples?
    2 projects | /r/LanguageTechnology | 10 Dec 2022
    (1) For large scale processing/tokenizing your data I would consider using something like NLTK or Spacy. That's if your books are already in text form. If they are scans, you'll need to use some OCR software first.
  • Estimation of text complexity
    3 projects | dev.to | 24 Nov 2022
    NLTK: for token processing

What are some alternatives?

When comparing cheatsheets and NLTK you can also consider the following projects:

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

TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

bert - TensorFlow code and pre-trained models for BERT

Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)

polyglot - Multilingual text (NLP) processing toolkit

finplot - Performant and effortless finance plotting for Python

Jieba - 结巴中文分词

Pattern - Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

manim - A community-maintained Python framework for creating mathematical animations.

SnowNLP - Python library for processing Chinese text

Fast-F1 - FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry