parsimonious
NLTK
| parsimonious | NLTK | |
|---|---|---|
| 5 | 64 | |
| 1,760 | 13,054 | |
| - | 1.0% | |
| 3.3 | 8.1 | |
| 4 months ago | 17 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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parsimonious
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How would I solve this?
Oh sorry, I grabbed the wrong PEG parser… I’ve used this one: https://github.com/erikrose/parsimonious
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Not Your Grandfather’s Perl
A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.
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Do programmers save chunks of code for repeated use?
I'm honestly shocked that you are being downvoted heavily for this. I was literally reading a pip module a few days ago that cites stackoverflow in the code. It may not be for code snips but it it's not wild to think that someone would do this for code they pulled from SO.
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Advent of Code 2020: Day 25 with Generators in Python
Parsimonious
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Advent of Code 2020: Day 07 using Python PEG grammars + NetworkX
Since the input comes in the form of well-formatted text with with variable-width lines, this seems a perfect fit for a PEG parser, as described in Day 04. Using a PEG parser with a node visitor also lets us process each each node as it is being parsed, saving a loop or two. As usual, I will be using the parsimonious library for Python. Importing it with from parsimonious.grammar import Grammar
NLTK
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Building a local AI smart Home Assistant
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/
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Sorry if this is a dumb question but is the main idea behind LLMs to output text based on user input?
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.
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Best Portfolio Projects for Data Science
NLTK Documentation
- Where to start learning NLP ?
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Is there a programmatic way to check if two strings are paraphrased?
If this is True, then you need also Natural Language Toolkit to process the words.
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[CROSS-POST] What programming language should I learn for corpus linguistics?
In that case, you should definitely have a look at Python's nltk library which stands for Natural Language Toolkit. They have a rich corpus collection for all kinds of specialized things like grammars, taggers, chunkers, etc.
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Transition to ml, starting with LLM
If not, start with Python's Natural Language Toolkit.
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Learning resources for NLP
Try https://www.nltk.org it runs you through the basics. The book is here
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Which programming language should I learn for NLP and computational linguistics?
In terms of programming languages, Python is a great first programming language. the learnpython subreddit has lots of good recommendations for resources to get started. Once you're comfortable with the language, NLTK would be a good place to start, and the docs have heaps of examples. Check it out https://www.nltk.org/
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Python for stock analysis?
The most popular library to do this is NLTK though I believe you can use some of the popular AI API services today as well. Bloomberg launched one.
What are some alternatives?
pip - The Python package installer
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
transitions - A lightweight, object-oriented finite state machine implementation in Python with many extensions
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
Lark - Lark is a parsing toolkit for Python, built with a focus on ergonomics, performance and modularity.
bert - TensorFlow code and pre-trained models for BERT
Bash-Utilities - A few bash scripts I've written that I wanted to share
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
NumPy - The fundamental package for scientific computing with Python.
polyglot - Multilingual text (NLP) processing toolkit
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
Jieba - 结巴中文分词