pdbpp
spaCy
pdbpp | spaCy | |
---|---|---|
9 | 107 | |
1,255 | 28,887 | |
1.3% | 1.1% | |
0.0 | 9.2 | |
about 1 month ago | 7 days ago | |
Python | Python | |
BSD 3-clause "New" or "Revised" License | MIT License |
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.
pdbpp
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The new pdbp (Pdb+) Python debugger!
Why not just use Python’s built-in pdb debugger or another existing one like ipdb or pdbpp?
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Show HN: Clamshell- an experimental Python based shell
I like pdbpp. Make sure to install from source as there hasn’t been a release in a while.
https://github.com/pdbpp/pdbpp
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Useful Python Modules for us
pdbpp: Improved pdb boltons: assorted python addtions twisted: event driven networking framework sorcery: Dark magic in python, things know where+how they are being called, helps reducing boilerplate sh: Better alternative for subprocess module, much more pythonic taskipy: npm run scipt_name like functionality snoop: pdb lite, record+replay function steps birdseye: graphical debugger remote-pdb: easy pdb from inside containers typer: wrapper around click for simpler code for CLIs arrow: Always TZ aware datetimes, plus more features more-itertools: more functions for iterators pydantic: data validation + dataclasses loguru: better logging notifiers: sending notifications from python
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For whose use Emacs and VS Code, when and why you use VSCode? #emacs #vscode
If you want to use pdbpp, install it into your Python environment you're using the debugger from and it'll automatically hook itself into pdb with no additional setup.
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What Python debugger do you use?
I love pdbpp
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Which not so well known Python packages do you like to use on a regular basis and why?
pdbpp feels like getting super powers over using pdb
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What dev tools do you use in your python projects?
Most of the tools and libraries I use have been mentioned, but I haven’t seen pdb++ brought up. It’s like ipython for debugging!
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Debug in VIM
Improved version of built-in debugger: https://github.com/pdbpp/pdbpp
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Icecream: Never use print() to debug again in Python
I like to use PDB++ which is a drop in replacement for PDB
https://github.com/pdbpp/pdbpp
spaCy
- How I discovered Named Entity Recognition while trying to remove gibberish from a string.
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Step by step guide to create customized chatbot by using spaCy (Python NLP library)
Hi Community, In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):
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Best AI SEO Tools for NLP Content Optimization
SpaCy: An open-source library providing tools for advanced NLP tasks like tokenization, entity recognition, and part-of-speech tagging.
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Who has the best documentation you’ve seen or like in 2023
spaCy https://spacy.io/
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A beginner’s guide to sentiment analysis using OceanBase and spaCy
In this article, I'm going to walk through a sentiment analysis project from start to finish, using open-source Amazon product reviews. However, using the same approach, you can easily implement mass sentiment analysis on your own products. We'll explore an approach to sentiment analysis with one of the most popular Python NLP packages: spaCy.
- Retrieval Augmented Generation (RAG): How To Get AI Models Learn Your Data & Give You Answers
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Against LLM Maximalism
Spacy [0] is a state-of-art / easy-to-use NLP library from the pre-LLM era. This post is the Spacy founder's thoughts on how to integrate LLMs with the kind of problems that "traditional" NLP is used for right now. It's an advertisement for Prodigy [1], their paid tool for using LLMs to assist data labeling. That said, I think I largely agree with the premise, and it's worth reading the entire post.
The steps described in "LLM pragmatism" are basically what I see my data science friends doing — it's hard to justify the cost (money and latency) in using LLMs directly for all tasks, and even if you want to you'll need a baseline model to compare against, so why not use LLMs for dataset creation or augmentation in order to train a classic supervised model?
[0] https://spacy.io/
[1] https://prodi.gy/
- Swirl: An open-source search engine with LLMs and ChatGPT to provide all the answers you need 🌌
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How to predict this sequence?
spaCy
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What do you all think about (setq sentence-end-double-space nil)?
I chose spacy. Although it's not state of the art, it's very well established and stable.
What are some alternatives?
ipdb - Integration of IPython pdb
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
pudb - Full-screen console debugger for Python
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
pdbr - pdb + Rich library
NLTK - NLTK Source
PySnooper - Never use print for debugging again
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT
python-devtools - Dev tools for python
polyglot - Multilingual text (NLP) processing toolkit
snoop - A powerful set of Python debugging tools, based on PySnooper
textacy - NLP, before and after spaCy