recommenders
NLTK
| recommenders | NLTK | |
|---|---|---|
| 6 | 64 | |
| 18,019 | 13,035 | |
| 1.0% | 0.8% | |
| 9.5 | 8.1 | |
| 5 days ago | 17 days ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
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.
recommenders
- My kernel dies when I fit my LightFm model from Microsoft Recommenders
- There is framework for everything.
-
This Week in Python
recommenders – Best Practices on Recommendation Systems
-
Input to SVD, SAR, NMF
I would like to do a benchmarking on the Microsoft models SVD, SAR and NMF (available here: https://github.com/microsoft/recommenders) but with this input data I get a precision and recall close to zero. Any ideas how I can improve this? For SVD and NMF (surprise library) the model wants a rating input that is normally distributed, which it not the case for my binary data where the transactions all have a rating of 1.
-
Opinion on choice of model - Recommender System
Then I tried to find some more advanced models and I found this really good list and in there I found the Microsoft one. So it's' where we are now, which a bunch of different models and not a documentation/tutorials out there.
NLTK
-
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/
-
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.
-
Best Portfolio Projects for Data Science
NLTK Documentation
- Where to start learning NLP ?
-
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.
-
[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.
-
Transition to ml, starting with LLM
If not, start with Python's Natural Language Toolkit.
-
Learning resources for NLP
Try https://www.nltk.org it runs you through the basics. The book is here
-
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/
-
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?
metarank - A low code Machine Learning personalized ranking service for articles, listings, search results, recommendations that boosts user engagement. A friendly Learn-to-Rank engine
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
azure-devops-python-api - Azure DevOps Python API
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
python-minecraft-clone - Source code for each episode of my Minecraft clone in Python YouTube tutorial series.
bert - TensorFlow code and pre-trained models for BERT
TensorRec - A TensorFlow recommendation algorithm and framework in Python.
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
polyglot - Multilingual text (NLP) processing toolkit
Google-rank-tracker - SEO: Python script + shell script and cronjob to check ranks on a daily basis
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)