piral
NLTK
piral | NLTK | |
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18 | 64 | |
1,626 | 13,035 | |
0.9% | 0.8% | |
9.5 | 8.1 | |
11 days ago | 15 days ago | |
TypeScript | Python | |
MIT License | Apache License 2.0 |
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piral
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Micro frontend frameworks in 2024
Piral Framework - Focused on developer experience with features like a visual UI editor, live previews and hot module replacement. Ref - https://piral.io/
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Building a Large Scale Micro-frontend Application.
Micro-frontend applications have become increasingly popular among developers thanks to their many advantages. It helps create modular and maintainable applications capable of handling complex business needs. As with any technology, implementing micro-frontends poses challenges, such as ensuring consistent APIs. But, with tools like Piral, developers can easily create and scale micro-frontend applications.
- Micro frontend
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Microfrontends: Microservices for the Frontend
Piral: implements isolated components called pilets. Pilets are modules that bundle content and behavior.
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Versioning Web Components
I've prepared a demo project on the basis of Piral. The running demo can be found at the Piral samples organization on GitHub. Running the demo locally does not look very spectacular.
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There is framework for everything.
https://bit.dev/ https://piral.io/ https://github.com/umijs/qiankun https://github.com/single-spa/single-spa
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Getting Started with Micro Frontends
3) Piral: Piral is a framework for next-gen portal applications.
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How we wrote our CLI integration tests
For the command line tooling of our micro frontend framework Piral we needed to be sure that it properly runs. This includes
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Writing "The Art Of Micro Frontends"
The idea of writing a book about micro frontends was born in mid 2019 when Piral was born out of smapiot's open-source efforts. We've been leading and assisting to micro frontend implementations for a while, and our intention was to put together an (almost) ideal pattern into an open-source framework.
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Donald Trump Hates It: Distributed Development Using Micro Frontends
Therefore, for the example, I've picked a rather fancy way to "loosely" get the micro frontends at runtime using a file called feed.json, which is created at build-time using the information which micro frontends (called pilets in this case, because I am using the Piral framework) are actually available. Therefore, just adding, e.g., a third micro frontend easily works without touching the app-shell package.
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?
single-spa - The router for easy microfrontends
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
Bit - A build system for development of composable software.
TextBlob - Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.
pixiv.moe - 😘 A pinterest-style layout site, shows illusts on pixiv.net order by popularity.
bert - TensorFlow code and pre-trained models for BERT
luigi - Micro frontend framework
Stanza - Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
Next.js - The React Framework
polyglot - Multilingual text (NLP) processing toolkit
puzzle-js - âš¡ Micro frontend framework for scalable and blazing fast websites.
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)