flashtext
cherche
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| flashtext | cherche | |
|---|---|---|
| 8 | 12 | |
| 5,531 | 311 | |
| - | - | |
| 0.0 | 4.4 | |
| 6 months ago | 9 days ago | |
| Python | Python | |
| MIT License | MIT License |
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flashtext
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Show HN: LLMs can generate valid JSON 100% of the time
I have some other comment on this thread where I point out why I don’t think it’s superficial. Would love to get your feedback on that if you feel like spending more time on this thread.
But it’s not obscure? FlashText was a somewhat popular paper at the time (2017) with a popular repo (https://github.com/vi3k6i5/flashtext). Their paper was pretty derivative of Aho-Corasick, which they cited. If you think they genuinely fucked up, leave an issue on their repo (I’m, maybe to your surprise lol, not the author).
Anyway, I’m not a fan of the whatabboutery here. I don’t think OG’s paper is up to snuff on its lit review - do you?
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[P] what is the most efficient way to pattern matching word-to-word?
The library flashtext basically creates these tries based on keywords you give it.
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What is the most efficient way to find substrings in strings?
Seems like https://github.com/vi3k6i5/flashtext would be better suited here.
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[P] Library for end-to-end neural search pipelines
I started developing this tool after using haystack. Pipelines are easier to build with cherche because of the operators. Also, cherche offers FlashText, Lunr.py retrievers that are not available in Haystack and that I needed for the project I wanted to solve. Haystack is clearly more complete but I think also more complex to use.
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How can I speed up thousands of re.subs()?
For the text part not requiring regex, https://github.com/vi3k6i5/flashtext might help
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My first NLP pipeline using SpaCy: detect news headlines with company acquisitions
Spacy for parsing the Headlines, remove stop words etc. might be ok but I think the problem is quite narrow so a set of fixed regex searches might work quite well. If regex is too slow, try: https://github.com/vi3k6i5/flashtext
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What tech do I need to learn to programmatically parse ingredients from a recipe?
I would probably use something like [flashtext](https://github.com/vi3k6i5/flashtext) which should not be too hard to port to kotlin.
- Quickest way to check that 14000 strings arent in An original string.
cherche
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[P] Semantic search
If you are interested, you can check out the documentation here: https://github.com/raphaelsty/cherche
- Minimalist semantic search with Cherche 2.0
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[D] is it time to investigate retrieval language models?
Here is a tool I made to create retriever-reader pipeline in a minute: Cherche, would recommend also Haystack on github !
- [P] Cherche - allows you to create a neural search pipeline using retrievers and pre-trained language models as rankers.
- Cherche - allows you to create a neural search pipeline using retrievers and pre-trained language models as rankers.
- GitHub - raphaelsty/cherche: Neural search
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[P] Library for end-to-end neural search pipelines
Github link Documentation Hackernews link
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Hacker News top posts: Jan 10, 2022
Neural Search for medium sized corpora\ (3 comments)
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Neural search library in Python for medium-sized corpora
https://github.com/raphaelsty/cherche
Cherche (search in French) allows you to create a neural search pipeline using retrievers and pre-trained language models as rankers. Cherche is meant to be used with small to medium sized corpora. Cherche's main strength is its ability to build diverse and end-to-end pipelines.
- Neural Search for medium sized corpora
What are some alternatives?
KeyBERT - Minimal keyword extraction with BERT
NetShears - iOS Network monitor/interceptor framework
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
primeqa - The prime repository for state-of-the-art Multilingual Question Answering research and development.
magnitude - A fast, efficient universal vector embedding utility package.
gpl - Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
mindflow - 🧠 AI-powered CLI git wrapper, boilerplate code generator, chat history manager, and code search engine to streamline your dev workflow 🌊
yake - Single-document unsupervised keyword extraction
oneline - Read a text file, one line at a time
gensim - Topic Modelling for Humans
megabots - 🤖 State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵