flashtext
magnitude
| flashtext | magnitude | |
|---|---|---|
| 8 | 6 | |
| 5,597 | 1,627 | |
| - | 0.6% | |
| 0.0 | 0.0 | |
| 5 months ago | over 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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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.
magnitude
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Show HN: Wordllama – Things you can do with the token embeddings of an LLM
Interesting... looks like this uses pymagnitude
https://github.com/plasticityai/magnitude
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Text Classification Library for a Quick Baseline
(3) FastText now supports multiple languages [2].
[1] https://github.com/plasticityai/magnitude#pre-converted-magn...
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Pgvector – vector similarity search for Postgres
Check out Magnitude, we built it to solve that problem: https://github.com/plasticityai/magnitude
It's still loaded from a file, but heavily uses memory-mapping and caching to be speedy and not overload your RAM immediately. And in production scenarios, multiple worker processes can share that memory due to the memory mapping.
Disclaimer: I'm the author.
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Build an Embeddings index from a data source
General language models from pymagnitude
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Tutorial series on txtai
Backed by the pymagnitude library. Pre-trained word vectors can be installed from the referenced link.
What are some alternatives?
KeyBERT - Minimal keyword extraction with BERT
faiss - A library for efficient similarity search and clustering of dense vectors.
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
finalfusion-rust - finalfusion embeddings in Rust
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Resume-Matcher - Resume Matcher is an open source, free tool to improve your resume. It works by using language models to compare and rank resumes with job descriptions.
yake - Single-document unsupervised keyword extraction
pretty-print-confusion-matrix - Confusion Matrix in Python: plot a pretty confusion matrix (like Matlab) in python using seaborn and matplotlib
gensim - Topic Modelling for Humans
Milvus - A cloud-native vector database, storage for next generation AI applications
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
Romanian-Word-Embeddings - Romanian Word Embeddings. Here you can find pre-trained corpora of word embeddings. Current methods: CBOW, Skip-Gram, Fast-Text (from Gensim library). The .vec and .model files are available for download (all in one archive).