flashtext
yake
| flashtext | yake | |
|---|---|---|
| 8 | 5 | |
| 5,597 | 1,656 | |
| - | 0.8% | |
| 0.0 | 3.0 | |
| 5 months ago | 11 months ago | |
| Python | Python | |
| MIT License | GNU General Public License v3.0 or later |
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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.
yake
- Show HN: Whisper.cpp and YAKE to Analyse Voice Reflections [iOS]
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Simplest keyword extractor
Personally I prefer using YAKE.
- What method should be used to tag specific texts, when the dataset is too small for training a model?
- Is there any YAKE (yet another keyword extractor) implementation in R? Unsupervised Approach for Automatic Keyword Extraction using Text Statistical Features.
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Alternate approaches to TF-IDF?
You can look for usage here: https://github.com/LIAAD/yake and there is also a reference section with publications for more details of how this works. From what I remember, each keyphrase candidate is assigned an aggregated score based on various features: position in the text, casing, frequency, surrounding text frequency...
What are some alternatives?
KeyBERT - Minimal keyword extraction with BERT
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
magnitude - A fast, efficient universal vector embedding utility package.
pke - Python Keyphrase Extraction module
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
simple_keyword_clusterer - A simple machine learning package to cluster keywords in higher-level groups.
gensim - Topic Modelling for Humans
scattertext - Beautiful visualizations of how language differs among document types.
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
faiss - A library for efficient similarity search and clustering of dense vectors.