AnnA_Anki_neuronal_Appendix
flashtext
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| AnnA_Anki_neuronal_Appendix | flashtext | |
|---|---|---|
| 3 | 8 | |
| 55 | 5,531 | |
| - | - | |
| 8.7 | 0.0 | |
| 13 days ago | 6 months ago | |
| Python | Python | |
| GNU General Public License v3.0 only | MIT License |
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AnnA_Anki_neuronal_Appendix
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Revolution? Using machine learning to handle backlog, introducing AnnA: Anki neural network appendix
ping /u/AnKingMed /u/Glutanimate Here's the link: https://github.com/thiswillbeyourgithub/AnnA_Anki_neuronal_Appendix
- Gaguing interest/ seeking help for a long term implementation of Anki along side NLP language models to revolutionize second language aquisition
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The Anki algorithm needs more research and development
It's currently on github and not at all finished. https://github.com/thiswillbeyourgithub/AMiMA_anki_mind_map/settings
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.
What are some alternatives?
autocards - Accelerating learning through machine-generated flashcards.
KeyBERT - Minimal keyword extraction with BERT
speed-focus-mode - Speed Focus Mode add-on for Anki
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
incremental-reading - Anki add-on providing incremental reading features
magnitude - A fast, efficient universal vector embedding utility package.
dutch-word-embeddings - Dutch word embeddings, trained on a large collection of Dutch social media messages and news/blog/forum posts.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
experimentalCardEaseFactor - Adjusts ease factor for cards individually during review in Anki in order to hit an 85% success rate.
yake - Single-document unsupervised keyword extraction
kanji-flashcard-generator - Simple script to generate flashcards for studying kanji
gensim - Topic Modelling for Humans