Char2Vec
flashtext
| Char2Vec | flashtext | |
|---|---|---|
| 1 | 8 | |
| 13 | 5,546 | |
| - | - | |
| 0.0 | 0.0 | |
| about 1 year ago | 7 months ago | |
| Python | Python | |
| MIT License | MIT License |
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Char2Vec
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GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text
There are character embeddings that allow one to recover word embedding just by summing embeddings of individual bytes/chars in the word: https://github.com/sonlamho/Char2Vec
The encodings of LM's tokens reserve individual characters so that scrambled or new words can be encoded. And most LM's are trained on scrambled words as part of training copus, thus, they learn character-level embeddings.
Thus, basically, the paper is a very old news. This behavior is expected.
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?
magnitude - A fast, efficient universal vector embedding utility package.
KeyBERT - Minimal keyword extraction with BERT
gensim - Topic Modelling for Humans
rake-nltk - Python implementation of the Rapid Automatic Keyword Extraction algorithm using NLTK.
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
yake - Single-document unsupervised keyword extraction
dutch-word-embeddings - Dutch word embeddings, trained on a large collection of Dutch social media messages and news/blog/forum posts.
lunr.py - A Python implementation of Lunr.js 🌖
AnnA_Anki_neuronal_Appendix - Using machine learning on your anki collection to enhance the scheduling via semantic clustering and semantic similarity
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python