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Top 13 Python Word2vec Projects
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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japanese-words-to-vectors
Word2vec (word to vectors) approach for Japanese language using Gensim and Mecab.
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dutch-word-embeddings
Dutch word embeddings, trained on a large collection of Dutch social media messages and news/blog/forum posts.
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YassQueenDB
Graph database library that allows you to store, analyze, and search through your data in a graph format. By using the Universal Sentence Encoder, it provides an efficient and semantic approach to handle text data. 📚🧠🚀
Project mention: Show HN: LLMs can generate valid JSON 100% of the time | news.ycombinator.com | 2023-08-14I 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?
Project mention: GPT-4 Can Almost Perfectly Handle Unnatural Scrambled Text | news.ycombinator.com | 2023-12-03There 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.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Python Word2vec related posts
- NLP augmentation models
- [P] what is the most efficient way to pattern matching word-to-word?
- What is the most efficient way to find substrings in strings?
- How can I speed up thousands of re.subs()?
- What tech do I need to learn to programmatically parse ingredients from a recipe?
- Quickest way to check that 14000 strings arent in An original string.
- [P] pyRDF2Vec 0.2.0 is out!
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A note from our sponsor - InfluxDB
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Index
What are some of the best open-source Word2vec projects in Python? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | gensim | 15,212 |
| 2 | flashtext | 5,531 |
| 3 | scattertext | 2,197 |
| 4 | magnitude | 1,610 |
| 5 | textaugment | 370 |
| 6 | pyRDF2Vec | 240 |
| 7 | text-summarizer | 113 |
| 8 | japanese-words-to-vectors | 83 |
| 9 | dutch-word-embeddings | 41 |
| 10 | YassQueenDB | 14 |
| 11 | Char2Vec | 13 |
| 12 | recommendation-system | 10 |
| 13 | embeddings_plot | 2 |
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