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Similar projects and alternatives to Fast_Sentence_Embeddings
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cso-classifier
Python library that classifies content from scientific papers with the topics of the Computer Science Ontology (CSO).
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inltk
Natural Language Toolkit for Indic Languages aims to provide out of the box support for various NLP tasks that an application developer might need
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RecSys_Course_AT_PoliMi
⚠️ [ARCHIVED] This version has been archived as of october 2024 and will not be updated anymore, please refer to the README for a link to the new version. This is the official repository for the Recommender Systems course at Politecnico di Milano.
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Fast_Sentence_Embeddings discussion
Fast_Sentence_Embeddings reviews and mentions
Posts with mentions or reviews of Fast_Sentence_Embeddings.
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and similar projects. The last one was on 2024-04-19.
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The Illustrated Word2Vec
This is a great guide.
Also - despite the fact that language model embedding [1] are currently the hot rage, good old embedding models are more than good enough for most tasks.
With just a bit of tuning, they're generally as good at many sentence embedding tasks [2], and with good libraries [3] you're getting something like 400k sentence/sec on laptop CPU versus ~4k-15k sentences/sec on a v100 for LM embeddings.
When you should use language model embeddings:
- Multilingual tasks. While some embedding models are multilingual aligned (eg. MUSE [4]), you still need to route the sentence to the correct embedding model file (you need something like langdetect). It's also cumbersome, with one 400mb file per language.
For LM embedding models, many are multilingual aligned right away.
- Tasks that are very context specific or require fine-tuning. For instance, if you're making a RAG system for medical documents, the embedding space is best when it creates larger deviations for the difference between seemingly-related medical words.
This means models with more embedding dimensions, and heavily favors LM models over classic embedding models.
1. sbert.net
2. https://collaborate.princeton.edu/en/publications/a-simple-b...
3. https://github.com/oborchers/Fast_Sentence_Embeddings
4. https://github.com/facebookresearch/MUSE
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You probably shouldn't use OpenAI's embeddings
You can find some comparisons and evaluation datasets/tasks here: https://www.sbert.net/docs/pretrained_models.html
Generally MiniLM is a good baseline. For faster models you want this library:
https://github.com/oborchers/Fast_Sentence_Embeddings
For higher quality ones, just take the bigger/slower models in the SentenceTransformers library
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[D] Unsupervised document similarity state of the art
Links: fse: https://github.com/oborchers/Fast_Sentence_Embeddings Sentence-transformers: https://github.com/oborchers/sentence-transformers
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Stats
Basic Fast_Sentence_Embeddings repo stats
3
624
0.0
over 3 years ago
oborchers/Fast_Sentence_Embeddings is an open source project licensed under GNU General Public License v3.0 only which is an OSI approved license.
The primary programming language of Fast_Sentence_Embeddings is Jupyter Notebook.
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