RecSys_Course_AT_PoliMi
Fast_Sentence_Embeddings
| RecSys_Course_AT_PoliMi | Fast_Sentence_Embeddings | |
|---|---|---|
| 1 | 3 | |
| 348 | 603 | |
| - | - | |
| 6.5 | 0.0 | |
| 3 months ago | about 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU Affero General Public License v3.0 | GNU General Public License v3.0 only |
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RecSys_Course_AT_PoliMi
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Noob question on simple recommender
At the recommender systems course I had in my university the instructor used the following repo: github.com/MaurizioFD/RecSys_Course_AT_PoliMi. For some things that would be too slow in python it uses a Cython implementation.
Fast_Sentence_Embeddings
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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
What are some alternatives?
CSGO-Pro-Gear-Performance-and-EDA - Modeling Professional (CS:GO) Gamer's Accuracy Performance Based on Gear and Settings, and Exploratory Data Analysis.
gensim - Topic Modelling for Humans
CyRK - Runge-Kutta ODE Integrator Implemented in Cython and Numba
smaller-labse - Applying "Load What You Need: Smaller Versions of Multilingual BERT" to LaBSE
artificial-intelligence-and-machine-learning - A repository for implementation of artificial intelligence algorithm which includes machine learning and deep learning algorithm as well as classical AI search algorithm
cso-classifier - Python library that classifies content from scientific papers with the topics of the Computer Science Ontology (CSO).
FLAML - A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
kgtk - Knowledge Graph Toolkit
clip-retrieval - Easily compute clip embeddings and build a clip retrieval system with them
sentence-transformers - Sentence Embeddings with BERT & XLNet