awesome-embedding-models
A curated list of embedding models tutorials, projects and communities.
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Decision brief
Curated resources on embedding models for AI applications
Good fit when
- Need a variety of tutorials and projects focused specifically on embedding models
- Interested in machine learning and natural language processing with embedding focus
Avoid when
- Looking for a tool that provides direct model training capabilities instead of resources
- Seeking detailed code implementations rather than a curated list of existing work
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
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Install
git clone https://github.com/Hironsan/awesome-embedding-modelsSimilar tools
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Repository that aggregates resources on different types of embedding models used in various AI applications with a focus on machine learning and natural language processing.
Capability facts
- Languages
- jupyter notebook
Source: github.language · Aug 22, 2026
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README
awesome-embedding-models
A curated list of awesome embedding models tutorials, projects and communities. Please feel free to pull requests to add links.
Table of Contents
- Papers
- Researchers
- Courses and Lectures
- Datasets
- Implementations and Tools
Papers
Word Embeddings
Word2vec, GloVe, FastText
- Efficient Estimation of Word Representations in Vector Space (2013), T. Mikolov et al. [pdf]
- Distributed Representations of Words and Phrases and their Compositionality (2013), T. Mikolov et al. [pdf]
- word2vec Parameter Learning Explained (2014), Xin Rong [pdf]
- word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method (2014), Yoav Goldberg, Omer Levy [pdf]
- GloVe: Global Vectors for Word Representation (2014), J. Pennington et al. [pdf]
- Improving Word Representations via Global Context and Multiple Word Prototypes (2012), EH Huang et al. [pdf]
- Enriching Word Vectors with Subword Information (2016), P. Bojanowski et al. [pdf]
- Bag of Tricks for Efficient Text Classification (2016), A. Joulin et al. [pdf]
Language Model
- Semi-supervised sequence tagging with bidirectional language models (2017), Peters, Matthew E., et al. [pdf]
- Deep contextualized word representations (2018), Peters, Matthew E., et al. [pdf]
- Contextual String Embeddings for Sequence Labeling (2018), Akbik, Alan, Duncan Blythe, and Roland Vollgraf. [pdf]
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018), [pdf]
Embedding Enhancement
- Sentence Embedding:Learning Semantic Sentence Embeddings using Pair-wise Discriminator(2018),Patro et al.[Project Page] [Paper]
- Retrofitting Word Vectors to Semantic Lexicons (2014), M. Faruqui et al. [pdf]
- Better Word Representations with Recursive Neural Networks for Morphology (2013), T.Luong et al. [pdf]
- Dependency-Based Word Embeddings (2014), Omer Levy, Yoav Goldberg [pdf]
- Not All Neural Embeddings are Born Equal (2014), F. Hill et al. [pdf]
- Two/Too Simple Adaptations of Word2Vec for Syntax Problems (2015), W. Ling[pdf]
Comparing count-based vs predict-based method
- Linguistic Regularities in Sparse and Explicit Word Representations (2014), Omer Levy, Yoav Goldberg[pdf]
- Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014), M. Baroni [pdf]
- Improving Distributional Similarity with Lessons Learned from Word Embeddings (2015), Omer Levy [pdf]
Evaluation, Analysis
For agents
This page has a .md twin and JSON over the API.