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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A curated list of awesome embedding models tutorials, projects and communities.
| Date | Stars |
|---|---|
| 2026-07-24 | 1845 |
| 2026-07-25 | 1845 |
| 2026-07-28 | 1845 |
| 2026-07-30 | 1845 |
| 2026-07-31 | 1849 |
| 2026-08-06 | 1849 |
Today
— stars today
This week
+4 stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.22%/day
# awesome-embedding-models[](https://github.com/sindresorhus/awesome) A curated list of awesome embedding models tutorials, projects and communities. Please feel free to pull requests to add links. ## Table of Contents * **[Papers](#papers)** * **[Researchers](#researchers)** * **[Courses and Lectures](#courses-and-lectures)** * **[Datasets](#datasets)** * **[Implementations and Tools](#implementations-and-tools)** <!--* **[Articles](#articles)**--> ## Papers ### Word Embeddings **Word2vec, GloVe, FastText** * Efficient Estimation of Word Representations in Vector Space (2013), T. Mikolov et al. [[pdf]](https://arxiv.org/pdf/1301.3781.pdf) * Distributed Representations of Words and Phrases and their Compositionality (2013), T. Mikolov et al. [[pdf]](https://arxiv.org/pdf/1310.4546.pdf) * word2vec Parameter Learning Explained (2014), Xin Rong [[pdf]](https://arxiv.org/pdf/1411.2738.pdf) * word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method (2014), Yoav Goldberg, Omer Levy [[pdf]](https://arxiv.org/pdf/1402.3722.pdf) * GloVe: Global Vectors for Word Representation (2014), J. Pennington et al. [[pdf]](http://nlp.stanford.edu/pubs/glove.pdf) * Improving Word Representations via Global Context and Multiple Word Prototypes (2012), EH Huang et al. [[pdf]](http://www.aclweb.org/anthology/P12-1092) * Enriching Word Vectors with Subword Information (2016), P. Bojanowski et al. [[pdf]](https://arxiv.org/pdf/1607.04606v1.pdf) * Bag of Tricks for Efficient Text Classification (2016), A. Joulin et al. [[pdf]](https://arxiv.org/pdf/1607.01759.pdf) **Language Model** * Semi-supervised sequence tagging with bidirectional language models (2017), Peters, Matthew E., et al. [[pdf]](https://arxiv.org/abs/1705.00108) * Deep contextualized word representations (2018), Peters, Matthew E., et al. [[pdf]](https://arxiv.org/abs/1802.05365) * Contextual String Embeddings for Sequence Labeling (2018), Akbik, Alan, Duncan Blythe, and Roland Vollgraf. [[pdf]](http://alanakbik.github.io/papers/coling2018.pdf) * BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018), [[pdf]](https://arxiv.org/abs/1810.04805) **Embedding Enhancement** * Sentence Embedding:Learning Semantic Sentence Embeddings using Pair-wise Discriminator(2018),Patro et al.[[Project Page]](https://badripatro.github.io/Question-Paraphrases/) [[Paper]](https://www.aclweb.org/anthology/C18-1230) * Retrofitting Word Vectors to Semantic Lexicons (2014), M. Faruqui et al. [[pdf]](https://arxiv.org/pdf/1411.4166.pdf) * Better Word Representations with Recursive Neural Networks for Morphology (2013), T.Luong et al. [[pdf]](http://www.aclweb.org/website/old_anthology/W/W13/W13-35.pdf#page=116) * Dependency-Based Word Embeddings (2014), Omer Levy, Yoav Goldberg [[pdf]](https://levyomer.files.wordpress.com/2014/04/dependency-based-word-embeddings-acl-2014.pdf) * Not All Neural Embeddings are Born Equal (2014), F. Hill et al. [[pdf]](https://arxiv.org/pdf/1410.0718.pdf) * Two/Too Simple Adaptations of Word2Vec for Syntax Problems (2015), W. Ling[[pdf]](http://www.cs.cmu.edu/~lingwang/papers/naacl2015.pdf) **Comparing count-based vs predict-based method** * Linguistic Regularities in Sparse and Explicit Word Representations (2014), Omer Levy, Yoav Goldberg[[pdf]](https://www.cs.bgu.ac.il/~yoavg/publications/conll2014analogies.pdf) * Don’t count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors (2014), M. Baroni [[pdf]](http://www.aclweb.org/anthology/P14-1023) * Improving Distributional Similarity with Lessons Learned from Word Embeddings (2015), Omer Levy [[pdf]](http://www.aclweb.org/anthology/Q15-1016) **Evaluation, Analysis** * Evaluation methods for unsupervised word embeddings (2015), T. Schnabel [[pdf]](http://www.aclweb.org/anthology/D15-1036) * Intrinsic Evalua
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Hiroki Nakayama · Japan
32
Vinod K Kurmi · IISER Bhopal
2
Guillaume Chevalier · Canada
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:77c113242d77f7a1, topic:awesome, topic:papers, desc:curated list
matched fp:77c113242d77f7a1, topic:embeddings, readme:sentence embeddings
matched fp:77c113242d77f7a1, topic:natural-language-processing, readme:text classification