{"data":{"slug":"hironsan-awesome-embedding-models","name":"awesome-embedding-models","tagline":"A curated list of embedding models tutorials, projects and communities.","github_url":"https://github.com/Hironsan/awesome-embedding-models","owner":"Hironsan","repo":"awesome-embedding-models","owner_avatar_url":"https://avatars.githubusercontent.com/u/6737785?v=4","primary_language":"Jupyter Notebook","stars":1850,"forks":249,"topics":["awesome","embedding-models","embeddings","machine-learning","natural-language-processing","papers","word2vec"],"archived":false,"github_pushed_at":"2019-04-07T22:56:01+00:00","maintenance_label":"Dormant","stars_delta_30d":5,"url":"https://www.graphcanon.com/tools/hironsan-awesome-embedding-models","markdown_url":"https://www.graphcanon.com/tools/hironsan-awesome-embedding-models.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/hironsan-awesome-embedding-models","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=hironsan-awesome-embedding-models","description":"A curated list of awesome embedding models tutorials, projects and communities.","homepage_url":null,"license":"MIT","open_issues":3,"watchers":102,"ai_summary":"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.","readme_excerpt":"# awesome-embedding-models\nA curated list of awesome embedding models tutorials, projects and communities.\nPlease feel free to pull requests to add links.\n\n## Table of Contents\n\n\n* **[Papers](#papers)**\n* **[Researchers](#researchers)**\n* **[Courses and Lectures](#courses-and-lectures)**\n* **[Datasets](#datasets)**\n* **[Implementations and Tools](#implementations-and-tools)**\n\n\n## Papers\n### Word Embeddings\n\n**Word2vec, GloVe, FastText**\n\n* Efficient Estimation of Word Representations in Vector Space (2013), T. Mikolov et al. [[pdf]](https://arxiv.org/pdf/1301.3781.pdf)\n* Distributed Representations of Words and Phrases and their Compositionality (2013), T. Mikolov et al. [[pdf]](https://arxiv.org/pdf/1310.4546.pdf)\n* word2vec Parameter Learning Explained (2014), Xin Rong [[pdf]](https://arxiv.org/pdf/1411.2738.pdf)\n* 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)\n* GloVe: Global Vectors for Word Representation (2014), J. Pennington et al. [[pdf]](http://nlp.stanford.edu/pubs/glove.pdf)\n* Improving Word Representations via Global Context and Multiple Word Prototypes (2012), EH Huang et al. [[pdf]](http://www.aclweb.org/anthology/P12-1092)\n* Enriching Word Vectors with Subword Information (2016), P. Bojanowski et al. [[pdf]](https://arxiv.org/pdf/1607.04606v1.pdf)\n* Bag of Tricks for Efficient Text Classification (2016), A. Joulin et al. [[pdf]](https://arxiv.org/pdf/1607.01759.pdf)\n\n**Language Model**\n\n* Semi-supervised sequence tagging with bidirectional language models (2017), Peters, Matthew E., et al. [[pdf]](https://arxiv.org/abs/1705.00108)\n* Deep contextualized word representations (2018), Peters, Matthew E., et al. [[pdf]](https://arxiv.org/abs/1802.05365)\n* Contextual String Embeddings for Sequence Labeling (2018), Akbik, Alan, Duncan Blythe, and Roland Vollgraf. [[pdf]](http://alanakbik.github.io/papers/coling2018.pdf)\n* BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2018), [[pdf]](https://arxiv.org/abs/1810.04805)\n\n\n\n**Embedding Enhancement**\n\n* 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)\n* Retrofitting Word Vectors to Semantic Lexicons (2014), M. Faruqui et al. [[pdf]](https://arxiv.org/pdf/1411.4166.pdf)\n* 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)\n* Dependency-Based Word Embeddings (2014), Omer Levy, Yoav Goldberg [[pdf]](https://levyomer.files.wordpress.com/2014/04/dependency-based-word-embeddings-acl-2014.pdf)\n* Not All Neural Embeddings are Born Equal (2014), F. Hill et al. [[pdf]](https://arxiv.org/pdf/1410.0718.pdf)\n* Two/Too Simple Adaptations of Word2Vec for Syntax Problems (2015), W. Ling[[pdf]](http://www.cs.cmu.edu/~lingwang/papers/naacl2015.pdf)\n\n\n**Comparing count-based vs predict-based method**\n\n* Linguistic Regularities in Sparse and Explicit Word Representations (2014), Omer Levy, Yoav Goldberg[[pdf]](https://www.cs.bgu.ac.il/~yoavg/publications/conll2014analogies.pdf)\n* 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)\n* Improving Distributional Similarity with Lessons Learned from Word Embeddings (2015), Omer Levy [[pdf]](http://www.aclweb.org/anthology/Q15-1016)\n\n\n**Evaluation, Analysis**\n\n* Evaluation methods for unsupervised word embeddings (2015), T. Schnabel [[pdf]](http://www.aclweb.org/anthology/D15-1036)\n* Intrinsic Evaluation of Word Vectors Fails to Predict Extrinsic Performance (2016), B. Chiu [[pdf]](https://www.aclweb.org/anthology/W/W16/W16-2501.pdf)\n* Problems With Evaluation of Word Embeddings Using","github_created_at":"2016-12-05T04:16:20+00:00","created_at":"2026-07-11T11:30:05.707305+00:00","updated_at":"2026-08-22T12:00:52.061374+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"model-training","name":"Model Training","url":"https://www.graphcanon.com/categories/model-training","markdown_url":"https://www.graphcanon.com/categories/model-training.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/model-training"}],"tags":[{"slug":"embedding-models","name":"embedding-models"},{"slug":"embeddings","name":"embeddings"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"natural-language-processing","name":"natural-language-processing"},{"slug":"papers","name":"papers"},{"slug":"word2vec","name":"word2vec"}],"trust":{"provenance":{"is_fork":false,"github_id":75587993,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-22T12:00:50.840Z","maintenance":{"label":"Dormant","score":18,"methodology":"github_public_v1","releases_90d":0,"days_since_push":2693,"last_release_at":null,"stars_delta_30d":5,"open_issues_delta_30d":0},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T11:30:06.991Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-22T12:00:51.455Z"},"languages":{"value":["jupyter notebook"],"source":"github.language","observed_at":"2026-08-22T12:00:51.455Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-08-22T12:00:51.455Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":null,"constraints":null,"when_to_use":["Need a variety of tutorials and projects focused specifically on embedding models","Interested in machine learning and natural language processing with embedding focus"],"when_not_to_use":["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"],"source":"enrich:decision_facts","observed_at":"2026-07-12T16:23:31.694Z"},"constraint_facets":null,"decision_summary":[{"label":"Adopt for","value":"Curated resources on embedding models for AI applications"}]}}