{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/language-models-with-pre-trained-glove-word","title":"Language Models with Pre-Trained (GloVe) Word Embeddings","arxiv_id":"1610.03759","date":"2016-10-12","proceeding":null,"authors":["Victor Makarenkov","Bracha Shapira","Lior Rokach"],"abstract":"In this work we implement a training of a Language Model (LM), using\nRecurrent Neural Network (RNN) and GloVe word embeddings, introduced by\nPennigton et al. in [1]. The implementation is following the general idea of\ntraining RNNs for LM tasks presented in [2], but is rather using Gated\nRecurrent Unit (GRU) [3] for a memory cell, and not the more commonly used LSTM\n[4].","url_abs":"http://arxiv.org/abs/1610.03759v2","url_pdf":"http://arxiv.org/pdf/1610.03759v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"language-models-with-pre-trained-glove-word","repo_url":"https://github.com/vicmak/ProofSeer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[{"method_slug":"glove","method_name":"GloVe"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}