{"id":19070446,"url":"https://github.com/mindspore-courses/deepnlp-models-mindspore","last_synced_at":"2025-04-28T14:21:15.753Z","repository":{"id":182240859,"uuid":"665510819","full_name":"mindspore-courses/DeepNLP-models-MindSpore","owner":"mindspore-courses","description":"About MindSpore implementations of various Deep NLP models in cs-224n(Stanford Univ)","archived":false,"fork":false,"pushed_at":"2023-10-09T03:14:53.000Z","size":719,"stargazers_count":23,"open_issues_count":1,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-18T17:19:11.002Z","etag":null,"topics":["deep-learning","mindspore","nlp","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DeepNLP-models-MindSpore\n\nThe Reproduction of Open Source Code Based on DSKSD\n\n- If you're interested in DeepNLP, I strongly recommend you to work with this awesome lecture.\n\n  * \u003ca href=\"http://web.stanford.edu/class/cs224n/syllabus.html\"\u003ecs-224n-slides\u003c/a\u003e\n  * \u003ca href=\"https://www.youtube.com/watch?v=OQQ-W_63UgQ\u0026list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6\"\u003ecs-224n-videos\u003c/a\u003e\n\nThis material is not perfect but will help your study and research:) Please feel free to pull requests!!\n\n\u003chr\u003e\n\n## Contents\n\n| Model      | Links   |\n| ------------- |:-------------:| \n| 01. \u003cstrong\u003eSkip-gram-Naive-Softmax\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/01.Skip-gram-Naive-Softmax.ipynb\"\u003enotebook\u003c/a\u003e / data / \u003ca href=\"https://arxiv.org/abs/1301.3781\"\u003epaper\u003c/a\u003e] |\n| 02. \u003cstrong\u003eSkip-gram-Negative-Sampling\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/02.Skip-gram-Negative-Sampling.ipynb\"\u003enotebook\u003c/a\u003e / data / \u003ca href=\"http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf\"\u003epaper\u003c/a\u003e] |\n| 03. \u003cstrong\u003eGloVe\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/03.GloVe.ipynb\"\u003enotebook\u003c/a\u003e / data / \u003ca href=\"https://nlp.stanford.edu/pubs/glove.pdf\"\u003epaper\u003c/a\u003e] |\n| 04. \u003cstrong\u003eWindow-Classifier-for-NER\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/04.Window-Classifier-for-NER.ipynb\"\u003enotebook\u003c/a\u003e / data / paper] |\n| 05. \u003cstrong\u003eNeural-Dependancy-Parser\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/05.Neural-Dependancy-Parser.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"https://github.com/rguthrie3/DeepDependencyParsingProblemSet/tree/master/data\"\u003edata\u003c/a\u003e / \u003ca href=\"http://cs.stanford.edu/people/danqi/papers/emnlp2014.pdf\"\u003epaper\u003c/a\u003e] |\n| 06. \u003cstrong\u003eRNN-Language-Model\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/06.RNN-Language-Model.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"https://github.com/tomsercu/lstm/tree/master/data\"\u003edata\u003c/a\u003e / \u003ca href=\"https://arxiv.org/pdf/1504.00941.pdf\"\u003epaper\u003c/a\u003e] |\n| 07. \u003cstrong\u003eNeural-Machine-Translation-with-Attention\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/07.Neural-Machine-Translation-with-Attention.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"http://www.manythings.org/anki/\"\u003edata\u003c/a\u003e / \u003ca href=\"https://arxiv.org/pdf/1409.0473.pdf\"\u003epaper\u003c/a\u003e] |\n| 08. \u003cstrong\u003eCNN-for-Text-Classification\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/08.CNN-for-Text-Classification.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"http://cogcomp.org/Data/QA/QC\"\u003edata\u003c/a\u003e / \u003ca href=\"http://www.aclweb.org/anthology/D14-1181\"\u003epaper\u003c/a\u003e] |\n| 09. \u003cstrong\u003eRecursive-NN-for-Sentiment-Classification\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/09.Recursive-NN-for-Sentiment-Classification.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"https://nlp.stanford.edu/sentiment/index.html\"\u003edata\u003c/a\u003e / \u003ca href=\"https://nlp.stanford.edu/~socherr/EMNLP2013_RNTN.pdf\"\u003epaper\u003c/a\u003e] |\n| 10. \u003cstrong\u003eDynamic-Memory-Network-for-Question-Answering\u003c/strong\u003e | [\u003ca href=\"https://github.com/mindspore-courses/DeepNLP-models-MindSpore/blob/main/notebooks/10.Dynamic-Memory-Network-for-Question-Answering.ipynb\"\u003enotebook\u003c/a\u003e / \u003ca href=\"https://research.fb.com/downloads/babi/\"\u003edata\u003c/a\u003e / \u003ca href=\"https://arxiv.org/abs/1506.07285\"\u003epaper\u003c/a\u003e] |\n\n\n## Requirements\n\n- Python 3.7\n- MindSpore 2.0\n- nltk 3.2.2\n- gensim 2.2.0\n- sklearn_crfsuite\n\n\n## Getting started\n\n`git clone https://github.com/mindspore-courses/DeepNLP-models-MindSpore.git`\n\n### prepare dataset\n\n````\ncd script\nchmod u+x prepare_dataset.sh\n./prepare_dataset.sh\n````\n\n\n## Author\n\nSungdong Kim / \u003ca href=\"https://github.com/FLoutione\"\u003e@FLoutione\u003c/a\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindspore-courses%2Fdeepnlp-models-mindspore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmindspore-courses%2Fdeepnlp-models-mindspore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmindspore-courses%2Fdeepnlp-models-mindspore/lists"}