{"id":15035998,"url":"https://github.com/msgi/nlp-journey","last_synced_at":"2026-01-25T06:43:34.620Z","repository":{"id":37733385,"uuid":"182781810","full_name":"msgi/nlp-journey","owner":"msgi","description":"Documents, papers and codes related to  Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. ","archived":false,"fork":false,"pushed_at":"2025-11-27T01:02:15.000Z","size":137,"stargazers_count":1629,"open_issues_count":0,"forks_count":377,"subscribers_count":61,"default_branch":"master","last_synced_at":"2025-11-27T18:16:03.805Z","etag":null,"topics":["deep-learning","paper"],"latest_commit_sha":null,"homepage":"https://github.com/msgi/nlp-journey","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/msgi.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-04-22T12:27:35.000Z","updated_at":"2025-11-27T01:02:19.000Z","dependencies_parsed_at":"2022-08-08T21:30:39.755Z","dependency_job_id":"bd07957b-276d-475a-958a-6ec6949a9933","html_url":"https://github.com/msgi/nlp-journey","commit_stats":{"total_commits":2,"total_committers":1,"mean_commits":2.0,"dds":0.0,"last_synced_commit":"8adabf919179f9a0e63480c44d5748f2e510993a"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/msgi/nlp-journey","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msgi%2Fnlp-journey","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msgi%2Fnlp-journey/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msgi%2Fnlp-journey/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msgi%2Fnlp-journey/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/msgi","download_url":"https://codeload.github.com/msgi/nlp-journey/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/msgi%2Fnlp-journey/sbom","scorecard":{"id":665598,"data":{"date":"2025-08-11","repo":{"name":"github.com/msgi/nlp-journey","commit":"8adabf919179f9a0e63480c44d5748f2e510993a"},"scorecard":{"version":"v5.2.1-40-gf6ed084d","commit":"f6ed084d17c9236477efd66e5b258b9d4cc7b389"},"score":3,"checks":[{"name":"Packaging","score":-1,"reason":"packaging workflow not detected","details":["Warn: no GitHub/GitLab publishing workflow detected."],"documentation":{"short":"Determines if the project is published as a package that others can easily download, install, easily update, and uninstall.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#packaging"}},{"name":"Pinned-Dependencies","score":-1,"reason":"no dependencies found","details":null,"documentation":{"short":"Determines if the project has declared and pinned the dependencies of its build process.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#pinned-dependencies"}},{"name":"Code-Review","score":0,"reason":"Found 0/2 approved changesets -- score normalized to 0","details":null,"documentation":{"short":"Determines if the project requires human code review before pull requests (aka merge requests) are merged.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#code-review"}},{"name":"Binary-Artifacts","score":10,"reason":"no binaries found in the repo","details":null,"documentation":{"short":"Determines if the project has generated executable (binary) artifacts in the source repository.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#binary-artifacts"}},{"name":"Dangerous-Workflow","score":-1,"reason":"no workflows found","details":null,"documentation":{"short":"Determines if the project's GitHub Action workflows avoid dangerous patterns.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#dangerous-workflow"}},{"name":"SAST","score":0,"reason":"no SAST tool detected","details":["Warn: no pull requests merged into dev branch"],"documentation":{"short":"Determines if the project uses static code analysis.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#sast"}},{"name":"Maintained","score":0,"reason":"0 commit(s) and 0 issue activity found in the last 90 days -- score normalized to 0","details":null,"documentation":{"short":"Determines if the project is \"actively maintained\".","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#maintained"}},{"name":"Token-Permissions","score":-1,"reason":"No tokens found","details":null,"documentation":{"short":"Determines if the project's workflows follow the principle of least privilege.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#token-permissions"}},{"name":"CII-Best-Practices","score":0,"reason":"no effort to earn an OpenSSF best practices badge detected","details":null,"documentation":{"short":"Determines if the project has an OpenSSF (formerly CII) Best Practices Badge.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#cii-best-practices"}},{"name":"Security-Policy","score":0,"reason":"security policy file not detected","details":["Warn: no security policy file detected","Warn: no security file to analyze","Warn: no security file to analyze","Warn: no security file to analyze"],"documentation":{"short":"Determines if the project has published a security policy.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#security-policy"}},{"name":"Vulnerabilities","score":10,"reason":"0 existing vulnerabilities detected","details":null,"documentation":{"short":"Determines if the project has open, known unfixed vulnerabilities.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#vulnerabilities"}},{"name":"Fuzzing","score":0,"reason":"project is not fuzzed","details":["Warn: no fuzzer integrations found"],"documentation":{"short":"Determines if the project uses fuzzing.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#fuzzing"}},{"name":"License","score":10,"reason":"license file detected","details":["Info: project has a license file: LICENSE:0","Info: FSF or OSI recognized license: Apache License 2.0: LICENSE:0"],"documentation":{"short":"Determines if the project has defined a license.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#license"}},{"name":"Signed-Releases","score":-1,"reason":"no releases found","details":null,"documentation":{"short":"Determines if the project cryptographically signs release artifacts.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#signed-releases"}},{"name":"Branch-Protection","score":0,"reason":"branch protection not enabled on development/release branches","details":["Warn: branch protection not enabled for branch 'master'"],"documentation":{"short":"Determines if the default and release branches are protected with GitHub's branch protection settings.","url":"https://github.com/ossf/scorecard/blob/f6ed084d17c9236477efd66e5b258b9d4cc7b389/docs/checks.md#branch-protection"}}]},"last_synced_at":"2025-08-21T17:55:13.261Z","repository_id":37733385,"created_at":"2025-08-21T17:55:13.261Z","updated_at":"2025-08-21T17:55:13.261Z"},"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28747201,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-25T05:12:38.112Z","status":"ssl_error","status_checked_at":"2026-01-25T05:04:50.338Z","response_time":113,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","paper"],"created_at":"2024-09-24T20:29:53.799Z","updated_at":"2026-01-25T06:43:34.614Z","avatar_url":"https://github.com/msgi.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# nlp journey\n\n[![Star](https://img.shields.io/github/stars/msgi/nlp-journey?color=success)](https://github.com/msgi/nlp-journey/)\n[![Fork](https://img.shields.io/github/forks/msgi/nlp-journey)](https://github.com/msgi/nlp-journey/fork)\n[![GitHub Issues](https://img.shields.io/github/issues/msgi/nlp-journey?color=success)](https://github.com/msgi/nlp-journey/issues)\n[![License](https://img.shields.io/badge/license-Apache%202-blue)](https://github.com/msgi/nlp-journey)\n\n\n## 0. llm chat\n\n[llm-chat](llm-chat/)\n\n![](llm-chat/imgs/llm-chat.png)\n\n## 1. Books\n\n1. Handbook of Graphical Models. [`online`](https://stat.ethz.ch/~maathuis/papers/Handbook.pdf)\n2. Deep Learning. [`online`](https://www.deeplearningbook.org/)\n3. Neural Networks and Deep Learning. [`online`](http://neuralnetworksanddeeplearning.com/)\n4. Speech and Language Processing. [`online`](http://web.stanford.edu/~jurafsky/slp3/ed3book.pdf)\n\n## 2. Papers\n\n### 01) Transformer papers\n\n1. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. [`paper`](https://arxiv.org/abs/1810.04805)\n2. GPT-2: Language Models are Unsupervised Multitask Learners. [`paper`](https://blog.openai.com/better-language-models/)\n3. Transformer-XL: Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context. [`paper`](https://arxiv.org/abs/1901.02860)\n4. XLNet: Generalized Autoregressive Pretraining for Language Understanding. [`paper`](https://arxiv.org/abs/1906.08237)\n5. RoBERTa: Robustly Optimized BERT Pretraining Approach. [`paper`](https://arxiv.org/abs/1907.11692)\n6. DistilBERT: a distilled version of BERT: smaller, faster, cheaper and lighter. [`paper`](https://arxiv.org/abs/1910.01108)\n7. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. [`paper`](https://arxiv.org/abs/1909.11942)\n8. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. [`paper`](https://arxiv.org/abs/1910.10683)\n9. ELECTRA: pre-training text encoders as discriminators rather than generators. [`paper`](https://openreview.net/pdf?id=r1xMH1BtvB)\n10. GPT3: Language Models are Few-Shot Learners. [`paper`](https://arxiv.org/pdf/2005.14165.pdf)\n\n\n### 02) Models\n\n1. LSTM(Long Short-term Memory). [`paper`](http://www.bioinf.jku.at/publications/older/2604.pdf)\n2. Sequence to Sequence Learning with Neural Networks. [`paper`](https://arxiv.org/pdf/1409.3215.pdf)\n3. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. [`paper`](https://arxiv.org/pdf/1406.1078.pdf)\n4. Residual Network(Deep Residual Learning for Image Recognition). [`paper`](https://arxiv.org/pdf/1512.03385.pdf)\n5. Dropout(Improving neural networks by preventing co-adaptation of feature detectors). [`paper`](https://arxiv.org/pdf/1207.0580.pdf)\n6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. [`paper`](https://arxiv.org/pdf/1502.03167.pdf)\n\n### 03) Summaries\n\n1. An overview of gradient descent optimization algorithms. [`paper`](https://arxiv.org/pdf/1609.04747.pdf)\n2. Analysis Methods in Neural Language Processing: A Survey. [`paper`](https://arxiv.org/pdf/1812.08951.pdf)\n3. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. [`paper`](https://arxiv.org/pdf/1910.10683.pdf)\n4. A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications. [`paper`](https://arxiv.org/pdf/2001.06937.pdf)\n5. A Gentle Introduction to Deep Learning for Graphs. [`paper`](https://arxiv.org/pdf/1912.12693.pdf)\n6. A Survey on Deep Learning for Named Entity Recognition. [`paper`](https://arxiv.org/pdf/1812.09449.pdf)\n7. More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction. [`paper`](https://arxiv.org/pdf/2004.03186.pdf)\n8. Deep Learning Based Text Classification: A Comprehensive Review. [`paper`](https://arxiv.org/pdf/2004.03705.pdf)\n9. Pre-trained Models for Natural Language Processing: A Survey. [`paper`](https://arxiv.org/pdf/2003.08271.pdf)\n10. A Survey on Contextual Embeddings. [`paper`](https://arxiv.org/pdf/2003.07278.pdf)\n11. A Survey on Knowledge Graphs: Representation, Acquisition and Applications. [`paper`](https://arxiv.org/pdf/2002.00388.pdf)\n12. Knowledge Graphs. [`paper`](https://arxiv.org/pdf/2003.02320v2.pdf)\n13. Pre-trained Models for Natural Language Processing: A Survey. [`paper`](https://arxiv.org/pdf/2003.08271.pdf)\n\n### 04) Pre-training\n\n1. A Neural Probabilistic Language Model. [`paper`](https://www.researchgate.net/publication/221618573_A_Neural_Probabilistic_Language_Model)\n2. word2vec Parameter Learning Explained. [`paper`](https://arxiv.org/pdf/1411.2738.pdf)\n3. Language Models are Unsupervised Multitask Learners. [`paper`](https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf)\n4. An Empirical Study of Smoothing Techniques for Language Modeling. [`paper`](https://dash.harvard.edu/bitstream/handle/1/25104739/tr-10-98.pdf?sequence=1)\n5. Efficient Estimation of Word Representations in Vector Space. [`paper`](https://arxiv.org/pdf/1301.3781.pdf)\n6. Distributed Representations of Sentences and Documents. [`paper`](https://arxiv.org/pdf/1405.4053.pdf)\n7. Enriching Word Vectors with Subword Information(FastText). [`paper`](https://arxiv.org/pdf/1607.04606.pdf)\n8. GloVe: Global Vectors for Word Representation. [`online`](https://nlp.stanford.edu/projects/glove/)\n9. ELMo (Deep contextualized word representations). [`paper`](https://arxiv.org/pdf/1802.05365.pdf)\n10. Pre-Training with Whole Word Masking for Chinese BERT. [`paper`](https://arxiv.org/pdf/1906.08101.pdf)\n\n### 05) Classification\n\n1. Bag of Tricks for Efficient Text Classification (FastText). [`paper`](https://arxiv.org/pdf/1607.01759.pdf)\n2. Convolutional Neural Networks for Sentence Classification. [`paper`](https://arxiv.org/pdf/1408.5882.pdf)\n3. Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification. [`paper`](http://www.aclweb.org/anthology/P16-2034)\n\n### 06) Text generation\n\n1. A Deep Ensemble Model with Slot Alignment for Sequence-to-Sequence Natural Language Generation. [`paper`](https://arxiv.org/pdf/1805.06553.pdf)\n2. SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient. [`paper`](https://arxiv.org/pdf/1609.05473.pdf)\n\n### 07) Text Similarity\n\n1. Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks. [`paper`](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.723.6492\u0026rep=rep1\u0026type=pdf)\n2. Learning Text Similarity with Siamese Recurrent Networks. [`paper`](https://www.aclweb.org/anthology/W16-1617)\n3. A Deep Architecture for Matching Short Texts. [`paper`](http://papers.nips.cc/paper/5019-a-deep-architecture-for-matching-short-texts.pdf)\n\n### 08) QA\n\n1. A Question-Focused Multi-Factor Attention Network for Question Answering. [`paper`](https://arxiv.org/pdf/1801.08290.pdf)\n2. The Design and Implementation of XiaoIce, an Empathetic Social Chatbot. [`paper`](https://arxiv.org/pdf/1812.08989.pdf)\n3. A Knowledge-Grounded Neural Conversation Model. [`paper`](https://arxiv.org/pdf/1702.01932.pdf)\n4. Neural Generative Question Answering. [`paper`](https://arxiv.org/pdf/1512.01337v1.pdf)\n5. Sequential Matching Network A New Architecture for Multi-turn Response Selection in Retrieval-Based Chatbots．[`paper`](https://arxiv.org/abs/1612.01627)\n6. Modeling Multi-turn Conversation with Deep Utterance Aggregation．[`paper`](https://arxiv.org/pdf/1806.09102.pdf)\n7. Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network．[`paper`](https://www.aclweb.org/anthology/P18-1103)\n8. Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes. [`paper`](https://arxiv.org/pdf/1907.02848.pdf)\n\n### 09) NMT\n\n1. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. [`paper`](https://arxiv.org/pdf/1406.1078v3.pdf)\n2. Neural Machine Translation by Jointly Learning to Align and Translate. [`paper`](https://arxiv.org/pdf/1409.0473.pdf)\n3. Transformer (Attention Is All You Need). [`paper`](https://arxiv.org/pdf/1706.03762.pdf)\n\n### 10) Summary\n\n1. Get To The Point: Summarization with Pointer-Generator Networks. [`paper`](https://arxiv.org/pdf/1704.04368.pdf)\n2. Deep Recurrent Generative Decoder for Abstractive Text Summarization. [`paper`](https://aclweb.org/anthology/D17-1222)\n\n### 11) Relation extraction\n\n1. Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks. [`paper`](https://www.aclweb.org/anthology/D15-1203)\n2. Neural Relation Extraction with Multi-lingual Attention. [`paper`](https://www.aclweb.org/anthology/P17-1004)\n3. FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation. [`paper`](https://aclweb.org/anthology/D18-1514)\n4. End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures. [`paper`](https://www.aclweb.org/anthology/P16-1105)\n\n### 12) Large Language Models\n\n1. Training language models to follow instructions with human feedback. [`paper`](https://arxiv.org/pdf/2203.02155.pdf)\n2. LLaMA: Open and Efficient Foundation Language Models. [`paper`](https://arxiv.org/pdf/2302.13971.pdf)\n\n## 3. Articles\n\n- TRANSFORMERS FROM SCRATCH. [`url`](http://peterbloem.nl/blog/transformers)\n- The Illustrated Transformer.[`url`](https://jalammar.github.io/illustrated-transformer/)\n- Attention-based-model. [`url`](http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/)\n- Modern Deep Learning Techniques Applied to Natural Language Processing. [`url`](https://nlpoverview.com/)\n- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation\n.[`url`](https://towardsdatascience.com/illustrated-guide-to-lstms-and-gru-s-a-step-by-step-explanation-44e9eb85bf21)\n- Applying word2vec to Recommenders and Advertising. [`url`](http://mccormickml.com/2018/06/15/applying-word2vec-to-recommenders-and-advertising/)\n\n\n## 4. Github\n\n* CLUE. [`github`](https://github.com/CLUEbenchmark/CLUE)\n* transformers. [`github`](https://github.com/huggingface/transformers)\n* HanLP. [`github`](https://github.com/hankcs/HanLP)\n* ML-For-Beginners. [`github`](https://github.com/microsoft/ML-For-Beginners.git)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsgi%2Fnlp-journey","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmsgi%2Fnlp-journey","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmsgi%2Fnlp-journey/lists"}