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- CCRss/arxiv_papers_cs
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<script src="https://cdn.jsdelivr.net/npm/d3@7.8.5/dist/d3.min.js"></script><script src="https://cdn.jsdelivr.net/npm/markmap-view@0.16.0/dist/browser/index.js"></script><script src="https://cdn.jsdelivr.net/npm/markmap-toolbar@0.16.0/dist/index.js"></script><script>(()=>{setTimeout(()=>{const{markmap:H,mm:ae}=window,W=new H.Toolbar;W.attach(ae);const we=W.render();we.setAttribute("style","position:absolute;bottom:20px;right:20px"),document.body.append(we)})})()</script><script>((o,T,c,r)=>{const g=o();window.mm=g.Markmap.create("svg#mindmap",(T||g.deriveOptions)(r),c)})(()=>window.markmap,null,{"content":"Top2Vec Model for Scientific Texts","children":[{"content":"Overview","children":[{"content":"<strong>Purpose:</strong> Analyze scientific texts for topic modeling and semantic search.","children":[],"payload":{"lines":"3,4"}},{"content":"<strong>Model:</strong> Top2Vec with Universal Sentence Encoder.","children":[],"payload":{"lines":"4,5"}},{"content":"<strong>Domain:</strong> Scientific literature, particularly UAV applications in disaster and emergency situations.","children":[],"payload":{"lines":"5,7"}}],"payload":{"lines":"2,3"}},{"content":"Installation","children":[{"content":"<code>pip install top2vec</code>","children":[],"payload":{"lines":"8,9"}},{"content":"<code>pip install tensorflow==2.8.0</code>","children":[],"payload":{"lines":"9,10"}},{"content":"<code>pip install tensorflow-probability==0.16.0</code>","children":[],"payload":{"lines":"10,12"}}],"payload":{"lines":"7,8"}},{"content":"Usage","children":[{"content":"<strong>Load Documents:</strong>","children":[{"content":"Source: arXiv articles in Computer Science.","children":[],"payload":{"lines":"14,15"}},{"content":"Time Frame: 2010 to 2023.","children":[],"payload":{"lines":"15,16"}}],"payload":{"lines":"13,16"}},{"content":"<strong>Initialize Model:</strong>","children":[{"content":"<code>Top2Vec(documents=docs, embedding_model='universal-sentence-encoder')</code>","children":[],"payload":{"lines":"17,18"}}],"payload":{"lines":"16,18"}},{"content":"<strong>Model Training:</strong>","children":[{"content":"UMAP for dimensionality reduction.","children":[],"payload":{"lines":"19,20"}},{"content":"HDBSCAN for clustering.","children":[],"payload":{"lines":"20,21"}}],"payload":{"lines":"18,21"}},{"content":"<strong>Save Model:</strong>","children":[{"content":"<code>model.save('top2vec_scientific_texts_model')</code>","children":[],"payload":{"lines":"22,24"}}],"payload":{"lines":"21,24"}}],"payload":{"lines":"12,13"}},{"content":"Thematic Groups Analysis","children":[{"content":"<strong>Example Group:</strong> \"UAV in Disasters and Emergency\"","children":[{"content":"<strong>Trend Analysis:</strong> Visualize interest over time.","children":[],"payload":{"lines":"26,27"}},{"content":"<strong>Key Metrics:</strong> Number of publications, growth rate, relative growth.","children":[],"payload":{"lines":"27,29"}}],"payload":{"lines":"25,29"}}],"payload":{"lines":"24,25"}},{"content":"Use Cases","children":[{"content":"<strong>Topic Discovery:</strong> Uncover main topics within scientific texts.","children":[],"payload":{"lines":"30,31"}},{"content":"<strong>Semantic Search:</strong> Find similar documents based on semantic content.","children":[],"payload":{"lines":"31,32"}},{"content":"<strong>Trend Analysis:</strong> Study the evolution of topics over time.","children":[],"payload":{"lines":"32,34"}}],"payload":{"lines":"29,30"}},{"content":"Data","children":[{"content":"<strong>Dataset:</strong> <a href=\"https://huggingface.co/datasets/CCRss/arxiv_papers_cs\">arxiv_papers_cs</a>","children":[{"content":"Contains scientific abstracts from arXiv.","children":[],"payload":{"lines":"36,38"}}],"payload":{"lines":"35,38"}}],"payload":{"lines":"34,35"}},{"content":"Examples","children":[{"content":"<strong>Trend Analysis Graph:</strong> Visualization of interest in \"UAV in Disasters and Emergency\".","children":[],"payload":{"lines":"39,40"}},{"content":"<strong>Key Metrics Table:</strong> Summary of publications and growth metrics for the thematic group.","children":[],"payload":{"lines":"40,42"}}],"payload":{"lines":"38,39"}},{"content":"Contributions","children":[{"content":"<strong>Feedback:</strong> Suggestions and improvements are welcome.","children":[],"payload":{"lines":"43,44"}},{"content":"<strong>Issues:</strong> Please report any issues or bugs.","children":[],"payload":{"lines":"44,46"}}],"payload":{"lines":"42,43"}}],"payload":{"lines":"0,1"}},null)</script>
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# Top2Vec Scientific Texts Model
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This repository hosts the `top2vec_scientific_texts` model, a specialized Top2Vec model trained on scientific texts for topic modeling and semantic search.
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### Key Metrics Table
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Analysis for Thematic Group: Disasters & Emergency
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| Year | Number of Publications | Growth Acceleration | Change in Number of Publications | Relative Growth |
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|-------:|-------------------------:|----------------------:|-----------------------------------:|:------------------|
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| 2010 | 19 | 0 | 0 | 0.0% |
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| 2011 | 15 | -4 | -4 | -21.05% |
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| 2012 | 28 | 17 | 13 | 86.67% |
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| 2013 | 38 | -3 | 10 | 35.71% |
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| 2014 | 28 | -20 | -10 | -26.32% |
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| 2015 | 47 | 29 | 19 | 67.86% |
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| 2016 | 63 | -3 | 16 | 34.04% |
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| 2017 | 94 | 15 | 31 | 49.21% |
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| 2018 | 173 | 48 | 79 | 84.04% |
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| 2019 | 266 | 14 | 93 | 53.76% |
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| 2020 | 337 | -22 | 71 | 26.69% |
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| 2021 | 380 | -28 | 43 | 12.76% |
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| 2022 | 453 | 30 | 73 | 19.21% |
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| 2023 | 509 | -17 | 56 | 12.36% |
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## Contributions
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