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‎README.md‎

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@@ -16,9 +16,9 @@ This catalog is a collection of repositories for various Machine Learning techni
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| [rag-bootcamp][rag-repo] | This repository contains demos for various Retrieval Augmented Generation techniques using different libraries. | Cloud search via LlamaHub, Document search via LangChain, LlamaIndex for OpenAI and Cohere models, Hybrid Search via Weaviate Vector Store, Evaluation via RAGAS library, Websearch via LangChain | 3 | [Vectors 2021 Annual Report], [PubMed Doc], [Banking Deposits] | bootcamp | 2024 |
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| [finetuning-and-alignment][fa-repo] | This repository contains demos for finetuning techniques for LLMs focussed on reducing computational cost. | DDP, FSDP, Instruction Tuning, LoRA, DoRA, QLora, Supervised finetuning | 3 | [samsam], [imdb], [Bias-DeBiased] | bootcamp | 2024 |
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| [Prompt Engineering Laboratory][pe-lab-repo] | This repository contains demos for various Prompt Engineering techniques, along with examples for Bias quantification, text classification. | Stereotypical Bias Analysis, Sentiment inference, Finetuning using HF Library, Activation Generation, Train and Test Model for Activations without Prompts, RAG, ABSA, Few shot prompting, Zero shot prompting (Stochastic, Greedy, Likelihood Estimation), Role play prompting, LLM Prompt Summarization, Zero shot and few shot prompt translation, Few shot CoT, Zero shot CoT, Self-Consistent CoT prompting (Zero shot, 5-shot), Balanced Choice of Plausible Alternatives, Bootstrap Ensembling(Generation & MC formulation), Vote Ensembling | 11 | [Crows-pairs][crow-pairs-pe-lab], [sst5][sst5-pe-lab], [czarnowska templates][czar-templ-pe-lab], [cnn_dailymail], [ag_news], [Weather and sports data], [Other] | bootcamp | 2024 |
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| [bias-mitigation-unlearning][bmu-repo] | This repository contains code for the paper [Can Machine Unlearning Reduce Social Bias in Language Models?][bmu-paper] which was published at EMNLP'24 in the Industry track. <br>Authors are Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak. | PCGU, Task vectors and DPO for Machine Unlearning | 20 | [BBQ][bbq-bmu], [Stereoset][stereoset-bmu], [Link1][link1-bmu], [Link2][link2-bmu] | bootcamp | 2024 |
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| [bias-mitigation-unlearning][bmu-repo] | This repository contains code for the paper [Can Machine Unlearning Reduce Social Bias in Language Models?][bmu-paper] which was published at EMNLP'24 in the Industry track. <br>Authors are Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak. | PCGU, Task vectors and DPO for Machine Unlearning | 20 | [BBQ][bbq-bmu], [Stereoset][stereoset-bmu], [Link1][link1-bmu], [Link2][link2-bmu] | applied-research | 2024 |
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| [cyclops-workshop][cyclops-repo] | This repository contains demos for using [CyclOps] package for clinical ML evaluation and monitoring. | XGBoost | 1 | [Diabetes 130-US hospitals dataset for years 1999-2008][diabetes-cyclops] | bootcamp | 2024 |
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| [odyssey][odyssey-repo] | This is a library created with research done for the paper [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records][odyssey-paper] published at ArXiv'24. <br>Authors are Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan. | EHRMamba, XGBoost, Bi-LSTM | 1 | [MIMIC-IV] | bootcamp | 2024 |
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| [odyssey][odyssey-repo] | This is a library created with research done for the paper [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records][odyssey-paper] published at ArXiv'24. <br>Authors are Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan. | EHRMamba, XGBoost, Bi-LSTM | 1 | [MIMIC-IV] | tool | 2024 |
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| [diffusion-model-bootcamp][diffusion-repo] | This repository contains demos for various diffusion models for tabular and time series data. | TabDDPM, TabSyn, ClavaDDPM, CSDI, TSDiff | 12 | [Physionet Challenge 2012], [wiki2000] | bootcamp | 2024 |
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| [News Media Bias][nmb-repo] | This repository contains code for libraries and experiments to recognise and evaluate bias and fakeness within news media articles via LLMs. | Bias evaluation via LLMs, finetuning and data annotation via LLM for fake news detection, Supervised finetuning for debiasing sentence, NER for biased phrases via LLMS, Evaluate using DeepEval library | 4 | [News Media Bias Full data][nmb-data], [Toxigen], [Nela GT], [Debiaser data] | bootcamp | 2024 |
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| [News Media Bias Plus][nmb-plus-repo] | Continuation of News Media Bias project, this repository contains code for libraries and experiments to collect and annotate data, recognise and evaluate bias and fakeness within news media articles via LLMs and LVMs. | Bias evaluation via LLMs and VLMs, finetuning and data annotation via LLM for fake news detection, supervised finetuning for debiasing sentence, NER for biased entities via LLMS | 2 | [News Media Bias Plus Full Data][nmb-plus-full-data], [NMB Plus Named Entities][nmb-plus-entities] | bootcamp | 2024 |

‎docs/index.md‎

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</div>
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<!-- Custom styling for the hero section -->
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<style>
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.hero-section {
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position: relative;
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<div class="catalog-stats">
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</div>
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</div>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/bmu" title="Go to Repository">bias-mitigation-unlearning</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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</div>
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<p>This repository contains code for the paper [Can Machine Unlearning Reduce Social Bias in Language Models?][bmu-paper] which was published at EMNLP'24 in the Industry track. <br>Authors are Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak.</p>
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<div class="tag-container">
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<span class="tag" data-tippy="PCGU">PCGU</span>
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<span class="tag" data-tippy="Task vectors and DPO for Machine Unlearning">Task vectors and DPO for Machine Unlearning</span>
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</div>
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<div class="datasets">
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<strong>Datasets:</strong> <span class="dataset-tag">BBQ</span> <span class="dataset-tag">bbq-bmu</span> <span class="dataset-tag">Stereoset</span> <span class="dataset-tag">stereoset-bmu</span> <span class="dataset-tag">Link1</span> <span class="dataset-tag">link1-bmu</span> <span class="dataset-tag">Link2</span> <span class="dataset-tag">link2-bmu</span>
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</div>
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</div>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/cyclops" title="Go to Repository">cyclops-workshop</a></h3>
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<span class="tag year-tag">2024</span>
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</div>
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</div>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/odyssey" title="Go to Repository">odyssey</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">bootcamp</span>
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</div>
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<p>This is a library created with research done for the paper [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records][odyssey-paper] published at ArXiv'24. <br>Authors are Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan.</p>
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<div class="tag-container">
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<span class="tag" data-tippy="EHRMamba">EHRMamba</span>
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<span class="tag" data-tippy="XGBoost">XGBoost</span>
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<span class="tag" data-tippy="Bi-LSTM">Bi-LSTM</span>
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</div>
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<div class="datasets">
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<strong>Datasets:</strong> <span class="dataset-tag">MIMIC-IV</span>
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</div>
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</div>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/diffusion" title="Go to Repository">diffusion-model-bootcamp</a></h3>
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</div>
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=== "tool"
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<div class="grid cards" markdown>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/odyssey" title="Go to Repository">odyssey</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">tool</span>
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</div>
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<p>This is a library created with research done for the paper [EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records][odyssey-paper] published at ArXiv'24. <br>Authors are Adibvafa Fallahpour, Mahshid Alinoori, Wenqian Ye, Xu Cao, Arash Afkanpour, Amrit Krishnan.</p>
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<div class="tag-container">
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<span class="tag" data-tippy="EHRMamba">EHRMamba</span>
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<span class="tag" data-tippy="XGBoost">XGBoost</span>
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<span class="tag" data-tippy="Bi-LSTM">Bi-LSTM</span>
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</div>
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<div class="datasets">
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<strong>Datasets:</strong> <span class="dataset-tag">MIMIC-IV</span>
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</div>
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</div>
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</div>
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=== "applied-research"
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<div class="grid cards" markdown>
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<div class="card" markdown>
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<div class="header">
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<h3><a href="https://github.com/VectorInstitute/bmu" title="Go to Repository">bias-mitigation-unlearning</a></h3>
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<span class="tag year-tag">2024</span>
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<span class="tag type-tag">applied-research</span>
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</div>
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<p>This repository contains code for the paper [Can Machine Unlearning Reduce Social Bias in Language Models?][bmu-paper] which was published at EMNLP'24 in the Industry track. <br>Authors are Omkar Dige, Diljot Arneja, Tsz Fung Yau, Qixuan Zhang, Mohammad Bolandraftar, Xiaodan Zhu, Faiza Khan Khattak.</p>
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<div class="tag-container">
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<span class="tag" data-tippy="PCGU">PCGU</span>
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<span class="tag" data-tippy="Task vectors and DPO for Machine Unlearning">Task vectors and DPO for Machine Unlearning</span>
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</div>
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<div class="datasets">
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<strong>Datasets:</strong> <span class="dataset-tag">BBQ</span> <span class="dataset-tag">bbq-bmu</span> <span class="dataset-tag">Stereoset</span> <span class="dataset-tag">stereoset-bmu</span> <span class="dataset-tag">Link1</span> <span class="dataset-tag">link1-bmu</span> <span class="dataset-tag">Link2</span> <span class="dataset-tag">link2-bmu</span>
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</div>
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</div>
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</div>
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‎scripts/sync_readme_to_docs.py‎

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# Ensure we have CSS for dataset tags and type tags
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# Ensure we have CSS for dataset tags, type tags, year tags, and hero section
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css_for_tags = """
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<style>
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.hero-section {
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position: relative;
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padding: 5rem 4rem;
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text-align: center;
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color: white;
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background-color: var(--md-primary-fg-color);
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background-image: linear-gradient(rgba(0, 0, 0, 0.35), rgba(0, 0, 0, 0.35)), url('assets/splash.png');
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background-size: cover;
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background-position: center;
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display: flex;
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justify-content: center;
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align-items: center;
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margin: 0;
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padding: 0;
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width: 100%;
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position: relative;
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min-height: 70vh;
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}
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.hero-content {
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max-width: 800px;
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z-index: 10;
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}
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.hero-content h1 {
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font-size: 3rem;
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margin-bottom: 1rem;
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text-shadow: 0 2px 8px rgba(0,0,0,0.7);
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font-weight: 600;
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letter-spacing: 0.5px;
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color: #ffffff;
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font-family: 'Roboto', sans-serif;
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}
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text-shadow: 0 2px 6px rgba(0,0,0,0.7);
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max-width: 700px;
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margin-left: auto;
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margin-right: auto;
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line-height: 1.4;
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color: #f8f8f8;
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font-family: 'Roboto', sans-serif;
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font-weight: 300;
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"""
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