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Columbia University
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Robust Speech Recognition via Large-Scale Weak Supervision
A curated list of Human Preference Datasets for LLM fine-tuning, RLHF, and eval.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
Code and documentation to train Stanford's Alpaca models, and generate the data.
intelligent-mutual-fund-prospectus-document-processing RAG project customization
Everything about note management. All in Zotero.
A third-party project that aims to facilitate the integration between Obsidian.md and Zotero, by providing a set of community plugins for both Obsidian and Zotero.
Fuzzy match SEC mutual funds with CRSP fund names to replicate Alekseev et al. (2022).
Python-based parser for parsing XBRL and iXBRL files
This repository shares the open sourced codes for replicating papers in the finance and accounting literature.
Convert Machine Learning Code Between Frameworks
提取微信聊天记录,将其导出成HTML、Word、Excel文档永久保存,对聊天记录进行分析生成年度聊天报告,用聊天数据训练专属于个人的AI聊天助手
Tool for advanced mining for content on Github
Interesting resources related to XAI (Explainable Artificial Intelligence)
A curated list of awesome responsible machine learning resources.
Open source implementation of sDTM - supervised Deep Topic Model
Leveraging BERT and c-TF-IDF to create easily interpretable topics.
Unlock your displays on your Mac! Flexible HiDPI scaling, XDR/HDR extra brightness, virtual screens, DDC control, extra dimming, PIP/streaming, EDID override and lots more!
A python package to run contextualized topic modeling. CTMs combine contextualized embeddings (e.g., BERT) with topic models to get coherent topics. Published at EACL and ACL 2021 (Bianchi et al.).
Learning structural topic modeling using the stm R package.
Print multiple stm model dashboards to a pdf file for inspection
Extract effects from estimateEffect in the stm package
Various Experiments with @bstewart's stm topic modelling package
Topic Modelling Customer Reviews with the stm package
A Shiny Application for Inspecting Structural Topic Models
Download and extract MDA section from edgar 10k forms