Search Results for "stanford"
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Showing 74 open source projects for "stanford"
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Stanford CoreNLP
Stanford CoreNLP, a Java suite of core NLP tools
CoreNLP is your one stop shop for natural language processing in Java! CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. CoreNLP currently supports 6 languages, Arabic, Chinese, English, French, German, and Spanish. The centerpiece of CoreNLP is the pipeline. Pipelines take in raw text,... -
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Ax
Build LLM powered Agents and "Agentic workflows"
...Also, we are big fans of the Stanford DSPy paper, and this library is the result of all of this coming together to build a powerful framework for you to build with. -
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Transformers & LLMs cheatsheet
VIP cheatsheet for Stanford's CME 295 Transformers and Large Language
Transformers & LLMs cheatsheet is an educational repository focused on transformers, large language models, and modern generative AI architectures based on Stanford CME 295 coursework. The project compiles structured notes, mathematical explanations, diagrams, and implementation references covering transformer internals, attention mechanisms, tokenization, training pipelines, and inference strategies. It is designed to help students and practitioners understand the technical foundations behind contemporary AI systems such as GPT-style models and multimodal architectures. ... -
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Pyreft
ReFT: Representation Finetuning for Language Models
PyreFT is a tool by Stanford NLP for fine-tuning transformer models with an emphasis on efficient, resource-conserving training and customizability for NLP tasks. -
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Stanford Data Miner
Tools for integration and analysis of heterogeneous immunological data
An extensive description of this system is published in the Journal of Translational Medicine (http://www.translational-medicine.com/). In brief, the system consists of two main web applications, a data integration app and a data exploration app. The data integration app is a fully custom Java "Web 2.0" product called Sherpa. Sherpa uses Seam, a platform integrating Asynchronous JavaScript and XML (AJAX), JavaServer Faces (JSF), the Java Persistence API (JPA), and Enterprise Java Beans... -
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Docspell
Assist in organizing your piles of documents
...It can learn metadata from existing documents and find things using NLP. This makes adding metadata to your documents a lot easier. For machine learning, it relies on the free (GPL) Stanford Core NLP library. -
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DSPy
DSPy: The framework for programming—not prompting—language models
Developed by the Stanford NLP Group, DSPy (Declarative Self-improving Python) is a framework that enables developers to program language models through compositional Python code rather than relying solely on prompt engineering. It facilitates the construction of modular AI systems and provides algorithms for optimizing prompts and weights, enhancing the quality and reliability of language model outputs. -
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ImplicitGlobalGrid.jl
Distributed parallelization of stencil-based GPU and CPU applications
ImplicitGlobalGrid is an outcome of a collaboration of the Swiss National Supercomputing Centre, ETH Zurich (Dr. Samuel Omlin) with Stanford University (Dr. Ludovic Räss) and the Swiss Geocomputing Centre (Prof. Yuri Podladchikov). It renders the distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid almost trivial and enables close to ideal weak scaling of real-world applications on thousands of GPUs [1, 2, 3]. ImplicitGlobalGrid relies on the Julia MPI wrapper (MPI.jl) to perform halo updates close to hardware limit and leverages CUDA-aware or ROCm-aware MPI for GPU-applications. ... -
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Tock
Tock, the open source conversational AI toolkit
Complete and autonomous NLU solution leveraging opensource libs, such as OpenNLP, Stanford, Duckling and more. Web, mobile, social networks, smart speakers and more. Create your bot once, connect it progressively to multiple channels as you need them. Simple graphical interfaces to build stories and models, manage multilingual and multichannel bots, better understand users with analytics. Program complex stories using Kotlin, Python or Node.js provided components, or integrate with any language by leveraging Tock APIs. ... -
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The Python Computer Science Degree
This is a curated list of free courses from reputable universities
...The repository focuses on Python-friendly learning paths, making it approachable for self-taught developers who want a structured academic foundation. It includes courses from reputable institutions such as Harvard, MIT, Stanford, and Princeton. The curriculum organizes learning into topics like computer science basics, programming, math, systems, theory, applications, and electives. It is useful for learners who want a serious long-term study plan without paying for a traditional degree program. -
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LabClaw
Operating Layer for LabOS (Stanford-Princeton AI Co-Scientists)
LabClaw is an open-source AI experimentation and agent orchestration platform designed to help developers build, test, and iterate on complex autonomous workflows in a controlled and modular environment. It provides a framework for composing multiple tools, prompts, and execution steps into structured pipelines that can be reused and evaluated across different scenarios. The system emphasizes experimentation, allowing users to run multiple variations of agent workflows, compare outputs, and... -
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Argilla
The open-source data curation platform for LLMs
...This feature uses vector search combined with traditional search (keyword and filter based). Argilla is free, open-source, and 100% compatible with major NLP libraries (Hugging Face transformers, spaCy, Stanford Stanza, Flair, etc.). In fact, you can use and combine your preferred libraries without implementing any specific interface. Most annotation tools treat data collection as a one-off activity at the beginning of each project. In real-world projects, data collection is a key activity of the iterative process of ML model development. ... -
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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of... -
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qpidctrl
Qt-based Control of various temperature controllers
A Qt-based application to interface to a number of different temperature controllers. Watlow 988 Series Stanford Research Systems PTC10 Oxford Cryostream Lakeshore LS3xx series -
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SUNTANS
Unstructured-grid, nonhydrostatic ocean model
The Stanford unstructured-grid, nonhydrostatic, parallel coastal ocean model. For simulation of nonhydrostatic flows at high resolution in estuaries and coastal seas. Requires a grid generator and ParMETIS (if run in parallel). -
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KEditor
An Editor to teach Programming Fundamentals using Karel Robot
This Editor is used for educational purposes in First Programming Course. It is integrated with Karel Robot library developed by Stanford. It is a secure editor with limited capabilities to enforce the students to avoid copy and paste, do it by yourself methodology. You can use Karel commands and write new functions and run your program without any hassle to configure Karel. Every program stamped with Author name which, once set, is not allowed to be changed. You need to install JDK 1.8 or above to use this editor. -
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snorkel
A system for quickly generating training data with weak supervision
The Snorkel team is now focusing their efforts on Snorkel Flow, an end-to-end AI application development platform based on the core ideas behind Snorkel. The Snorkel project started at Stanford in 2016 with a simple technical bet: that it would increasingly be the training data, not the models, algorithms, or infrastructure, that decided whether a machine learning project succeeded or failed. Given this premise, we set out to explore the radical idea that you could bring mathematical and systems structure to the messy and often entirely manual process of training data creation and management, starting by empowering users to programmatically label, build, and manage training data. ... -
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D2L.ai
Interactive deep learning book with multi-framework code
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge. This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Offers sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist. -
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Alpaca.cpp
Locally run an Instruction-Tuned Chat-Style LLM
Run a fast ChatGPT-like model locally on your device. This combines the LLaMA foundation model with an open reproduction of Stanford Alpaca a fine-tuning of the base model to obey instructions (akin to the RLHF used to train ChatGPT) and a set of modifications to llama.cpp to add a chat interface. Download the zip file corresponding to your operating system from the latest release. The weights are based on the published fine-tunes from alpaca-lora, converted back into a PyTorch checkpoint with a modified script and then quantized with llama.cpp the regular way. -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
Self-learning Computer Science is a curated, open-source guide repository designed to help learners independently study computer science topics using high-quality university-level resources. The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. ... -
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Robotics Toolbox for MATLAB
Robotics Toolbox for MATLAB
...The Toolbox uses a very general method of representing the kinematics and dynamics of serial-link manipulators as MATLAB® objects – robot objects can be created by the user for any serial-link manipulator and a number of examples are provided for well-known robots from Kinova, Universal Robotics, Rethink as well as classical robots such as the Puma 560 and the Stanford arm. The toolbox also supports mobile robots with functions for robot motion models (unicycle, bicycle), path planning algorithms (bug, distance transform, D*, PRM), kinodynamic planning (lattice, RRT), localization (EKF, particle filter), map building (EKF) and simultaneous localization and mapping (EKF), and a Simulink model a of non-holonomic vehicle. ... -
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Machine Learning cheatsheets Stanford
VIP cheatsheets for Stanford's CS 229 Machine Learning
stanford-cs-229-machine-learning is an open-source educational repository that provides illustrated cheat sheets summarizing the key concepts taught in Stanford University’s CS229 machine learning course. The project compiles concise explanations of important topics in machine learning and presents them in an accessible format that helps learners review complex ideas quickly. -
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Provides a GUI interface to grammatical structure and relations (as parsed by the Stanford Parser) of any text.Contains grammatical relation editor to modify, import, export grammatical relation definitions (tregex patterns and features).
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SPL Compiler
Student Programming Language (SPL) a subset of PL/I
Student Programming Language (SPL) compiles and executes programs written in a subset of PL/I. SPL was created by David B. Wortman in 1969 at Stanford University as a teaching tool for beginning programmers. In 1970 the compiler was modified by Robin A. Vowels at Royal Melbourne Institute of Technology to be more compatible with the PL/I language. The compiler is written in XPL using an MSP parser. XPL is described in the book "A Compiler Generator" by McKeeman, Horning and Wortman. -
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Deep Learning cheatsheets
VIP cheatsheets for Stanford's CS 230 Deep Learning
Deep Learning cheatsheets forStanford's CS 230 is an educational repository that compiles comprehensive cheat sheets, summaries, and study resources covering the core concepts taught in Stanford’s CS230 Deep Learning course. The project organizes complex machine learning topics into visually structured reference materials that simplify studying neural networks, convolutional architectures, recurrent networks, optimization strategies, and training methodologies. It was created to help...
