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Showing 8 open source projects for "stanford"
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Our computer monitoring software allows employees, field contractors, and freelancers to manually clock in when they begin working on an assignment. The application will take screenshots randomly or at set intervals, which allows employers to observe the work process. The application only tracks activity when the employee is clocked in. No spying, only transparency.
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Key capabilities of MYT SaaS include back-office management, unique tourism CRM, marketing tools, a central reservation system, feedback management, operation management, contact database, real-time feedback, group registration, online tour booking, and more.
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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... -
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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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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... -
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Argilla
The open-source data curation platform for LLMs
Argilla is a production-ready framework for building and improving datasets for NLP projects. Deploy your own Argilla Server on Spaces with a few clicks. Use embeddings to find the most similar records with the UI. 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... -
Aurigo builds software that helps build the world. Aurigo provides modern, cloud-based solutions for capital infrastructure and private owners to help them plan with confidence, build with quality, and maintain their assets efficiently. With more than $300b of capital programs under management, Aurigo’s solutions are trusted by over 300 customers in transportation, water and utilities, healthcare, higher education, and government on over 40,000 projects across North America. Founded in 2003, Aurigo is a privately held U.S. corporation headquartered in Austin, Texas, with global offices in Canada and India.Our award-winning Aurigo Masterworks Cloud platform offers owners full lifecycle capital program management capabilities from planning, through construction, to business operations. Aurigo helps capital program executives make better decisions based on proprietary artificial intelligence and machine learning technology.
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
... this example code for training on the Stanford Natural Language Inference (SNLI) Corpus. Now you've setup your pipeline, you may want to ensure that some functions run deterministically. Wrap any code that's random, with fork_rng and you'll be good to go. Now that you've computed your vocabulary, you may want to make use of pre-trained word vectors to set your embeddings. -
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stanford-tensorflow-tutorials
This repository contains code examples for the Stanford's course
This repository contains code examples for the course CS 20: TensorFlow for Deep Learning Research. It will be updated as the class progresses. Detailed syllabus and lecture notes can be found in the site. For this course, I use python3.6 and TensorFlow 1.4.1. -
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t2t-pipe
automatic alignment pipeline for parallel treebanks
The *Tree-to-Tree (t2t) Alignment Pipe* is a collection of python scripts, co-ordinating the process of automatic alignment of parallel treebanks from plain text files with a single call from a unix command line. Supported Languages: DE, FR, EN -
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UPDATE: Chromium is no longer updated or maintained. The project is frozen. Chromium is a flexible framework for scalable real-time rendering on clusters of workstations, derived from the Stanford WireGL project code base.
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