dgl
refinery
| dgl | refinery | |
|---|---|---|
| 4 | 20 | |
| 13,018 | 1,366 | |
| 0.8% | 0.9% | |
| 9.9 | 4.5 | |
| 9 days ago | 19 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
dgl
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
For graph embeddings, there's quite a few. I'd recommend this one, but there's also this one (disclaimer: I'm the author) or this one, more of a DGL library.
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Detecting Out-of-Distribution Datapoints via Embeddings or Predictions
For trees/graphs, you’ll want a neural net that can take these as inputs for which I’m not sure a standard library exists. One recommendation is to checkout dgl: https://github.com/dmlc/dgl
- Beyond Message Passing: A Physics-Inspired Paradigm for Graph Neural Networks
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[D] Convenient libs to use for new research project at the intersection of GNN and RL.
The best pkg for GCN - https://github.com/dmlc/dgl
refinery
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[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
You definitely forgot https://www.kern.ai/ :)
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How we used AI to automate stock sentiment classification
We will build the web scraper in Kern AI workflow, labeled our news articles in refinery, and then enrich the data with gates AI. After that, we will use workflow again to send out the predictions and the enriched data via a webhook to Slack. If you'd like to follow along or explore these tools on your own, you can join our waitlist here: https://www.kern.ai/
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German's NLP startup Kern AI has raised €2.7M in seed funding to accelerate its recent growth
A platform has been developed by the German startup Kern AI for NLP developers and data scientists to not only control the labeling process but also automate and orchestrate tangential tasks and enable them to address low-quality data that comes their way. Several companies exist substantively to power this labeling process.
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Why and how we started Kern AI (our seed funding announcement)
Fast forward to July ‘22 (after many further product iterations and a full redesign), we open-sourced our product under a new name: Kern AI refinery (the origin of the name is very simple: we want to improve, i.e., refine, the foundation for building models).
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GPT and BERT: A Comparison of Transformer Architectures
Get it for free here: https://github.com/code-kern-ai/refinery
- Open-source tool to label, assess and maintain natural language data. Treat training data like a software artifact!
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Drastically decrease the size of your Docker application
Containers are amazing for building applications. Because they allow you to pack up a programm together with all it's dependencies and execute it wherever you like. That is why our application consists of 20+ individual containers, forming our data-centric IDE for NLP, which you can check out here: https://github.com/code-kern-ai/refinery.
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Introducing bricks, an open-source content-library for NLP
Today we launched bricks, an open-source library which provides enrichments for your natural language processing projects. Our main goal with bricks is to shorten the amount of time that you need from idea to implementation. Bricks also seamlessly integrates into our main tool, the Kern AI refinery.
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How to fine-tune your embeddings for better similarity search
This blog post will share our experience with fine-tuning sentence embeddings on a commonly available dataset using similarity learning. We additionally explore how this could benefit the labeling workflow in the Kern AI refinery. To understand this post, you should know what embeddings are and how they are generated. A rough idea of what fine-tuning is also helps. All the code and data referenced in this post is available on GitHub.
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Build for Hugging Face, Rasa or Sklearn
We've built our open-source IDE for data-centric NLP with the belief that data scientists and engineers know best what kind of framework they want to use for their model building. Today, we'll show you three new adapters for the SDK.
What are some alternatives?
pytorch_geometric - Graph Neural Network Library for PyTorch
sqlx - 🧰 The Rust SQL Toolkit. An async, pure Rust SQL crate featuring compile-time checked queries without a DSL. Supports PostgreSQL, MySQL, and SQLite.
pytorch_geometric_temporal - PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
fiftyone - The open-source tool for building high-quality datasets and computer vision models
torchdrug - A powerful and flexible machine learning platform for drug discovery
dbs-tools - Perl tools to transform account / transaction data from DBS Bank into proper CSV
spektral - Graph Neural Networks with Keras and Tensorflow 2.
azuredatastudio - Azure Data Studio is a data management and development tool with connectivity to popular cloud and on-premises databases. Azure Data Studio supports Windows, macOS, and Linux, with immediate capability to connect to Azure SQL and SQL Server. Browse the extension library for more database support options including MySQL, PostgreSQL, and MongoDB.
deep_gcns_torch - Pytorch Repo for DeepGCNs (ICCV'2019 Oral, TPAMI'2021), DeeperGCN (arXiv'2020) and GNN1000(ICML'2021): https://www.deepgcns.org
serde_postgres - Easily Deserialize Postgres rows.
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
cleanlab - The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.