Awesome Ai For Science
Section: Data Labeling & Annotation · Programmatic data labeling and weak supervision
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Appears in 6 awesome lists
System for quickly generating training data with weak supervision. Programmatically label, build, and manage training data using labeling functions and probabilistic consensus models. Powers Snorkel Flow and used by Google, Apple, and Intel. Apache 2.0 licensed.
Section: Data Labeling & Annotation · Programmatic data labeling and weak supervision
Section: Data Enrichment · A system for quickly generating training data with weak supervision.
Section: 1. Core Frameworks & Libraries · System for quickly generating training data with weak supervision. Programmatically label, build, and manage training data using labeling functions and probabilistic consensus models. Powers Snorkel Flow and used by Google, Apple, and Intel. Apache 2.0 licensed.
Section: Data-centric AI · A system for quickly generating training data with weak supervision.
Section: snorkel (22 · 6K) - A system for quickly generating training data with weak supervision. Apache-2 · (👨💻 81 · 🔀 850 · 📦 680):
Section: Machine Learning Frameworks · A system for quickly generating training data with weak supervision
How to use the Hexagon Delegate to speed up model inference on mobile and edge devices. Also see blog post Accelerating TensorFlow Lite on Qualcomm Hexagon DSPs.
(label: good first issue) PyTorch is an open source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
Python module for building complex pipelines of batch jobs. Handles dependency resolution, workflow management, visualization, and Hadoop integration. Built at Spotify and battle-tested in production. Apache 2.0 licensed.
(formerly known as pytorch-transformers and pytorch-pretrained-bert) provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, CTRL...) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in…
"Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it…
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow. [Apache2]