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A guidance language for controlling large language models.
Find and fix bugs in natural language machine learning models using adaptive testing.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Unified slicing for all Python data structures.
Beyond Accuracy: Behavioral Testing of NLP models with CheckList
SHAP (SHapley Additive exPlnation) visualization for 'XGBoost' in 'R'
Tools for training explainable models using attribution priors.
Implementation of the AIControl paper in Julia 1.0
Code for "High-Precision Model-Agnostic Explanations" paper
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning …
🔥 A well-tested feature-rich modular Firebase implementation for React Native. Supports both iOS & Android platforms for all Firebase services.
3D visualization of scientific data in Python
Fast, flexible and easy to use probabilistic modelling in Python.
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, Dask, Flink and DataFlow
Repository for the code for DeepATAC project presented at WCB workshop in ICML2017
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
A game theoretic approach to explain the output of any machine learning model.
Interpretable ML package designed to explain any machine learning model.
Lime: Explaining the predictions of any machine learning classifier
Lasso/Elastic Net linear and generalized linear models