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transformers-interpretModel explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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responsible-ai-toolboxThis project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
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hierarchical-dnn-interpretationsUsing / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)
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Transformer-MM-Explainability[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.
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expmrcExpMRC: Explainability Evaluation for Machine Reading Comprehension
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graspEssential NLP & ML, short & fast pure Python code
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MindsdbPredictive AI layer for existing databases.
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InterpretFit interpretable models. Explain blackbox machine learning.
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TensorwatchDebugging, monitoring and visualization for Python Machine Learning and Data Science
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ml-fairness-frameworkFairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
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WhiteBox-Part1In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)
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global-attribution-mappingGAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
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fastshapFast approximate Shapley values in R
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shaprExplaining the output of machine learning models with more accurately estimated Shapley values
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CARLACARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
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zennitZennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.
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