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Model explainability that works seamlessly with ๐ค transformers. Explain your transformers model in just 2 lines of code.
Interpretability for sequence generation models ๐ ๐
Collection of NLP model explanations and accompanying analysis tools
Overview of different model interpretability libraries.
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).
Cyber Security AI Dashboard
This repository contains the source code for Indoor Scene Detector, a full stack deep learning computer vision application.
XAI-Tris
Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model deems most likely to be labeled falsehoods, the @DeepClassiflie twitter bot tweets out a statement analysis and model interpretation "report"
OdoriFy is an open-source tool with multiple prediction engines. This is the source code of the webserver.
Interpretable graph classifications using Graph Convolutional Neural Network
Model interpretability for Explainable Artificial Intelligence
PyTorch Beginner Workshop (Brad Heintz)
"XAI๋ฅผ ์ํ Attribution Method ์ ๊ทผ๋ฒ ๋ถ์ ๋ฐ ๋ํฅ Analysis and Trend of Attribution Methods for XAI" ์์ ์ฌ์ฉํ ์ฝ๋์ ์์๋ฅผ ๊ณต๊ฐ
Collection of associated files for my bachelor thesis
Deep_classiflie_db is the backend data system for managing Deep Classiflie metadata, analyzing Deep Classiflie intermediate datasets and orchestrating Deep Classiflie model training pipelines. Deep_classiflie_db includes data scraping modules for the initial model data sources. Deep Classiflie depends upon deep_classiflie_db for much of its analytical and dataset generation functionality but the data system is currently maintained as a separate repository here to maximize architectural flexibility. Depending on how Deep Classiflie evolves (e.g. as it supports distributed data stores etc.), it may make more sense to integrate deep_classiflie_db back into deep_classiflie. Currently, deep_classiflie_db releases are synchronized to deep_classiflie releases. To learn more, visit deepclassiflie.org.
Based on the papers "Interpretability Beyond Feature Attribution: QuantitativeTestingwithConceptActivationVectors(TCAV)" and Captum's instantiation https://captum.ai/docs/captum_insights, we developed this frontend for the Captum project based on the streamlit framework.
COVID-19 forecasting model for East Java cities using Joint Learning
Interpretability Metrics