Interpretability for sequence generation models 🐛 🔍
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Updated
Jun 19, 2024 - Python
Interpretability for sequence generation models 🐛 🔍
Cyber Security AI Dashboard
Collection of associated files for my bachelor thesis
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.
XAI-Tris
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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 d…
Collection of NLP model explanations and accompanying analysis tools
Model interpretability for Explainable Artificial Intelligence
End-to-end toxic Russian comment classification
COVID-19 forecasting model for East Java cities using Joint Learning. My undergrad thesis.
Overview of different model interpretability libraries.
This repository contains the source code for Indoor Scene Detector, a full stack deep learning computer vision application.
Interpretability Metrics
Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).
OdoriFy is an open-source tool with multiple prediction engines. This is the source code of the webserver.
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
XAI Tutorial for the Explainable AI track in the ALPS winter school 2021
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