Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
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Updated
Aug 30, 2023 - Jupyter Notebook
Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.
Interpretability for sequence generation models 🐛 🔍
Overview of different model interpretability libraries.
Collection of NLP model explanations and accompanying analysis tools
XAI Tutorial for the Explainable AI track in the ALPS winter school 2021
Trained Neural Networks (LSTM, HybridCNN/LSTM, PyramidCNN, Transformers, etc.) & comparison for the task of Hate Speech Detection on the OLID Dataset (Tweets).
A small repository to test Captum Explainable AI with a trained Flair transformers-based text classifier.
OdoriFy is an open-source tool with multiple prediction engines. This is the source code of the webserver.
End-to-end toxic Russian comment classification
Interpretability Metrics
Cyber Security AI Dashboard
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…
Model interpretability for Explainable Artificial Intelligence
Collection of associated files for my bachelor thesis
XAI-Tris
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 anal…
Interpretable graph classifications using Graph Convolutional Neural Network
"XAI를 위한 Attribution Method 접근법 분석 및 동향 Analysis and Trend of Attribution Methods for XAI" 에서 사용한 코드와 예시를 공개
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