shap
transformers-interpret
shap | transformers-interpret | |
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1 | 3 | |
20,121 | 1,212 | |
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10.0 | 2.9 | |
8 months ago | 9 months ago | |
Jupyter Notebook | Jupyter Notebook | |
MIT License | Apache License 2.0 |
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shap
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Ethical and Bias Testing in Generative AI: A Practical Guide to Ensuring Ethical Conduct with Test Cases and Tools
Other tools like Fairness Indicators, Lime, and SHAP are also valuable resources for ethical and bias testing.
transformers-interpret
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[P] XAI Recipes for the HuggingFace 🤗 Image Classification Models
Very cool, I like seeing this. I also noticed the transformers interpret package has released support for an image classification explainer: https://github.com/cdpierse/transformers-interpret
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Using LIME to explain the predictions from a BERT model, it looks like "the", "and", "or" are "very important" features, and thus I don't think the model is learning anything interesting. Any tips?
You could look at the Transformers Interpret python library: https://github.com/cdpierse/transformers-interpret
- Show HN: Transformers Interpret – Explain and visualize Transformer models
What are some alternatives?
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
lime - Lime: Explaining the predictions of any machine learning classifier
small-text - Active Learning for Text Classification in Python
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
gensim - Topic Modelling for Humans
shap - A game theoretic approach to explain the output of any machine learning model.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
Vision-DiffMask - Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.