transformers-interpret
shap
| transformers-interpret | shap | |
|---|---|---|
| 3 | 1 | |
| 1,212 | 20,121 | |
| - | - | |
| 2.9 | 10.0 | |
| 8 months ago | 8 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | MIT License |
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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
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.
What are some alternatives?
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
csgo-impact-rating - A probabilistic player rating system for Counter Strike: Global Offensive, powered by machine learning
small-text - Active Learning for Text Classification in Python
lime - Lime: Explaining the predictions of any machine learning classifier
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
awesome-shapley-value - Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
gensim - Topic Modelling for Humans
augmented-interpretable-models - Interpretable and efficient predictors using pre-trained language models. Scikit-learn compatible.
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
shap - A game theoretic approach to explain the output of any machine learning model.
Vision-DiffMask - Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models