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/r/technology top posts: Mar 1, 2021
FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.\ (0 comments)
- FACET is an open source library for human-explainable AI. It combines sophisticated model inspection and model-based simulation to enable better explanations of your supervised machine learning models.
- Human-Explainable AI
- Facet: ML model inspection and model-based simulation for better explanations
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?
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small-text - Active Learning for Text Classification in Python
shapash - 🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.
gensim - Topic Modelling for Humans
imodels - Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
Vision-DiffMask - Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.