neuro-symbolic-sudoku-solver
transformers-interpret
| neuro-symbolic-sudoku-solver | transformers-interpret | |
|---|---|---|
| 1 | 3 | |
| 66 | 1,212 | |
| - | - | |
| 0.0 | 2.9 | |
| over 2 years ago | 8 months ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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neuro-symbolic-sudoku-solver
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Neuro-Symbolic Sudoku Solver
Github: https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver
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?
MAGIST-Algorithm - Multi-Agent Generally Intelligent Simultaneous Training Algorithm for Project Zeta
small-text - Active Learning for Text Classification in Python
DiCE - Generate Diverse Counterfactual Explanations for any machine learning model.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
sudoku - Can Neural Networks Crack Sudoku?
gensim - Topic Modelling for Humans
DragGAN - Unofficial implementation of the DragGAN paper
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
TicTacToe - Tic Tac Toe game, designed to be used to train a Deep Neural Network via Reinforcement Learning (DQN). It can also be played by 2 humans and features a hard coded AI that never looses and will win if you do not do perfect play against it.
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
Agar.io_Q-Learning_AI - An experiment on the performance of homemade Q-learning AIs in Agar.io depending on their state representation and available actions
Vision-DiffMask - Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.