transformers-interpret
nlp
| transformers-interpret | nlp | |
|---|---|---|
| 3 | 1 | |
| 1,212 | 0 | |
| - | - | |
| 2.9 | 6.0 | |
| 8 months ago | 10 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | - |
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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
nlp
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[N] State of GPT by Andrej karpathy in MSBuild 2023
GitHub: https://github.com/iliyaML/nlp/tree/main/microsoft-build-2023/state-of-gpt
What are some alternatives?
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
ocrpy - OCR, Archive, Index and Search: Implementation agnostic OCR framework.
small-text - Active Learning for Text Classification in Python
pytorch-sentiment-analysis - Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
happy-transformer - Happy Transformer makes it easy to fine-tune and perform inference with NLP Transformer models.
PLOD-AbbreviationDetection - This repository contains the PLOD Dataset for Abbreviation Detection released with our LREC 2022 publication
gensim - Topic Modelling for Humans
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
spaCy - 💫 Industrial-strength Natural Language Processing (NLP) in Python
adaptnlp - An easy to use Natural Language Processing library and framework for predicting, training, fine-tuning, and serving up state-of-the-art NLP models.
shap - A game theoretic approach to explain the output of any machine learning model. [Moved to: https://github.com/shap/shap]
tokenizers - 💥 Fast State-of-the-Art Tokenizers optimized for Research and Production