transformers-interpret
happy-transformer
| transformers-interpret | happy-transformer | |
|---|---|---|
| 3 | 1 | |
| 1,212 | 500 | |
| - | - | |
| 2.9 | 9.0 | |
| 8 months ago | about 2 months ago | |
| Jupyter Notebook | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
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
happy-transformer
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GPT-Neo-125M-AID (Mia) oversight + retrained
This appears to be an actual issue with Happy Transformer judging by a GitHub issue I've found of the same problem.
What are some alternatives?
neuro-symbolic-sudoku-solver - ⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.
FARM - :house_with_garden: Fast & easy transfer learning for NLP. Harvesting language models for the industry. Focus on Question Answering.
small-text - Active Learning for Text Classification in Python
FinBERT-QA - Financial Domain Question Answering with pre-trained BERT Language Model
gensim - Topic Modelling for Humans
transformers - 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
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]
gector - Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
Vision-DiffMask - Official PyTorch implementation of Vision DiffMask, a post-hoc interpretation method for vision models.
quickai - QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.