232 Open source projects that are alternatives of or similar to transformers-interpret

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Collection of NLP model explanations and accompanying analysis tools

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Fit interpretable models. Explain blackbox machine learning.

Molecular Explanation Generator

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

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Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.

A collection of Models, Datasets, DataModules, Callbacks, Metrics, Losses and Loggers to better integrate pytorch-lightning with transformers.

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Using Transformers from HuggingFace in R

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A Framework for Textual Entailment based Zero Shot text classification

Transformer/Transformer-XL/R-Transformer examples and explanations

Transformer based translation quality estimation

Code snippets and solutions for the Introduction to Deep Learning and Neural Networks Course hosted in educative.io

Examples of Data Science projects and Artificial Intelligence use cases

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

Easy text classification for everyone : Bert based models via Huggingface transformers (KR / EN)

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

This repository contains code for the O'Reilly Live Online Training for BERT

Implementation of STAM (Space Time Attention Model), a pure and simple attention model that reaches SOTA for video classification

CLIP (Contrastive Language–Image Pre-training) for Italian

🤗 A list of wonderful open-source projects & applications integrated with Hugging Face libraries.

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

X-Transformer: Taming Pretrained Transformers for eXtreme Multi-label Text Classification

The official repository for our paper "The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers". We significantly improve the systematic generalization of transformer models on a variety of datasets using simple tricks and careful considerations.

Unofficial JAX implementations of deep learning research papers

Essential NLP & ML, short & fast pure Python code

Visualize BERT's self-attention layers on text classification tasks

CINO: Pre-trained Language Models for Chinese Minority (少数民族语言预训练模型)

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

Use custom tokenizers in spacy-transformers

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

Neural Relation Understanding: neural cardinality estimators for tabular data

A Python multilingual toolkit for Sentiment Analysis and Social NLP tasks

This Repo containes the implemnetation of generating Guided-GradCAM for 3D medical Imaging using Nifti file in tensorflow 2.0. Different input files can be used in that case need to edit the input to the Guided-gradCAM model.

An NLP system for generating reading comprehension questions

⚙️ Solving sudoku using Deep Reinforcement learning in combination with powerful symbolic representations.

Bottleneck Transformers for Visual Recognition

NeurIPS 2018. Linear-time model comparison tests.

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

KB국민은행에서 제공하는 경제/금융 도메인에 특화된 한국어 ALBERT 모델

[AAAI 2022] This is the official PyTorch implementation of "Less is More: Pay Less Attention in Vision Transformers"

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage machine learning, deep learning and deep transfer learning to learn and solve popular tasks using NLP including NER, Classification, Recommendation \ Information Retrieval, Summarization, Classification, Language Translation, Q&A and T…

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

A collection of Danish Transformers

(TPAMI 2022) Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer

Implementation of the paper "Language-agnostic representation learning of source code from structure and context".

Powerful unsupervised domain adaptation method for dense retrieval. Requires only unlabeled corpus and yields massive improvement: "GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval" https://arxiv.org/abs/2112.07577

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020