A framework to train language models to learn invariant representations.

Overview

Invariant Language Modeling

Implementation of the training for invariant language models.

Motivation

Modern pretrained language models are critical components of NLP pipelines. Yet, they suffer from spurious correlations, poor out-of-domain generalization, and biases. Inspired by recent progress in causal machine learning, we propose invariant language modeling, a framework to learn invariant representations that should generalize across training environments. In particular, we adapt IRM-games to language models, where the invariance emerges from a specific training schedule in which environments compete to optimize their environment-specific loss by updating subsets of the model in a round-robin fashion.

Model Description

The data is assumed to come as n distinct environments and we aim to learn a language model that focusing on correlations that generalize across environments.

The model is decomposed into two components:

  • ϕ the main body of the transformer language model,
  • w the language modeling head that predicts the missing token.

In our implementation, there are now as many heads as environments: n. For each data point, all heads make their predictions and they are averaged. However, during training we sample one batch from each environment in a round-robin fashion. When seeing a batch from environment e only the head w_e and the main body ϕ receive a batch update.

Usage

To get started with the code:

pip install -r requirements.txt

PyTorch with a CUDA installation is required to run this framework. Please find all useful installation information here

Then, to continue the training of a language model from a huggingface checkpoint:

python3 run_invariant_mlm.py \
    --model_name_or_path roberta-base \
    --validation_file data-folder/validation_file.txt \
    --do_train \
    --do_eval \
    --nb_steps 5000 \
    --learning_rate 1e-5 \
    --output_dir folder-to-save-model \
    --seed 123 \
    --train_file data-folder/training-environments \
    --overwrite_cache

Currently, the supported base models are:

Implementation

To train language models according to the IRM-games, one needs to modify:

  • the training schedule to perform batch updates according to each environment in a round-robin fashion. This logic is implemented by the InvariantTrainer in invariant_trainer.py', a class inherited from the Trainer` from huggingface.
  • the language modeling heads in the model. It needs one head per environment. This is done by creating variations of the base model classes. It is implemented in invariant_roberta.py for roberta and in invariant_distilbert.py for distilbert.

Contact

Maxime Peyrard, [email protected]

Repository of our paper 'Refer-it-in-RGBD' in CVPR 2021

Refer-it-in-RGBD This is the repository of our paper 'Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images' in CVPR 2021 Pape

Haolin Liu 34 Nov 07, 2022
Code for MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks

MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks This is the code for the paper: MentorNet: Learning Data-Driven Curriculum fo

Google 302 Dec 23, 2022
Official PyTorch Implementation of Rank & Sort Loss [ICCV2021]

Rank & Sort Loss for Object Detection and Instance Segmentation The official implementation of Rank & Sort Loss. Our implementation is based on mmdete

Kemal Oksuz 229 Dec 20, 2022
level1-image-classification-level1-recsys-09 created by GitHub Classroom

level1-image-classification-level1-recsys-09 ❗ 주제 설명 COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델 ?

6 Mar 17, 2022
공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다.

ObsCare_Main 소개 공공장소에서 눈만 돌리면 CCTV가 보인다는 말이 과언이 아닐 정도로 CCTV가 우리 생활에 깊숙이 자리 잡았습니다. CCTV의 대수가 급격히 늘어나면서 관리와 효율성 문제와 더불어, 곳곳에 설치된 CCTV를 개별 관제하는 것으로는 응급 상

5 Jul 07, 2022
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree

35 Jan 03, 2023
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.

CyberBattleSim April 8th, 2021: See the announcement on the Microsoft Security Blog. CyberBattleSim is an experimentation research platform to investi

Microsoft 1.5k Dec 25, 2022
CVPR2022 (Oral) - Rethinking Semantic Segmentation: A Prototype View

Rethinking Semantic Segmentation: A Prototype View Rethinking Semantic Segmentation: A Prototype View, Tianfei Zhou, Wenguan Wang, Ender Konukoglu and

Tianfei Zhou 239 Dec 26, 2022
Adaptive Prototype Learning and Allocation for Few-Shot Segmentation (CVPR 2021)

ASGNet The code is for the paper "Adaptive Prototype Learning and Allocation for Few-Shot Segmentation" (accepted to CVPR 2021) [arxiv] Overview data/

Gen Li 91 Dec 23, 2022
Official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive

TTT++ This is an official implementation for TTT++: When Does Self-supervised Test-time Training Fail or Thrive? TL;DR: Online Feature Alignment + Str

VITA lab at EPFL 39 Dec 25, 2022
Interactive Visualization to empower domain experts to align ML model behaviors with their knowledge.

An interactive visualization system designed to helps domain experts responsibly edit Generalized Additive Models (GAMs). For more information, check

InterpretML 83 Jan 04, 2023
The code written during my Bachelor Thesis "Classification of Human Whole-Body Motion using Hidden Markov Models".

This code was written during the course of my Bachelor thesis Classification of Human Whole-Body Motion using Hidden Markov Models. Some things might

Matthias Plappert 14 Dec 06, 2022
Semi-supervised Learning for Sentiment Analysis

Neural-Semi-supervised-Learning-for-Text-Classification-Under-Large-Scale-Pretraining Code, models and Datasets for《Neural Semi-supervised Learning fo

47 Jan 01, 2023
Keras udrl - Keras implementation of Upside Down Reinforcement Learning

keras_udrl Keras implementation of Upside Down Reinforcement Learning This is me

Eder Santana 7 Jan 24, 2022
PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020).

Scaffold-Federated-Learning PyTorch implementation of SCAFFOLD (Stochastic Controlled Averaging for Federated Learning, ICML 2020). Environment numpy=

KI 30 Dec 29, 2022
Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Simple PyTorch implementations of Badnets on MNIST and CIFAR10.

Vera 75 Dec 13, 2022
Pyeventbus: a publish/subscribe event bus

pyeventbus pyeventbus is a publish/subscribe event bus for Python 2.7. simplifies the communication between python classes decouples event senders and

15 Apr 21, 2022
3ds-Ghidra-Scripts - Ghidra scripts to help with 3ds reverse engineering

3ds Ghidra Scripts These are ghidra scripts to help with 3ds reverse engineering

Zak 7 May 23, 2022
NeurIPS 2021 Datasets and Benchmarks Track

AP-10K: A Benchmark for Animal Pose Estimation in the Wild Introduction | Updates | Overview | Download | Training Code | Key Questions | License Intr

AP-10K 82 Dec 11, 2022
Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

SSRL-for-image-classification Semi-supervised Representation Learning for Remote Sensing Image Classification Based on Generative Adversarial Networks

Feng 2 Nov 19, 2021