The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization

Related tags

Deep LearningPRIMER
Overview

PRIMER

The official code for PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization.

PRIMER is a pre-trained model for multi-document representation with focus on summarization that reduces the need for dataset-specific architectures and large amounts of fine-tuning labeled data. With extensive experiments on 6 multi-document summarization datasets from 3 different domains on the zero-shot, few-shot and full-supervised settings, PRIMER outperforms current state-of-the-art models on most of these settings with large margins.

Set up

  1. Create new virtual environment by
conda create --name primer python=3.7
conda activate primer
conda install cudatoolkit=10.0
  1. Install Longformer by
pip install git+https://github.com/allenai/longformer.git
  1. Install requirements to run the summarization scripts and data generation scripts by
pip install -r requirements.txt

Usage of PRIMER

  1. Download the pre-trained PRIMER model here to ./PRIMER_model
  2. Load the tokenizer and model by
from transformers import AutoTokenizer
from longformer import LongformerEncoderDecoderForConditionalGeneration
from longformer import LongformerEncoderDecoderConfig

tokenizer = AutoTokenizer.from_pretrained('./PRIMER_model/')
config = LongformerEncoderDecoderConfig.from_pretrained('./PRIMER_model/')
model = LongformerEncoderDecoderForConditionalGeneration.from_pretrained(
            './PRIMER_model/', config=config)

Make sure the documents separated with <doc-sep> in the input.

Summarization Scripts

You can use script/primer_main.py for pre-train/train/test PRIMER, and script/compared_model_main.py for train/test BART/PEGASUS/LED.

Pre-training Data Generation

Newshead: we crawled the newshead dataset using the original code, and cleaned up the crawled data, the final newshead dataset can be found here.

You can use utils/pretrain_preprocess.py to generate pre-training data.

  1. Generate data with scores and entities with --mode compute_all_scores
  2. Generate pre-training data with --mode pretraining_data_with_score:
    • Pegasus: --strategy greedy --metric pegasus_score
    • Entity_Pyramid: --strategy greedy_entity_pyramid --metric pyramid_rouge

Datasets

  • For Multi-News and Multi-XScience, it will automatically download from Huggingface.
  • WCEP-10: the preprocessed version can be found here
  • Wikisum: we only use a small subset for few-shot training(10/100) and testing(3200). The subset we used can be found here. Note we have significantly more examples than we used in train.pt and valid.pt, as we sample 10/100 examples multiple times in the few-shot setting, and we need to make sure it has a large pool to sample from.
  • DUC2003/2004: You need to apply for access based on the instruction
  • arXiv: you can find the data we used in this repo
Adaptation through prediction: multisensory active inference torque control

Adaptation through prediction: multisensory active inference torque control Submitted to IEEE Transactions on Cognitive and Developmental Systems Abst

Cristian Meo 1 Nov 07, 2022
4D Human Body Capture from Egocentric Video via 3D Scene Grounding

4D Human Body Capture from Egocentric Video via 3D Scene Grounding [Project] [Paper] Installation: Our method requires the same dependencies as SMPLif

Miao Liu 37 Nov 08, 2022
A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perform basic tasks.

AI_Personal_Voice_Assistant_Using_Python A project to build an AI voice assistant using Python . The Voice assistant interacts with the humans to perf

Chumui Tripura 1 Oct 30, 2021
SSD: Single Shot MultiBox Detector pytorch implementation focusing on simplicity

SSD: Single Shot MultiBox Detector Introduction Here is my pytorch implementation of 2 models: SSD-Resnet50 and SSDLite-MobilenetV2.

Viet Nguyen 149 Jan 07, 2023
SAN for Product Attributes Prediction

SAN Heterogeneous Star Graph Attention Network for Product Attributes Prediction This repository contains the official PyTorch implementation for ADVI

Xuejiao Zhao 9 Dec 12, 2022
tf2onnx - Convert TensorFlow, Keras and Tflite models to ONNX.

tf2onnx converts TensorFlow (tf-1.x or tf-2.x), tf.keras and tflite models to ONNX via command line or python api.

Open Neural Network Exchange 1.8k Jan 08, 2023
Synthesize photos from PhotoDNA using machine learning 🌱

Ribosome Synthesize photos from PhotoDNA. See the blog post for more information. Installation Dependencies You can install Python dependencies using

Anish Athalye 112 Nov 23, 2022
The fastai deep learning library

Welcome to fastai fastai simplifies training fast and accurate neural nets using modern best practices Important: This documentation covers fastai v2,

fast.ai 23.2k Jan 07, 2023
A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

A Library for Modelling Probabilistic Hierarchical Graphical Models in PyTorch

Korbinian Pöppel 47 Nov 28, 2022
The code succinctly shows how our ensemble learning based on deep learning CNN is used for LAM-avulsion-diagnosis.

deep-learning-LAM-avulsion-diagnosis The code succinctly shows how our ensemble learning based on deep learning CNN is used for LAM-avulsion-diagnosis

1 Jan 12, 2022
Make Watson Assistant send messages to your Discord Server

Make Watson Assistant send messages to your Discord Server Prerequisites Sign up for an IBM Cloud account. Fill in the required information and press

1 Jan 10, 2022
Per-Pixel Classification is Not All You Need for Semantic Segmentation

MaskFormer: Per-Pixel Classification is Not All You Need for Semantic Segmentation Bowen Cheng, Alexander G. Schwing, Alexander Kirillov [arXiv] [Proj

Facebook Research 1k Jan 08, 2023
Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation

STCN Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [a

Rex Cheng 456 Dec 12, 2022
Acute ischemic stroke dataset

AISD Acute ischemic stroke dataset contains 397 Non-Contrast-enhanced CT (NCCT) scans of acute ischemic stroke with the interval from symptom onset to

Kongming Liang 21 Sep 06, 2022
Official code for "Mean Shift for Self-Supervised Learning"

MSF Official code for "Mean Shift for Self-Supervised Learning" Requirements Python = 3.7.6 PyTorch = 1.4 torchvision = 0.5.0 faiss-gpu = 1.6.1 In

UMBC Vision 44 Nov 21, 2022
Anonymize BLM Protest Images

Anonymize BLM Protest Images This repository automates @BLMPrivacyBot, a Twitter bot that shows the anonymized images to help keep protesters safe. Us

Stanford Machine Learning Group 40 Oct 13, 2022
Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021

Contextualized Perturbation for Textual Adversarial Attack Introduction This is a PyTorch implementation of Contextualized Perturbation for Textual Ad

cookielee77 30 Jan 01, 2023
Code for ACL 21: Generating Query Focused Summaries from Query-Free Resources

marge This repository releases the code for Generating Query Focused Summaries from Query-Free Resources. Please cite the following paper [bib] if you

Yumo Xu 28 Nov 10, 2022
Official pytorch implementation of paper Dual-Level Collaborative Transformer for Image Captioning (AAAI 2021).

Dual-Level Collaborative Transformer for Image Captioning This repository contains the reference code for the paper Dual-Level Collaborative Transform

lyricpoem 160 Dec 11, 2022
Official repository of "DeepMIH: Deep Invertible Network for Multiple Image Hiding", TPAMI 2022.

DeepMIH: Deep Invertible Network for Multiple Image Hiding (TPAMI 2022) This repo is the official code for DeepMIH: Deep Invertible Network for Multip

Junpeng Jing 67 Nov 22, 2022