This repository contains the scripts for downloading and validating scripts for the documents

Related tags

Deep LearningHC4
Overview

HC4: HLTCOE CLIR Common-Crawl Collection

This repository contains the scripts for downloading and validating scripts for the documents. Document ids, topics, and qrel files are in resources/hc4/

Required packages for the scripts are recorded in requirements.txt.

Topics and Qrels

Topics are stored in jsonl format and located in resources/hc4. The language(s) the topic is annotated for is recored in the language_with_qrels field. We provide the English topic title and description for all topics and human translation for the languages that it has qrels for. We also provide machine translation of them in all three languages for all topics. Narratives(field narratives) are all in English and has one entry for each of the languages that has qrels. Each topic also has an English report(field report) that is designed to record the prior knowledge the searcher has.

Qrels are stored in the classic TREC style located in resources/hc4/{lang}.

Download Documents

To download the documents from Common Crawl, please use the following command. If you plan to use HC4 with ir_datasets, please specify ~/.ir_datasets/hc4 as the storage or make a soft link to to the directory you wish to store the documents. The document ids and hashs are stored in resources/hc4/{lang}/ids*.jsonl.gz. Russian document ids are separated into 8 files.

python download_documents.py --storage ./data/ \
                             --zho ./resources/hc4/zho/ids.jsonl.gz \
                             --fas ./resources/hc4/fas/ids.jsonl.gz \
                             --rus ./resources/hc4/rus/ids.*.jsonl.gz \
                             --jobs 4 \
                             --check_hash 

If you wish to only download the documents for one language, just specify the id file for the language you wish to download. We encourage using the flag --check_hash to varify the documents downloaded match with the documents we intend to use in the collection. The full description of the arguments can be found when execute with the --help flag.

Validate

After documents are downloaded, please run the validate_hc4_documents.py to verify all documents are downloaded for each language.

python validate_hc4_documents.py --hc4_file ./data/zho/hc4_docs.jsonl \
                                 --id_file ./resources/hc4/zho/ids.jsonl.gz \
                                 --qrels ./resources/hc4/zho/*.qrels.v1-0.txt

Reference

If you use this collection, please kindly cite our dataset paper with the following bibtex entry.

@inproceedings{hc4,
	author = {Dawn Lawrie and James Mayfield and Douglas W. Oard and Eugene Yang},
	title = {{HC4}: A New Suite of Test Collections for Ad Hoc {CLIR}},
	booktitle = {Proceedings of the 44th European Conference on Information Retrieval (ECIR)},
	year = {2022}
}
Owner
JHU Human Language Technology Center of Excellence
JHU Human Language Technology Center of Excellence
FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation.

FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [Project] [Paper] [arXiv] [Home] Official implementation of FastFCN:

Wu Huikai 815 Dec 29, 2022
Artificial Intelligence search algorithm base on Pacman

Pacman Search Artificial Intelligence search algorithm base on Pacman Source The Pacman Projects by the University of California, Berkeley. Layouts Di

Day Fundora 6 Nov 17, 2022
DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation

DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation By Qing Xu, Wenting Duan and Na He Requirements pytorch==1.1

Qing Xu 20 Dec 09, 2022
Wav2Vec for speech recognition, classification, and audio classification

Soxan در زبان پارسی به نام سخن This repository consists of models, scripts, and notebooks that help you to use all the benefits of Wav2Vec 2.0 in your

Mehrdad Farahani 140 Dec 15, 2022
PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework."

PyTorch Implementation of SSTN for Hyperspectral Image Classification Paper links: SSTN published on IEEE T-GRS. Also, you can directly find the imple

Zilong Zhong 54 Dec 19, 2022
Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses‘

Graph-based joint model with Nonignorable Missingness (GNM) This is a Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Lear

Fan Zhou 2 Apr 17, 2022
Quasi-Dense Similarity Learning for Multiple Object Tracking, CVPR 2021 (Oral)

Quasi-Dense Tracking This is the offical implementation of paper Quasi-Dense Similarity Learning for Multiple Object Tracking. We present a trailer th

ETH VIS Research Group 327 Dec 27, 2022
BMW TechOffice MUNICH 148 Dec 21, 2022
Code for the RA-L (ICRA) 2021 paper "SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition"

SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition [ArXiv+Supplementary] [IEEE Xplore RA-L 2021] [ICRA 2021 YouTube Video]

Sourav Garg 63 Dec 12, 2022
PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

PPLNN is a Primitive Library for Neural Network is a high-performance deep-learning inference engine for efficient AI inferencing

943 Jan 07, 2023
Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++).

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for vox

Diagnostic Image Analysis Group 32 Dec 08, 2022
Python utility to generate filesystem content for Obsidian.

Security Vault Generator Quickly parse, format, and output common frameworks/content for Obsidian.md. There is a strong focus on MITRE ATT&CK because

Justin Angel 73 Dec 02, 2022
Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond

Nils Thuerey 1.3k Jan 08, 2023
Reinforcement learning for self-driving in a 3D simulation

SelfDrive_AI Reinforcement learning for self-driving in a 3D simulation (Created using UNITY-3D) 1. Requirements for the SelfDrive_AI Gym You need Pyt

Surajit Saikia 17 Dec 14, 2021
Pytorch implementation of ICASSP 2022 paper Attention Probe: Vision Transformer Distillation in the Wild

Attention Probe: Vision Transformer Distillation in the Wild Jiahao Wang, Mingdeng Cao, Shuwei Shi, Baoyuan Wu, Yujiu Yang In ICASSP 2022 This code is

IIGROUP 6 Sep 21, 2022
Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have undergone breast cancer surgery.

Patient-Survival - Using Python, I developed a Machine Learning model using classification techniques such as Random Forest and SVM classifiers to predict a patient's survival status that have underg

Nafis Ahmed 1 Dec 28, 2021
Official code for paper "Demystifying Local Vision Transformer: Sparse Connectivity, Weight Sharing, and Dynamic Weight"

Demysitifing Local Vision Transformer, arxiv This is the official PyTorch implementation of our paper. We simply replace local self attention by (dyna

138 Dec 28, 2022
Scales, Chords, and Cadences: Practical Music Theory for MIR Researchers

ISMIR-musicTheoryTutorial This repository has slides and Jupyter notebooks for the ISMIR 2021 tutorial Scales, Chords, and Cadences: Practical Music T

Johanna Devaney 58 Oct 11, 2022
code for paper -- "Seamless Satellite-image Synthesis"

Seamless Satellite-image Synthesis by Jialin Zhu and Tom Kelly. Project site. The code of our models borrows heavily from the BicycleGAN repository an

Light 14 Apr 05, 2022
Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge

Neural Lexicon Reader: Reduce Pronunciation Errors in End-to-end TTS by Leveraging External Textual Knowledge This is an implementation of the paper,

Mutian He 19 Oct 14, 2022