Month: October 2021

Neural Network

An Open-Source Package for Neural Relation Extraction (NRE)

We have a DEMO website (http://opennre.thunlp.ai/). Try it out! OpenNRE is an open-source and extensible toolkit that provides a unified framework to implement relation extraction models. This package is designed for the following groups: New to relation extraction: We have hand-by-hand tutorials and detailed documents that can not only enable you to use relation extraction tools, but also help you better understand the research progress in this field. Developers: Our easy-to-use interface and high-performance implementation can acclerate your deployment in […]

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Text

Unsupervised text tokenizer focused on computational efficiency

YouTokenToMe is an unsupervised text tokenizer focused on computational efficiency. It currently implements fast Byte Pair Encoding (BPE) [Sennrich et al.]. Our implementation is much faster in training and tokenization than Hugging Face, fastBPE and SentencePiece. In some test cases, it is 90 times faster. Check out our benchmark results. Key advantages: Multithreading for training and tokenization The algorithm has O(N) complexity, where N is the length of training data Highly efficient implementation in C++ Python wrapper and command-line interface […]

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Deep Learning

A deep learning based natural language and speech processing platform

What is DELTA? DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper. What can DELTA do? DELTA has been used for developing several state-of-the-art algorithms for publications and    

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Python

Pip vs Conda: an in-depth comparison of Python’s two packaging systems

If you’re using Python in the world of data science or scientific computing, you will soon discover that Python has two different packaging systems: pip and Conda. Which raises some questions: How are they different? What are the tradeoffs between the two? Which should you use? While it’s not possible to answer this question for every situation, in this article you will learn the basic differences, constrained to: Python only; Conda has support for other languages but I won’t go […]

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Semi-supervised

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Preprocess file of the dataset used in implicit sub-populations:(Demographic groups: race and gender) The following code will pre-process the jigsaw dataset and return train/test dataset files including demographic groups information. Step-1: Download the jigsaw dataset: identity_individual_annotations.csv from https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data. Step-2: python preprocecss_jiasaw_toxicity_gender_and_race_balanced.py Implementation of SSL methods Please follow the official implementations of MixMatch, MixText, and UDA. [1] https://github.com/google-research/mixmatch [2] https://github.com/GT-SALT/MixText [3] https://github.com/google-research/uda GitHub – UCSC-REAL/Disparate-SSL at pythonawesome.com Contribute to UCSC-REAL/Disparate-SSL development by creating an account on GitHub. GitHubUCSC-REAL    

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TIME SERIES

Dynamical Wasserstein Barycenters for Time Series Modeling

Dynamical Wasserstein Barycenters for Time Series Modeling This is the code related for the Dynamical Wasserstein Barycenter model published in Neurips 2021. To run the code and replicate the results reported in our paper, # usage: DynamicalWassersteinBarycenters.py dataSet dataFile debugFolder interpModel [–ParamTest PARAMTEST] [–lambda LAM] [–s S] # Sample run on MSR data >> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/MSR/subj001_1.mat Wass # Sample run for parameter test >> python DynamicalWassersteinBarycenters.py MSR_Batch ../Data/MSR_Data/subj090_1.mat ../debug/ParamTest/subj001_1.mat Wass –ParamTest 1 –lambda 100 –s 1.0 The […]

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Scripts

Formerly Codalab Competitions v2

(formerly Codalab Competitions v2) Installation $ cp .env_sample .env $ docker-compose up -d $ docker-compose exec django ./manage.py migrate $ docker-compose exec django ./manage.py generate_data $ docker-compose exec django ./manage.py collectstatic –noinput You can now login as username “admin” with password “admin” at http://localhost:8000 If you ever need to reset the database, use the script ./reset_db.sh Running tests # Non “end to end tests” $    

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