Scripts used to make and evaluate OpenAlex's concept tagging model

OurResearch

Last update: Dec 9, 2022

Related tags

Deep Learning openalex-concept-tagging

Overview

openalex-concept-tagging

This repository contains all of the code for getting the concept tagger up and running. To learn more about where this model is being used, feel free to visit: https://openalex.org/

Model Development

You can find an explanation of the modeling process at the following link: --INSERT LINK HERE---

Model Iterations

V1 (current)
V2 (in progress)

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

9.6k Dec 31, 2022

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

9.6k Jan 6, 2023

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It can use GPUs and perform efficient symbolic differentiation.

============================================================================================================ `MILA will stop developing Theano https:

9.3k Feb 12, 2021

Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

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Ludwig is a toolbox that allows to train and evaluate deep learning models without the need to write code.

Translated in 🇰🇷 Korean/ Ludwig is a toolbox that allows users to train and test deep learning models without the need to write code. It is built on

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OCTIS: Comparing Topic Models is Simple! A python package to optimize and evaluate topic models (accepted at EACL2021 demo track)

OCTIS : Optimizing and Comparing Topic Models is Simple! OCTIS (Optimizing and Comparing Topic models Is Simple) aims at training, analyzing and compa

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Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent

Narya The Narya API allows you track soccer player from camera inputs, and evaluate them with an Expected Discounted Goal (EDG) Agent. This repository

121 Dec 30, 2022

HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events globally on daily to subseasonal timescales.

HeatNet HeatNet is a python package that provides tools to build, train and evaluate neural networks designed to predict extreme heat wave events glob

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Metrics to evaluate quality and efficacy of synthetic datasets.

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Comments

Benchmark with entity fishing ?

Hello

I was wondering if you have an idea how well the model developed for openalex performs compared to entity-fishing (https://github.com/kermitt2/entity-fishing) ?

Thanks

Eric

opened by ericjeangirard 0
How to transform abstract for the api?

Hi, I'm able to run the api locally and it works for the test data from test_json_V2_single.json but I'm wondering how can I turn my own data into the required format? Title property is fine because it's plain text but abstract has some special format and I'm not sure how to convert plain text abstract into that. Thank you for this work and for help!

opened by kamilpz 0

Owner

OurResearch

We build tools to make scholarly research more open, connected, and reusable—for everyone. Logo CC0.

GitHub

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

An Effective Loss Function for Generating 3D Models from Single 2D Image without Rendering Papers with code | Paper Nikola Zubić Pietro Lio University

213 Dec 27, 2022

This project provides an unsupervised framework for mining and tagging quality phrases on text corpora with pretrained language models (KDD'21).

UCPhrase: Unsupervised Context-aware Quality Phrase Tagging To appear on KDD'21...[pdf] This project provides an unsupervised framework for mining and

146 Dec 22, 2022

Code and data form the paper BERT Got a Date: Introducing Transformers to Temporal Tagging

BERT Got a Date: Introducing Transformers to Temporal Tagging Satya Almasian*, Dennis Aumiller*, and Michael Gertz Heidelberg University Contact us vi

54 Dec 4, 2022

Code for the TASLP paper "PSLA: Improving Audio Tagging With Pretraining, Sampling, Labeling, and Aggregation".

PSLA: Improving Audio Tagging with Pretraining, Sampling, Labeling, and Aggregation Introduction Getting Started FSD50K Recipe AudioSet Recipe Label E

84 Dec 27, 2022

Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The original code is written in keras.

CasRel-pytorch-reimplement Pytorch reimplement of the paper "A Novel Cascade Binary Tagging Framework for Relational Triple Extraction" ACL2020. The o

170 Dec 1, 2022

Scripts used to make and evaluate OpenAlex's concept tagging model

Related tags

Overview

openalex-concept-tagging

Model Development

Model Iterations

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Comments

Benchmark with entity fishing ?

How to transform abstract for the api?

Owner

OurResearch

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods and 3D representations, all in terms of performance, accuracy, and training time.

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