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followers: 9.0 following: 9.0 repos: 26.0 gists: 0.0

Name: Tyler Bikaun

Type: User

Company: The University of Western Australia

Location: Perth

👋 Hi, I’m Tyler Bikaun (@4theKnowledge)
👀 I’m interested in developing applications to support natural language processing (NLP) and knowledge graph construction (as well as other odd things!)
🌱 I’m currently learning graph visualisation and generative language modelling.
💞️ I’m looking to collaborate on NLP and graph annotation software.
📫 Feel free to reach out if you're interested in my software or want to collaborate - [email protected]

Tyler Bikaun's Projects

al_pretrain_lms

Active Learning for Sequence Labelling Tasks using Representations from Pre-trained Language Models

awesome-tlp

A curated list of awesome Technical Language Processing

binder_test

Test repository for binder interactive notebooks

cleangraph

Application for Interactive Knowledge Graph Refinement

e2e-entity-typing

End-to-end entity typing using Pytorch and BERT.

label_corrector

Corrects incorrect annotations on text documents

lexnorm-labeller-node

A simple nodejs app for labelling lexical normalisation data.

Natural language processing pipeline developed for extracting knowledge from the Semantic Scholar research corpus. The goal of this pipeline is to provide insights into literature and to aid in identifying research focus araes.

maintie_lrec

Maintenance Information Extraction (MaintIE)

probabilistic_ner

python-docs-hello-world

A simple python application for docs

pytorch-lexnorm

redcoat

A lightweight web-based annotation tool for labelling entity recognition data.

redcoat-curator

A simple tool for visualising and comparing annotations from Redcoat.

repopwa

Repo for PWA development (firebase)

retirement_simulation

Financial simulator for retirement

rl_rbd

RL + RBD

s-vaal

Sentence Based Variational Adversarial Active Learning

temporal-word-graphs

Generation and visualisation of temporal word graphs

wgan-gp

A pytorch implementation of Paper "Improved Training of Wasserstein GANs"

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