Hiram Ring - Director at Ring Records

Hiram Ring

Director at Ring Records

Singapore

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Summary

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Senior

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Top School

Hiram Ring is a Director and computational linguist based in Singapore with a decade of experience applying machine learning to natural language tasks and a parallel academic career in Austroasiatic descriptive and historical linguistics. He builds practical NLP tools—contributing Electra support to the popular fast-bert library—and leads applied research at Encoding AI while lecturing at Nanyang Technological University. His work spans text classification, sentiment analysis, information extraction, and semi-automated methods for grammatical reconstruction using word vectors. A published field linguist and former postdoc, he combines deep field experience documenting minority languages with hands-on ML engineering, a rare bridge between theoretical linguistics and production NLP. Outside tech, he runs an independent record label and brings audio engineering, production, and event management skills to multidisciplinary projects.

10 years of coding experience

Doctor of Philosophy (Ph.D.), Linguistics, Doctor of Philosophy (Ph.D.), Linguistics at Nanyang Technological University

BA, Intercultural Studies, Linguistics, Art, BA, Intercultural Studies, Linguistics, Art at Houghton University

English, French, Spanish, Persian, pnar, Swahili

Github Skills (8)

transformers10

pytorch10

nlp10

electra10

bert10

python9

machine-learning9

fastai8

Programming languages (7)

C#ShellRustVueJavaScriptHTMLPython

Github contributions (5)

utterworks/fast-bert

Jun 2020 - Jun 2020

Super easy library for BERT based NLP models

Role in this project:

ML Engineer

Contributions:5 commits, 8 PRs, 20 comments in 1 day

Contributions summary:Hiram primarily contributed to the `fast-bert` library by adding support for the Electra model. Their commits involved integrating Electra into the existing codebase, including updates to the model definitions, data loading, and learner classes. They modified the `fast_bert/learner_cls.py`, `fast_bert/modeling.py`, `fast_bert/data_lm.py`, and `fast_bert/learner_lm.py` files to incorporate Electra for classification and language modeling tasks. Additionally, the user made a minor change to the scheduler within `fast_bert/learner_util.py`.

fast-bertnlptransformersdistilbertbert

lingdoc/trs2txt

Nov 2015 - Jan 2019

Contributions:3 releases, 23 commits, 8 PRs in 3 years 2 months

toolboxtranscriberfieldworkconverter

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