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Francois Meyer's Projects

fasttext icon fasttext

Library for fast text representation and classification.

lexical-ambiguity-dms icon lexical-ambiguity-dms

Code for training and evaluating density matrices, as in the CoNLL paper "Modelling Lexical Ambiguity with Density Matrices" (Meyer and Lewis, 2020)

nglueni icon nglueni

Repository for the NGLUEni benchmark, a standardised evaluation suite for evaluating Nguni PLMs. Presented in the 2024 LREC-COLING paper "NGLUEni: Benchmarking and Adapting Pretrained Language Models for Nguni Languages".

nlp2 icon nlp2

Repo for NLP2 assignments

nonce2vec icon nonce2vec

This is the repo accompanying the paper "High-risk learning: acquiring new word vectors from tiny data" (Herbelot & Baroni, 2017)

slm icon slm

Code of EMNLP paper: http://aclweb.org/anthology/D18-1531

ssmt icon ssmt

Code for training and evaluating subword segmental machine translation models, as in the Findings of ACL paper "Subword Segmental Machine Translation: Unifying Segmentation and Target Sentence Generation" (Meyer and Buys, 2023).

sspg icon sspg

Code for training and evaluating subword segmental pointer generator models for data-to-text, as in the LREC-COLING paper "Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation" (Meyer and Buys, 2024).

subword-segmental-lm icon subword-segmental-lm

Code for training and evaluating subword segmental language models, as in the Findings of EMNLP paper "Subword Segmental Language Modelling for Nguni Languages" (Meyer and Buys, 2022)

t2x icon t2x

Data-to-text dataset for isiXhosa, presented in the LREC-COLING paper "Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation" (Meyer and Buys, 2024).

time2vec icon time2vec

This repository accompanies the paper "Learning Concept Embeddings from Temporal Data" (Meyer, Van Der Merwe, and Coetsee, 2018)

word2vec-pytorch icon word2vec-pytorch

Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling

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