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Name: Francois Meyer
Type: User
Bio: I am a PhD student at the University of Cape Town, working on neural text generation for low-resource languages.
Twitter: francoisrmeyer
Location: Cape Town, South Africa
Name: Francois Meyer
Type: User
Bio: I am a PhD student at the University of Cape Town, working on neural text generation for low-resource languages.
Twitter: francoisrmeyer
Location: Cape Town, South Africa
Library for fast text representation and classification.
Topic Modelling for Humans
Code for training and evaluating density matrices, as in the CoNLL paper "Modelling Lexical Ambiguity with Density Matrices" (Meyer and Lewis, 2020)
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".
Repo for NLP2 assignments
This is the repo accompanying the paper "High-risk learning: acquiring new word vectors from tiny data" (Herbelot & Baroni, 2017)
Code of EMNLP paper: http://aclweb.org/anthology/D18-1531
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).
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).
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)
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).
This repository accompanies the paper "Learning Concept Embeddings from Temporal Data" (Meyer, Van Der Merwe, and Coetsee, 2018)
Extremely simple and fast word2vec implementation with Negative Sampling + Sub-sampling
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