I'm
Sange Maxaku
Full S
|Software Developer, Full Stack Web Developer, Machine Learning (NLP) Engineer
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University of Cape Town | 2019 - 2021 (Incomplete)
Research towards dynamic and centralized language resource collection, management & processing for the development of Natural Language Processing (NLP) systems for South African Speech & Sign Language.
University of Cape Town | 2015 - 2018
Botlhale AI | 2019 - Present
Research & development company specializing in Airtificial Intelligence.
Botlhale AI | 2019 - Present
Responsible for the overall technology strategy and vision of the organization. I lead the development and implementation of technology solutions and I'm responsible for driving innovation and technology initiatives.
Using algorithms and machine learning to understand the structure and context of language data. Applications include language translation, text summarization, question answering, and text classification.
Case StudiesUsing data as input to predict future events without explicitly programming an application. Type of data I've worked with includes text, images and audio
Sentiment analysis classifies text as either positve, negative or neutral.
For example, the sentence, I love nature
would be regarded to have a positive sentiment while the sentence I hate nature
has a negative sentiment.
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ComputeMasked language modeling fills in the missing word in a sentence.
For it to work a mask token, [MASK], is required where the model would do the word filling.
For example, in the sentence The [MASK] of France is Paris
, the model would try to guess the word that fits in place of the mask token.
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ComputeToken classification is used for labelling important words in a sentence.
Some popular token classification subtasks are Named Entity Recognition (NER) and Part-of-Speech (PoS) tagging.
NER is trained to identify specific entities in a text, such as dates, individuals and places; and PoS tagging identifies, for example, which words in a text are verbs, nouns, and punctuation marks.
