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Hello! I've been working in crypto since 2020, focused on products that promote individual autonomy and personal freedom.

I'm currently building tools for developers at thirdweb.

Find a list of my current and past projects here.


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greg's Projects

vit-pytorch icon vit-pytorch

Pytorch reimplementation of the Vision Transformer (An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale)

word_segmentation icon word_segmentation

This challenge involves word segmentation for Mandarin Chinese. Words are not delimited by spaces in the standard orthography of Chinese, and therefore it becomes important for NLP tools to segment text into words before further processing (POS tagging, parsing, machine translation, text-to-speech, etc.). The same is true for a number of other widely-used scripts: Japanese, Thai, Tibetan, Lao, etc. This can be thought of as a sequence tagging problem, where each character is assigned a tag: a 1 when the character is the final one in a word, and a 0 otherwise. With this setup, supervised learning approaches are quite effective, but they depend on the existence of hand-labeled training corpora. Given the huge amount of unsegmented Mandarin text available on the web, it is interesting to explore whether a system for word segmentation can be trained in an unsupervised fashion.

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