Uploaded April 2021 | Updated September 2026, 17 hours ago
Part 1 video: youtu.be/HuZq5KkLx8Q
In the first video we talked a little bit of some general theory behind Neural Machine Translation (NMT). This time we covered the data pipeline and data processing we will need to train our model. We are using an Asian language + English dataset for our training.
The previous video went over NLP models for sequential data, attention, self-attention, the mT5 model, and the Hugging Face library for transformers.
GitHub repo: github.com/ejmejm/multilingual-nmt-mt5
Colab code: colab.research.google.com/drive/1eGSCod03SjWD_YOfwb33kMJOeDZGu7lP?usp=sharing
Transformer paper: arxiv.org/abs/1706.03762
mT5 paper: arxiv.org/abs/2010.11934
Article on how transformers work: towardsdatascience.com/transformers-141e32e69591
Part 1 video: youtu.be/HuZq5KkLx8Q
In the first video we talked a little bit of some general theory behind Neural Machine Translation (NMT). This time we covered the data pipeline and data processing we will need to train our model. We are using an Asian language + English dataset for our training.
The previous video went over NLP models for sequential data, attention, self-attention, the mT5 model, and the Hugging Face library for transformers.
GitHub repo: github.com/ejmejm/multilingual-nmt-mt5
Colab code: colab.research.google.com/drive/1eGSCod03SjWD_YOfwb33kMJOeDZGu7lP?usp=sharing
Transformer paper: arxiv.org/abs/1706.03762
mT5 paper: arxiv.org/abs/2010.11934
Article on how transformers work: towardsdatascience.com/transformers-141e32e69591
