Technical Area:
- Learned how to train BERT, xlnet, roberta, distilbert models using the Simple Transformers library
- Was able to combine various models such as BERT and Random Forest and see their overall effectivness.
- Learned how BERT operates and how it can function
Tools:
- Simple Transformers
- tokenizers
- Docker
- SKlearn
- Jupiter Notebook
Achievement Highlights:
- Was able to train various models and see what their relative efficiencies were.
- Tried using different types of data, such as cleaned, cleaned, and not cleaned, to see which one was more effective.
- Tried dockerization of ML App with the advanced models → currently still having issues with this and is one of my main challenges.
Challenges:
- As the modules are becoming more complicated, a better understanding of the ins and outs of machine learning is needed to progress. Even though I had a lot of trouble at first, I was eventually able to obtain the knowledge I needed to complete this module.