Over the past several months, I’ve been doing a deep-dive into transformer language models #NLProc and their applications in #psychology for the last part of my #PhD thesis. 👩🏻🎓 Here are a few resources that I’ve found invaluable and really launched me forward on this project 🧵:
🏃🏻♀️💨 If you already have some data and you want a jump start to classify it all using #transformers, check out @maria_antoniak’s #BERT for Humanists/Computational Social Scientists Talk: Colab notebook and other info: bertforhumanists.org/tutorials/#cla…
🤓 I’m an absolute nerd for stats and experiment design in psych, but doing #ML experiments is very different. A clear, detailed (and reasonable length) course to get up to speed on train-test splits, hyperparameter tuning, etc. and do the very best work: coursera.org/learn/deep-neu…
🔍 On that note, if you like to get down to the nitty-gritty on the tools you use, everyone will recommend you jalammar.github.io/illustrated-be…, but I’ll recommend this more detailed, 3-part series explaining how transformers work borealisai.com/en/blog/tutori… by @SimonPrinceAI
🔬 If you’re using language models to model some human behavior, you’ll eventually want to explain the predictions/model itself, or learn something new about the text features people generate in your task. @ChristophMolnar puts you on the right path christophm.github.io/interpretable-…
Also! 📕 Looking forward to reading transformersbook.com by @_lewtun @lvwerra and @Thom_Wolf.
@huggingface is relatively easy to use (see BERT link above), and many people do, so even if the documentation doesn't tell you what you want to know, the community forum might 🤗
@huggingface is relatively easy to use (see BERT link above), and many people do, so even if the documentation doesn't tell you what you want to know, the community forum might 🤗
And I'm still learning! So, if you've got some more (better?) recommendations, please share. 😁
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