Intent Detection: From Sesame Street to LLMs

#IndoML 2023 #Datathon #Tutorial

Notebooks

1. EDA.ipynb ➞

2. TransformerFinetune.ipynb ➞

3. LLM_ShortPrompt.ipynb ➞

Tutorial

In the online tutorial we will go over the notebooks of this repo. We focus on the AmazonScience/MASSIVE multilingual intent-detection.

There are few different parts to the tutorial:

1. EDA.ipynb: We first explore the dataset a bit to understand the task and various stats about the data. We also get to see how to use the datasets library from HuggingFace, what metadata information is available, and how to apply preprocessing to the data.
2. FinetuneTransformer.ipynb: We then finetune a transformer model on the dataset. We use the Trainer class from HuggingFace to do this.
3. LLM_PromptEngineering.ipynb: We then explore the use of LLMs for intent detection. We explore an in-context learning method to solve the task using a instruction-finetuned large-language model (LLM), without any finetuning.

There are also a few other bonus scripts available that showcases the use of other advanced techniques like parameter efficient tuning and low-rank adaptation methods. These methods will be useful for the datathon, to tackle the few-shot challenge in later phases and running on GPU devices available through Colab.

Where to get free GPU resources?

Best options are Kaggle Notebooks and Google Colab.

Other Links

• Nice blog about training very large models (Falcon-7b!) on Colab: The Falcon has landed in the Hugging Face ecosystem
  • Colab Link: Finetuning Falcon-7b