Module 4: Transformer Models for Semantic Search
Module 4 will be all about Transformer Models and how to use them for various NLP tasks, but especially for the semantic search:
- Semantic search is a search engine technology that interprets the meaning of words and phrases. The results of a semantic search will return content matching the meaning of a query, as opposed to content that literally matches words in the query.
- Semantic search uses context clues to determine the meaning of a word across a dataset of millions of examples.
- Semantic search also identifies what other words can be used in similar contexts
Preparation for Module 4:
-
Read the article listed under literature below and prepare for class discussion:
- Why are machine learning methods called "Black Boxes"?
- What does XAI stand for?
- What is a self-attention mechanism?
- Name a few methods to look into the "Black Box"
- Create at least one more entry in the Glossary
Literature:
Dobson, J.E. On reading and interpreting black box deep neural networks. Int J Digit Humanities 5, 431–449 (2023). https://doi.org/10.1007/s42803-023-00075-w
Notebooks we will use in class:
Download über API der DDB
Introduction to Transformers: What Can They Do?
Transformers and Semantic Search
Workload (after class):
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Try the semantic search for your own research question:
- Can you find new relevant keywords/articles?
Date and Time:
November 22, 2024 (10:00 AM to 11:30 AM)