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At ECIR 2021, the Glasgow IR group presented a set of very good tutorial notebooks for experimenting with PyTerrier.
Some of their notebooks cover concepts not yet covered in our courses (e.g., LTR, see #2).
As their notebooks are designed to run on Google Colab, I think slight adjustments and updates would be necessary to integrate them with our Dev container workflow.
At ECIR 2021, the Glasgow IR group presented a set of very good tutorial notebooks for experimenting with PyTerrier.
Some of their notebooks cover concepts not yet covered in our courses (e.g., LTR, see #2).
As their notebooks are designed to run on Google Colab, I think slight adjustments and updates would be necessary to integrate them with our Dev container workflow.
Overview
1.Classical IR: indexing, retrieval and evaluation
2. Modern retrieval architectures: PyTerrier data model and operators, towards re-rankers and learning-to-rank
3. Contemporary retrieval architectures: neural re-rankers, BERT, EPIC, ColBERT
3.1. OpenNIR and monoT5
3.2. ColBERT re-ranking
4. Recent advances beyond the classical inverted index: neural inverted index augmentation, nearest neighbor search, dense retrieval
4.1. doc2query and DeepCT
4.2. ANCE retrieval
4.3. ColBERT retrieval
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