Comments (1)
okhat
commented on June 24, 2024
Hi Shashank! Sorry for the late response.
I strongly recommend using a passage-level Wikipedia corpus. It's common in the Open-QA literature (e.g., our ColBERT-QA paper) to divide Wikipedia into 100-word or (say) 200-token passages, keeping the title of the page at the start of each passage.
For the second one, encoding the corpus (or the queries) with colbert.index can give you files with all the embeddings. Or you can use the ModelInference class from colbert/modeling/inference.py, and in particular queryFromText and docFromText. See existing uses in the code for how to do this; it's pretty simple!
Let me know if you face any issues!
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