Content-Length: 1412 | pFad | http://github.com/postgresml/postgresml/pull/1026.patch

thub.com From 3529c6c9ab79d72443ceac6db60f764d7385ea0f Mon Sep 17 00:00:00 2001 From: Alex Boquist Date: Fri, 22 Sep 2023 10:51:57 -0400 Subject: [PATCH] Fix instructor typo --- pgml-sdks/pgml/python/examples/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pgml-sdks/pgml/python/examples/README.md b/pgml-sdks/pgml/python/examples/README.md index d1b16c6a1..3cd4298e6 100644 --- a/pgml-sdks/pgml/python/examples/README.md +++ b/pgml-sdks/pgml/python/examples/README.md @@ -15,7 +15,7 @@ This is a basic example to perform semantic search on a collection of documents. ## [Question Answering](./question_answering.py) This is an example to find documents relevant to a question from the collection of documents. It loads the Stanford Question Answering Dataset (SQuAD) into the database, generates chunks and embeddings. Query is passed to vector search to retrieve documents that match closely in the embeddings space. A score is returned with each of the search result. -## [Question Answering using Instructore Model](./question_answering_instructor.py) +## [Question Answering using Instructor Model](./question_answering_instructor.py) In this example, we will use `hknlp/instructor-base` model to build text embeddings instead of the default `intfloat/e5-small` model. ## [Extractive Question Answering](./extractive_question_answering.py)

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