From 974ab8bd94e593478e7bdf8d0be12773306a05d5 Mon Sep 17 00:00:00 2001 From: Ted Sanders Date: Thu, 9 Jun 2022 17:46:16 -0700 Subject: [PATCH] fixes a few links --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 85258463..6f997274 100644 --- a/README.md +++ b/README.md @@ -431,7 +431,7 @@ Tips for translation: When it comes to translation, large language models particularly shine at combining other instructions alongside translation. For example, you can ask GPT-3 to translate Slovenian to English but keep all LaTeX typesetting commands unchanged. The following notebook details how we translated a Slovenian math book into English: -[Translation of a Slovenian math book into English](book_translation/translate_latex_book.ipynb) +[Translation of a Slovenian math book into English](examples/book_translation/translate_latex_book.ipynb) ### 4. Compare text @@ -448,13 +448,13 @@ The simplest way to use embeddings for search is as follows: * Before the search (precompute): * Split your text corpus into chunks smaller than the token limit (e.g., ~2,000 tokens) * Embed each chunk using a 'doc' model (e.g., `text-search-curie-doc-001`) - * Store those embeddings in your own database or in a vector search provider like [pinecone.io](pinecone.io) or [weaviate](weaviate.io) + * Store those embeddings in your own database or in a vector search provider like [Pinecone](https://www.pinecone.io) or [Weaviate](https://weaviate.io) * At the time of the search (live compute): * Embed the search query using the correponding 'query' model (e.g. `text-search-curie-query-001`) * Find the closest embeddings in your database * Return the top results, ranked by cosine similarity -An example of how to use embeddings for search is shown in [Semantic_search.ipynb](examples/Semantic_search.ipynb). +An example of how to use embeddings for search is shown in [Semantic_text_search_using_embeddings.ipynb](examples/Semantic_text_search_using_embeddings.ipynb). In more advanced search systems, the the cosine similarity of embeddings can be used as one feature among many in ranking search results.