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Merge pull request #3 from openai/ted_fix_link

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fixes a few links
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Ted Sanders 2022-06-09 19:08:07 -07:00 committed by GitHub
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@ -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.