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Search story notes by meaning with Sentence Transformers embeddings and a reranker
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What this guide covers
For writers and asset managers who want to find story notes or asset descriptions by meaning rather than exact words. You end with a short Python script that embeds texts, scores them against a query and reranks the best candidates. Based on the project README; nothing was run.
More about this guide
Guide ID: art_01M6VTGS9T3SQG6PYG3KM6TKZ2
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Guide title
Search story notes by meaning with Sentence Transformers embeddings and a reranker
Coverage
For writers and asset managers who want to find story notes or asset descriptions by meaning rather than exact words. You end with a short Python script that embeds texts, scores them against a query and reranks the best candidates. Based on the project README; nothing was run.
Example
A hypothetical three-note example (a keeper, a night train arrival, a fish market) searched with the query 'Who comes by rail?'. It was not run, so no scores are reported.
Useful starting points
- In the README, related sentences scored about 0.67 and unrelated ones about 0.1, so the right note should stand out clearly.
- A (3, 384) embedding shape for the MiniLM model means one 384-number vector per text.
- If model downloads hit rate limits, the README suggests logging in with the Hugging Face CLI.
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