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Евгения Суходольская
2025-05-05 12:52:54 +02:00
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qdrant-landing/content/articles/miniCOIL.md
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@@ -6,7 +6,7 @@ social_preview_image: /articles_data/minicoil/preview/social_preview.jpg
preview_dir: /articles_data/minicoil/preview
weight: -190
author: Evgeniya Sukhodolskaya
date: 2025-05-02T00:00:00+03:00
date: 2025-05-05T00:00:00+03:00
draft: false
keywords:
- hybrid search
@@ -231,7 +231,7 @@ Here are the specific characteristics of the miniCOIL model we trained based on
Each word was **trained on just one CPU**, and it took approximately fifty seconds per word to train.
We released this version of a miniCOIL in [our FastEmbed library](https://qdrant.tech/documentation/fastembed/).
TBD MINICOIL in FASTEMBED CC ANDREY/GEORGE.
TBD MINICOIL in FASTEMBED CC ANDREY/GEORGE ADD LINK BEFORE PUBLISHING.
## Results
@@ -276,7 +276,7 @@ To use any model for your specific use case, always benchmark it yourself!<br> P
## Key Takeaways
This article describes our attempt to make a lightweight sparse neural retriever that is able to generalize to our-of-domain data. Sparse neural retrieval has a lot of potential, and we hope to see it gain more traction.
This article describes our attempt to make a lightweight sparse neural retriever that is able to generalize to out-of-domain data. Sparse neural retrieval has a lot of potential, and we hope to see it gain more traction.
### Why this Approach can be Called Usable?