There are several fancy machine learning libraries out there, so what makes Finetuner unique?
Focus on the quality of embeddings#
Finetuner is not designed to tackle classification, sentiment analysis or object detection task. Finetuner cares about the quality of the embeddings for neural search, and this is what the fine-tuned model will produce.
Given a query
Document represented by
you can compare the similarity/distance of the query Documents against all indexed (embedded) Documents in your storage backend.
Dedicated to optimizing your search task#
Finetuner helps you boost your search system performance on different uses cases:
text-to-text search (or dense vector search)
image-to-image search (or content-based image search)
text-to-image search (based on OpenAI CLIP)
more is on the way!
Search performance depends on a lot of factors. Internally we have conducted a lot of experiments on various tasks, such as image-to-image search, text-to-text search, cross-modal search. Across these three tasks, Finetuner is able to boost 20%-45% of precision@k and recall@k. You can also observe significant performance improvement on other search metrics, such as mean recipal rank (mRR) or normalized discounted cumulative gain (nDCG).
Easy to use#
Finetuner gives the user flexibility to choose machine learning hyper-parameters, while all these parameters are optional.
If you do not have a machine learning background,
don’t worry about it.
As was stated before, you only need to provide the training data organized as a
In case you do not know which backbone to choose,
describe_models() to let Finetuner suggest a backbone model for you.