This model is a vectorizer developed by Sinequa. It produces an embedding vector given a passage or a query. The passage vectors are stored in our vector index and the query vector is used at query time to look up relevant passages in the index.
Note that the relevance score is computed as an average over 14 retrieval datasets (see
details below).
Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
can be around 0.5 to 1 GiB depending on the used GPU.
The model was trained on a Sinequa curated version of Google's
Natural Questions.
To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
BEIR benchmark. Note that all these datasets are in English.
