This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results.
Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see
list of languages).
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.
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.
We evaluated the model on the datasets of the
MIRACL benchmark to test its multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics for the existing languages.
