Model Card for vectorizer-v1-S-multilingual

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.

Model name: vectorizer-v1-S-multilingual

Supported Languages

The model was trained and tested in the following languages:

• English
• French
• German
• Spanish

Scores

Note that the relevance score is computed as an average over 14 retrieval datasets (see details below).

Inference Times

Gpu Memory usage

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.

Requirements

• Minimal Sinequa version: 11.10.0
• Cuda compute capability: above 5.0 (above 6.0 for FP16 use)

Model Details

Overview

• Number of parameters: 39 million
• Base language model: Homegrown Sinequa BERT-Small (Paper) pretrained in the four supported languages
• Insensitive to casing and accents
• Training procedure: Query-passage pairs using in-batch negatives

Training Data

• Natural Questions (Paper, Official Page)
  • Original English dataset
  • Translated datasets for the other three supported languages

Evaluation Metrics

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.