vectorizer.vanilla

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.vanilla

Supported Languages

The model was trained and tested in the following languages:

• English

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 7.5

Model Details

Overview

• Number of parameters: 23 million
• Base language model: English MiniLM-L6-H384
• Insensitive to casing and accents
• Output dimensions: 256 (reduced with an additional dense layer)
• Training procedure: Query-passage-negative triplets for datasets that have mined hard negative data, Query-passage pairs for the rest. Number of negatives is augmented with in-batch negative strategy.

Training Data

The model have been trained using all datasets that are cited in the all-MiniLM-L6-v2 model.

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.