---
language:
  - en
---

# Model Card for `vectorizer-v1-S-en`

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-en`

## Supported Languages

The model was trained and tested in the following languages:

- English

## Scores

| Metric                 | Value |
|:-----------------------|------:|
| Relevance (Recall@100) | 0.456 |

Note that the relevance score is computed as an average over 14 retrieval datasets (see
[details below](#evaluation-metrics)).

## Inference Times

| GPU        | Batch size 1 (at query time) | Batch size 32 (at indexing) |
|:-----------|-----------------------------:|----------------------------:|
| NVIDIA A10 |                         2 ms |                       14 ms |
| NVIDIA T4  |                         4 ms |                       52 ms |

The inference times only measure the time the model takes to process a single batch, it does not include pre- or
post-processing steps like the tokenization.

## Requirements

- Minimal Sinequa version: 11.10.0
- GPU memory usage: 330 MiB

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.

## Model Details

### Overview

- Number of parameters: 29 million
- Base language model: [English BERT-Small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8)
- Insensitive to casing and accents
- Output dimensions: 256 (reduced with an additional dense layer)
- Training procedure: TBD

### Training Data

TBD

### Evaluation Metrics

To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.

| Dataset           | Recall@100 |
|:------------------|-----------:|
| Average           |      0.456 |
|                   |            |
| Arguana           |      0.832 |
| CLIMATE-FEVER     |      0.342 |
| DBPedia Entity    |      0.299 |
| FEVER             |      0.660 |
| FiQA-2018         |      0.301 |
| HotpotQA          |      0.434 |
| MS MARCO          |      0.610 |
| NFCorpus          |      0.159 |
| NQ                |      0.671 |
| Quora             |      0.966 |
| SCIDOCS           |      0.194 |
| SciFact           |      0.592 |
| TREC-COVID        |      0.037 |
| Webis-Touche-2020 |      0.285 |