---
pipeline_tag: sentence-similarity
tags:
  - feature-extraction
  - sentence-similarity
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                                       | Quantization type |  Batch size 1  |  Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10                                | FP16              |           1 ms |           4 ms |
| NVIDIA A10                                | FP32              |           2 ms |          13 ms |
| NVIDIA T4                                 | FP16              |           1 ms |          13 ms |
| NVIDIA T4                                 | FP32              |           2 ms |          52 ms |
| NVIDIA L4                                 | FP16              |           1 ms |           5 ms |
| NVIDIA L4                                 | FP32              |           2 ms |          18 ms |

## Gpu Memory usage

| Quantization type                                |   Memory   |
|:-------------------------------------------------|-----------:|
| FP16                                             |    300 MiB |
| FP32                                             |    500 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.

## Requirements

- Minimal Sinequa version: 11.10.0
- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)

## 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: A first model was trained with query-passage pairs, using the in-batch negative strategy with [this loss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss). A second model was then trained on query-passage-negative triplets with negatives mined from the previous model, like a variant of [ANCE](https://arxiv.org/pdf/2007.00808.pdf) but with different hyper parameters.

### Training Data

The model was trained on a Sinequa curated version of Google's [Natural Questions](https://ai.google.com/research/NaturalQuestions).

### 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 |