transformers pytorch bert feature-extraction sentence-similarity de en es fr arxiv:1908.08962 text-embeddings-inference endpoints_compatible region:us
---
language:
- de
- en
- es
- fr
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
---

# 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

| Metric                 | Value |
|:-----------------------|------:|
| Relevance (Recall@100) | 0.448 |

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 |           5 ms |
| NVIDIA A10                                | FP32              |           3 ms |          14 ms |
| NVIDIA T4                                 | FP16              |           1 ms |          12 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                                             |    600 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: 39 million
- Base language model: Homegrown Sinequa BERT-Small ([Paper](https://arxiv.org/abs/1908.08962)) 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](https://research.google/pubs/pub47761/),
  [Official Page](https://github.com/google-research-datasets/natural-questions))
    - 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](https://github.com/beir-cellar/beir). Note that all these datasets are in English.

| Dataset           | Recall@100 |
|:------------------|-----------:|
| Average           |      0.448 |
|                   |            |
| Arguana           |      0.835 |
| CLIMATE-FEVER     |      0.350 |
| DBPedia Entity    |      0.287 |
| FEVER             |      0.645 |
| FiQA-2018         |      0.305 |
| HotpotQA          |      0.396 |
| MS MARCO          |      0.533 |
| NFCorpus          |      0.162 |
| NQ                |      0.701 |
| Quora             |      0.947 |
| SCIDOCS           |      0.194 |
| SciFact           |      0.580 |
| TREC-COVID        |      0.051 |
| Webis-Touche-2020 |      0.289 |


We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) 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.

| Language | Recall@100 |
|:---------|-----------:|
| French   |      0.583 |
| German   |      0.524 |
| Spanish  |      0.483 |