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---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
language:
- de
- en
- es
- fr
---
# 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 | |