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---
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pipeline_tag: sentence-similarity
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tags:
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- feature-extraction
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- sentence-similarity
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language:
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- de
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- en
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- es
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- fr
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---
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# Model Card for `vectorizer-v1-S-multilingual`
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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.
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Model name: `vectorizer-v1-S-multilingual`
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## Supported Languages
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The model was trained and tested in the following languages:
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- English
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- French
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- German
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- Spanish
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## Scores
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| Metric | Value |
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|:-----------------------|------:|
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| Relevance (Recall@100) | 0.448 |
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Note that the relevance score is computed as an average over 14 retrieval datasets (see
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[details below](#evaluation-metrics)).
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## Inference Times
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| GPU | Quantization type | Batch size 1 | Batch size 32 |
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|:------------------------------------------|:------------------|---------------:|---------------:|
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| NVIDIA A10 | FP16 | 1 ms | 5 ms |
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| NVIDIA A10 | FP32 | 3 ms | 14 ms |
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| NVIDIA T4 | FP16 | 1 ms | 12 ms |
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| NVIDIA T4 | FP32 | 2 ms | 52 ms |
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| NVIDIA L4 | FP16 | 1 ms | 5 ms |
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| NVIDIA L4 | FP32 | 2 ms | 18 ms |
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## Gpu Memory usage
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| Quantization type | Memory |
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|:-------------------------------------------------|-----------:|
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| FP16 | 300 MiB |
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| FP32 | 600 MiB |
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Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
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size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
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can be around 0.5 to 1 GiB depending on the used GPU.
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## Requirements
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- Minimal Sinequa version: 11.10.0
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- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
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- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
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## Model Details
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### Overview
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- Number of parameters: 39 million
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- Base language model: Homegrown Sinequa BERT-Small ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
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supported languages
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- Insensitive to casing and accents
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- Training procedure: Query-passage pairs using in-batch negatives
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### Training Data
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- Natural Questions
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([Paper](https://research.google/pubs/pub47761/),
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[Official Page](https://github.com/google-research-datasets/natural-questions))
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- Original English dataset
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- Translated datasets for the other three supported languages
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### Evaluation Metrics
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To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
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[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
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| Dataset | Recall@100 |
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|:------------------|-----------:|
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| Average | 0.448 |
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| | |
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| Arguana | 0.835 |
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| CLIMATE-FEVER | 0.350 |
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| DBPedia Entity | 0.287 |
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| FEVER | 0.645 |
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| FiQA-2018 | 0.305 |
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| HotpotQA | 0.396 |
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| MS MARCO | 0.533 |
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| NFCorpus | 0.162 |
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| NQ | 0.701 |
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| Quora | 0.947 |
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| SCIDOCS | 0.194 |
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| SciFact | 0.580 |
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| TREC-COVID | 0.051 |
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| Webis-Touche-2020 | 0.289 |
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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.
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| Language | Recall@100 |
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|:---------|-----------:|
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| French | 0.583 |
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| German | 0.524 |
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| Spanish | 0.483 | |
