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---
language:
- de
- en
- es
- fr
---
# Model Card for `passage-ranker-v1-XS-multilingual`
This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is used to order search results.
Model name: `passage-ranker-v1-XS-multilingual`
## Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
## Scores
| Metric | Value |
|:--------------------|------:|
| Relevance (NDCG@10) | 0.453 |
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 | 2 ms |
| NVIDIA A10 | FP32 | 1 ms | 7 ms |
| NVIDIA T4 | FP16 | 1 ms | 6 ms |
| NVIDIA T4 | FP32 | 1 ms | 20 ms |
| NVIDIA L4 | FP16 | 1 ms | 3 ms |
| NVIDIA L4 | FP32 | 2 ms | 8 ms |
## Gpu Memory usage
| Quantization type | Memory |
|:-------------------------------------------------|-----------:|
| FP16 | 150 MiB |
| FP32 | 300 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: 16 million
- Base language model: Homegrown Sinequa BERT-Mini ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four
supported languages
- Insensitive to casing and accents
- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
### Training Data
- Probably-Asked Questions
([Paper](https://arxiv.org/abs/2102.07033),
[Official Page](https://github.com/facebookresearch/PAQ))
- 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 | NDCG@10 |
|:------------------|--------:|
| Average | 0.453 |
| | |
| Arguana | 0.516 |
| CLIMATE-FEVER | 0.159 |
| DBPedia Entity | 0.355 |
| FEVER | 0.729 |
| FiQA-2018 | 0.282 |
| HotpotQA | 0.688 |
| MS MARCO | 0.334 |
| NFCorpus | 0.341 |
| NQ | 0.438 |
| Quora | 0.726 |
| SCIDOCS | 0.143 |
| SciFact | 0.630 |
| TREC-COVID | 0.664 |
| Webis-Touche-2020 | 0.337 |
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 | NDCG@10 |
|:---------|--------:|
| French | 0.346 |
| German | 0.368 |
| Spanish | 0.416 |
|