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---
language:
- de
- en
- es
- fr
- it
- ja
- nl
- pt
- zh
---
# Model Card for `passage-ranker.strawberry`
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.strawberry`
## Supported Languages
The model was trained and tested in the following languages:
- Chinese
- Dutch
- English
- French
- German
- Italian
- Japanese
- Portuguese
- Spanish
Besides the aforementioned languages, basic support can be expected for additional 91 languages that were used during
the pretraining of the base model (see Appendix A of [XLM-R paper](https://arxiv.org/abs/1911.02116)).
## Scores
| Metric | Value |
|:--------------------|------:|
| Relevance (NDCG@10) | 0.451 |
Note that the relevance score is computed as an average over 14 retrieval datasets (see
[details below](#evaluation-metrics)).
## Inference Times
| GPU | Batch size 32 |
|:-----------|--------------:|
| NVIDIA A10 | 22 ms |
| NVIDIA T4 | 63 ms |
The inference times only measure the time the model takes to process a single batch, it does not include pre- or
post-processing steps like the tokenization.
## Requirements
- Minimal Sinequa version: 11.10.0
- GPU memory usage: 1060 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.
## Model Details
### Overview
- Number of parameters: 107 million
- Base language model:
[mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large)
([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
- Insensitive to casing and accents
- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085)
### Training Data
- MS MARCO Passage Ranking
([Paper](https://arxiv.org/abs/1611.09268),
[Official Page](https://microsoft.github.io/msmarco/),
[English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco))
- Original English dataset
- Translated datasets for the other eight 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 | 0.451 |
| | |
| Arguana | 0.527 |
| CLIMATE-FEVER | 0.167 |
| DBPedia Entity | 0.343 |
| FEVER | 0.698 |
| FiQA-2018 | 0.297 |
| HotpotQA | 0.648 |
| MS MARCO | 0.409 |
| NFCorpus | 0.317 |
| NQ | 0.430 |
| Quora | 0.761 |
| SCIDOCS | 0.135 |
| SciFact | 0.597 |
| TREC-COVID | 0.670 |
| Webis-Touche-2020 | 0.311 |
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 | 0.382 |
| German | 0.320 |
| Spanish | 0.418 |
|