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---
language:
- de
- en
- es
- fr
- it
- ja
- nl
- pt
- zh
- pl
---
# Model Card for `passage-ranker.pistachio`
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.pistachio`
## Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
- Italian
- Dutch
- Japanese
- Portuguese
- Chinese (simplified)
- Polish
Besides the aforementioned languages, basic support can be expected for additional 93 languages that were used during the pretraining of the base model (see
[list of languages](https://github.com/google-research/bert/blob/master/multilingual.md#list-of-languages)).
## Scores
| Metric | Value |
|:----------------------------|------:|
| English Relevance (NDCG@10) | 0.474 |
| Polish Relevance (NDCG@10) | 0.380 |
Note that the relevance score is computed as an average over several retrieval datasets (see
[details below](#evaluation-metrics)).
## Inference Times
| GPU | Quantization type | Batch size 1 | Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10 | FP16 | 2 ms | 28 ms |
| NVIDIA A10 | FP32 | 4 ms | 82 ms |
| NVIDIA T4 | FP16 | 3 ms | 65 ms |
| NVIDIA T4 | FP32 | 14 ms | 369 ms |
| NVIDIA L4 | FP16 | 3 ms | 38 ms |
| NVIDIA L4 | FP32 | 5 ms | 123 ms |
## Gpu Memory usage
| Quantization type | Memory |
|:-------------------------------------------------|-----------:|
| FP16 | 850 MiB |
| FP32 | 1200 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: 167 million
- Base language model: [Multilingual BERT-Base](https://huggingface.co/bert-base-multilingual-uncased)
- 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), [translated dataset in Polish on the HF dataset hub](https://huggingface.co/datasets/clarin-knext/msmarco-pl))
- Original English dataset
- Translated datasets for the other nine supported languages
### Evaluation Metrics
##### English
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 | 0.474 |
| | |
| Arguana | 0.539 |
| CLIMATE-FEVER | 0.230 |
| DBPedia Entity | 0.369 |
| FEVER | 0.765 |
| FiQA-2018 | 0.329 |
| HotpotQA | 0.694 |
| MS MARCO | 0.413 |
| NFCorpus | 0.337 |
| NQ | 0.486 |
| Quora | 0.714 |
| SCIDOCS | 0.144 |
| SciFact | 0.649 |
| TREC-COVID | 0.651 |
| Webis-Touche-2020 | 0.312 |
#### Polish
This model has polish capacities, that are being evaluated over a subset of
the [PIRBenchmark](https://github.com/sdadas/pirb) with BM25 as the first stage retrieval.
| Dataset | NDCG@10 |
|:--------------|--------:|
| Average | 0.380 |
| | |
| arguana-pl | 0.285 |
| dbpedia-pl | 0.283 |
| fiqa-pl | 0.223 |
| hotpotqa-pl | 0.603 |
| msmarco-pl | 0.259 |
| nfcorpus-pl | 0.293 |
| nq-pl | 0.355 |
| quora-pl | 0.613 |
| scidocs-pl | 0.128 |
| scifact-pl | 0.581 |
| trec-covid-pl | 0.560 |
#### Other languages
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 | 0.439 |
| German | 0.418 |
| Spanish | 0.487 |
| Japanese | 0.517 |
| Chinese (simplified) | 0.454 | |