Model Card for passage-ranker.nectarine

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.nectarine

Supported Languages

The model was trained and tested in the following languages:

• English
• French
• German
• Spanish
• Italian
• Dutch
• Japanese
• Portuguese
• Chinese (simplified)
• Polish
• Arabic
• Korean

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).

Scores

Metric	Value
English Relevance (NDCG@10)	0.455
Arabic Relevance (NDCG@10)	0.250
Korean Relevance (NDCG@10)	0.232

Note that the relevance score is computed as an average over several retrieval datasets (see details below).

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
• Cuda compute capability: above 5.0 (above 6.0 for FP16 use)

Model Details

Overview

• Number of parameters: 167 million
• Base language model: Multilingual BERT-Base
• Insensitive to casing and accents
• Training procedure: MonoBERT

Training Data

• MS MARCO Passage Ranking (Paper, Official Page, English & translated datasets on the HF dataset hub, translated dataset in Polish on the HF dataset hub)
  • 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. Note that all these datasets are in English.

Dataset	NDCG@10
Average	0.455
Arguana	0.501
CLIMATE-FEVER	0.200
DBPedia Entity	0.353
FEVER	0.723
FiQA-2018	0.299
HotpotQA	0.657
MS MARCO	0.406
NFCorpus	0.299
NQ	0.449
Quora	0.751
SCIDOCS	0.136
SciFact	0.605
TREC-COVID	0.694
Webis-Touche-2020	0.296

Arabic

This model has arabic capacities, that are being evaluated over a home made translation of Msmarco with BM25 as the first stage retrieval.

Dataset	NDCG@10
msmarco-ar	0.250

Korean

This model has korean capacities, that are being evaluated over a home made translation of Msmarco with BM25 as the first stage retrieval.

Dataset	NDCG@10
msmarco-ko	0.232

Other languages

We evaluated the model on the datasets of the MIRACL benchmark 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	0.390
German	0.371
Spanish	0.447
Japanese	0.488
Chinese (simplified)	0.429