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Model Card for passage-ranker.mango
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.mango
Supported Languages
The model was trained and tested in the following languages:
- Chinese (simplified)
- Dutch
- English
- French
- German
- Italian
- Japanese
- Portuguese
- Spanish
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 |
|---|---|
| Relevance (NDCG@10) | 0.480 |
Note that the relevance score is computed as an average over 14 retrieval datasets (see details below).
Inference Times
| GPU | Batch size 32 |
|---|---|
| NVIDIA A10 | 84 ms |
| NVIDIA T4 | 358 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: 1070 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: 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)
- 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. Note that all these datasets are in English.
| Dataset | NDCG@10 |
|---|---|
| Average | 0.480 |
| Arguana | 0.537 |
| CLIMATE-FEVER | 0.241 |
| DBPedia Entity | 0.371 |
| FEVER | 0.777 |
| FiQA-2018 | 0.327 |
| HotpotQA | 0.696 |
| MS MARCO | 0.414 |
| NFCorpus | 0.332 |
| NQ | 0.484 |
| Quora | 0.768 |
| SCIDOCS | 0.143 |
| SciFact | 0.648 |
| TREC-COVID | 0.673 |
| Webis-Touche-2020 | 0.310 |
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 |
|---|---|
| Chinese (simplified) | 0.463 |
| French | 0.447 |
| German | 0.415 |
| Japanese | 0.526 |
| Spanish | 0.485 |