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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 (simplified)
- 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).
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).
Inference Times
| GPU | Quantization type | Batch size 1 | Batch size 32 |
|---|---|---|---|
| NVIDIA A10 | FP16 | 1 ms | 5 ms |
| NVIDIA A10 | FP32 | 2 ms | 22 ms |
| NVIDIA T4 | FP16 | 1 ms | 13 ms |
| NVIDIA T4 | FP32 | 3 ms | 64 ms |
| NVIDIA L4 | FP16 | 2 ms | 6 ms |
| NVIDIA L4 | FP32 | 2 ms | 30 ms |
Gpu Memory usage
| Quantization type | Memory |
|---|---|
| FP16 | 550 MiB |
| FP32 | 1100 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: above 5.0 (above 6.0 for FP16 use)
Model Details
Overview
- Number of parameters: 107 million
- Base language model: mMiniLMv2-L6-H384-distilled-from-XLMR-Large (Paper, GitHub)
- 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 | 0.451 |
| 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 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) | 0.414 |
| French | 0.382 |
| German | 0.320 |
| Japanese | 0.479 |
| Spanish | 0.418 |