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--- |
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language: |
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- de |
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- en |
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- es |
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- fr |
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- it |
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- ja |
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- nl |
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- pt |
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- zh |
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--- |
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# Model Card for `passage-ranker.strawberry` |
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This model is a passage ranker developed by Sinequa. It produces a relevance score given a query-passage pair and is |
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used to order search results. |
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Model name: `passage-ranker.strawberry` |
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## Supported Languages |
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The model was trained and tested in the following languages: |
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- Chinese (simplified) |
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- Dutch |
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- English |
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- French |
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- German |
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- Italian |
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- Japanese |
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- Portuguese |
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- Spanish |
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Besides the aforementioned languages, basic support can be expected for additional 91 languages that were used during |
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the pretraining of the base model (see Appendix A of [XLM-R paper](https://arxiv.org/abs/1911.02116)). |
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## Scores |
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| Metric | Value | |
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|:--------------------|------:| |
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| Relevance (NDCG@10) | 0.451 | |
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Note that the relevance score is computed as an average over 14 retrieval datasets (see |
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[details below](#evaluation-metrics)). |
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## Inference Times |
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| GPU | Batch size 32 | |
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|:-----------|--------------:| |
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| NVIDIA A10 | 22 ms | |
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| NVIDIA T4 | 63 ms | |
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The inference times only measure the time the model takes to process a single batch, it does not include pre- or |
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post-processing steps like the tokenization. |
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## Requirements |
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- Minimal Sinequa version: 11.10.0 |
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- GPU memory usage: 1060 MiB |
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Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch |
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size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which |
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can be around 0.5 to 1 GiB depending on the used GPU. |
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## Model Details |
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### Overview |
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- Number of parameters: 107 million |
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- Base language model: |
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[mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) |
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([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm)) |
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- Insensitive to casing and accents |
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- Training procedure: [MonoBERT](https://arxiv.org/abs/1901.04085) |
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### Training Data |
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- MS MARCO Passage Ranking |
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([Paper](https://arxiv.org/abs/1611.09268), |
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[Official Page](https://microsoft.github.io/msmarco/), |
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[English & translated datasets on the HF dataset hub](https://huggingface.co/datasets/unicamp-dl/mmarco)) |
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- Original English dataset |
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- Translated datasets for the other eight supported languages |
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### Evaluation Metrics |
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To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the |
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[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English. |
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| Dataset | NDCG@10 | |
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|:------------------|--------:| |
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| Average | 0.451 | |
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| Arguana | 0.527 | |
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| CLIMATE-FEVER | 0.167 | |
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| DBPedia Entity | 0.343 | |
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| FEVER | 0.698 | |
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| FiQA-2018 | 0.297 | |
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| HotpotQA | 0.648 | |
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| MS MARCO | 0.409 | |
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| NFCorpus | 0.317 | |
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| NQ | 0.430 | |
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| Quora | 0.761 | |
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| SCIDOCS | 0.135 | |
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| SciFact | 0.597 | |
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| TREC-COVID | 0.670 | |
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| Webis-Touche-2020 | 0.311 | |
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We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its |
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multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics |
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for the existing languages. |
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| Language | NDCG@10 | |
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|:----------------------|--------:| |
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| Chinese (simplified) | 0.414 | |
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| French | 0.382 | |
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| German | 0.320 | |
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| Japanese | 0.479 | |
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| Spanish | 0.418 | |
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