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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- feature-extraction |
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- sentence-similarity |
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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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--- |
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# Model Card for `vectorizer-v1-S-multilingual` |
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This model is a vectorizer developed by Sinequa. It produces an embedding vector given a passage or a query. The |
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passage vectors are stored in our vector index and the query vector is used at query time to look up relevant passages |
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in the index. |
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Model name: `vectorizer-v1-S-multilingual` |
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## Supported Languages |
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The model was trained and tested in the following languages: |
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- English |
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- French |
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- German |
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- Spanish |
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## Scores |
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| Metric | Value | |
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|:-----------------------|------:| |
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| Relevance (Recall@100) | 0.448 | |
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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 1 (at query time) | Batch size 32 (at indexing) | |
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|:-----------|-----------------------------:|----------------------------:| |
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| NVIDIA A10 | 2 ms | 14 ms | |
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| NVIDIA T4 | 4 ms | 51 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: 580 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: 39 million |
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- Base language model: Homegrown Sinequa BERT-Small ([Paper](https://arxiv.org/abs/1908.08962)) pretrained in the four |
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supported languages |
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- Insensitive to casing and accents |
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- Training procedure: Query-passage pairs using in-batch negatives |
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### Training Data |
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- Natural Questions |
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([Paper](https://research.google/pubs/pub47761/), |
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[Official Page](https://github.com/google-research-datasets/natural-questions)) |
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- Original English dataset |
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- Translated datasets for the other three 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 | Recall@100 | |
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|:------------------|-----------:| |
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| Average | 0.448 | |
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| Arguana | 0.835 | |
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| CLIMATE-FEVER | 0.350 | |
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| DBPedia Entity | 0.287 | |
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| FEVER | 0.645 | |
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| FiQA-2018 | 0.305 | |
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| HotpotQA | 0.396 | |
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| MS MARCO | 0.533 | |
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| NFCorpus | 0.162 | |
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| NQ | 0.701 | |
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| Quora | 0.947 | |
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| SCIDOCS | 0.194 | |
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| SciFact | 0.580 | |
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| TREC-COVID | 0.051 | |
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| Webis-Touche-2020 | 0.289 | |
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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 | Recall@100 | |
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|:---------|-----------:| |
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| French | 0.583 | |
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| German | 0.524 | |
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| Spanish | 0.483 | |
