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language: |
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- en |
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--- |
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# Model Card for `vectorizer-v1-S-en` |
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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-en` |
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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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## Scores |
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| Metric | Value | |
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|:-----------------------|------:| |
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| Relevance (Recall@100) | 0.456 | |
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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 | 52 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: 330 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: 29 million |
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- Base language model: [English BERT-Small](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) |
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- Insensitive to casing and accents |
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- Output dimensions: 256 (reduced with an additional dense layer) |
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- Training procedure: TBD |
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### Training Data |
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TBD |
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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 | 0.456 | |
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| Arguana | 0.832 | |
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| CLIMATE-FEVER | 0.342 | |
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| DBPedia Entity | 0.299 | |
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| FEVER | 0.660 | |
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| FiQA-2018 | 0.301 | |
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| HotpotQA | 0.434 | |
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| MS MARCO | 0.610 | |
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| NFCorpus | 0.159 | |
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| NQ | 0.671 | |
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| Quora | 0.966 | |
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| SCIDOCS | 0.194 | |
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| SciFact | 0.592 | |
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| TREC-COVID | 0.037 | |
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| Webis-Touche-2020 | 0.285 | |
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