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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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- 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 passage vectors are stored in our vector index and the query vector is used at query time to look up relevant passages 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 | Quantization type | Batch size 1 | Batch size 32 |
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|:------------------------------------------|:------------------|---------------:|---------------:|
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| NVIDIA A10 | FP16 | 1 ms | 4 ms |
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| NVIDIA A10 | FP32 | 2 ms | 13 ms |
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| NVIDIA T4 | FP16 | 1 ms | 13 ms |
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| NVIDIA T4 | FP32 | 2 ms | 52 ms |
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| NVIDIA L4 | FP16 | 1 ms | 5 ms |
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| NVIDIA L4 | FP32 | 2 ms | 18 ms |
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## Gpu Memory usage
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| Quantization type | Memory |
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|:-------------------------------------------------|-----------:|
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| FP16 | 300 MiB |
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| FP32 | 500 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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## Requirements
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- Minimal Sinequa version: 11.10.0
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- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
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- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
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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: A first model was trained with query-passage pairs, using the in-batch negative strategy with [this loss](https://www.sbert.net/docs/package_reference/losses.html#multiplenegativesrankingloss). A second model was then trained on query-passage-negative triplets with negatives mined from the previous model, like a variant of [ANCE](https://arxiv.org/pdf/2007.00808.pdf) but with different hyper parameters.
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### Training Data
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The model was trained on a Sinequa curated version of Google's [Natural Questions](https://ai.google.com/research/NaturalQuestions).
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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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| | |
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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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