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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.vanilla` |
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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.vanilla` |
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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) | 0.639 | |
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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 | 5 ms | |
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| NVIDIA A10 | FP32 | 2 ms | 20 ms | |
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| NVIDIA T4 | FP16 | 1 ms | 14 ms | |
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| NVIDIA T4 | FP32 | 2 ms | 53 ms | |
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| NVIDIA L4 | FP16 | 1 ms | 5 ms | |
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| NVIDIA L4 | FP32 | 3 ms | 25 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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- [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: 23 million |
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- Base language model: [English MiniLM-L6-H384](https://huggingface.co/nreimers/MiniLM-L6-H384-uncased) |
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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: Query-passage-negative triplets for datasets that have mined hard negative data, Query-passage pairs for the rest. Number of negatives is augmented with in-batch negative strategy. |
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### Training Data |
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The model have been trained using all datasets that are cited in the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model. |
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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 | 0.639 | |
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| | | |
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| Arguana | 0.969 | |
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| CLIMATE-FEVER | 0.509 | |
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| DBPedia Entity | 0.409 | |
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| FEVER | 0.839 | |
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| FiQA-2018 | 0.702 | |
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| HotpotQA | 0.609 | |
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| MS MARCO | 0.849 | |
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| NFCorpus | 0.315 | |
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| NQ | 0.786 | |
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| Quora | 0.995 | |
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| SCIDOCS | 0.497 | |
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| SciFact | 0.911 | |
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| TREC-COVID | 0.129 | |
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| Webis-Touche-2020 | 0.427 | |
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