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---
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
language:
- de
- en
- es
- fr
- it
- nl
- ja
- pt
- zh
---
# Model Card for `vectorizer.raspberry`
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.
Model name: `vectorizer.raspberry`
## Supported Languages
The model was trained and tested in the following languages:
- English
- French
- German
- Spanish
- Italian
- Dutch
- Japanese
- Portuguese
- Chinese (simplified)
Besides these languages, basic support can be expected for additional 91 languages that were used during the pretraining of the base model (see Appendix A of XLM-R paper).
## Scores
| Metric | Value |
|:-----------------------|------:|
| Relevance (Recall@100) | 0.613 |
Note that the relevance score is computed as an average over 14 retrieval datasets (see
[details below](#evaluation-metrics)).
## Inference Times
| GPU | Quantization type | Batch size 1 | Batch size 32 |
|:------------------------------------------|:------------------|---------------:|---------------:|
| NVIDIA A10 | FP16 | 1 ms | 5 ms |
| NVIDIA A10 | FP32 | 2 ms | 18 ms |
| NVIDIA T4 | FP16 | 1 ms | 12 ms |
| NVIDIA T4 | FP32 | 3 ms | 52 ms |
| NVIDIA L4 | FP16 | 2 ms | 5 ms |
| NVIDIA L4 | FP32 | 4 ms | 24 ms |
## Gpu Memory usage
| Quantization type | Memory |
|:-------------------------------------------------|-----------:|
| FP16 | 550 MiB |
| FP32 | 1050 MiB |
Note that GPU memory usage only includes how much GPU memory the actual model consumes on an NVIDIA T4 GPU with a batch
size of 32. It does not include the fix amount of memory that is consumed by the ONNX Runtime upon initialization which
can be around 0.5 to 1 GiB depending on the used GPU.
## Requirements
- Minimal Sinequa version: 11.10.0
- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
## Model Details
### Overview
- Number of parameters: 107 million
- Base language
model: [mMiniLMv2-L6-H384-distilled-from-XLMR-Large](https://huggingface.co/nreimers/mMiniLMv2-L6-H384-distilled-from-XLMR-Large) ([Paper](https://arxiv.org/abs/2012.15828), [GitHub](https://github.com/microsoft/unilm/tree/master/minilm))
- Insensitive to casing and accents
- Output dimensions: 256 (reduced with an additional dense layer)
- 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
### Training Data
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. In addition to that, this model has been trained on the datasets cited in [this paper](https://arxiv.org/pdf/2108.13897.pdf) on the 9 aforementioned languages.
### Evaluation Metrics
To determine the relevance score, we averaged the results that we obtained when evaluating on the datasets of the
[BEIR benchmark](https://github.com/beir-cellar/beir). Note that all these datasets are in English.
| Dataset | Recall@100 |
|:------------------|-----------:|
| Average | 0.613 |
| | |
| Arguana | 0.957 |
| CLIMATE-FEVER | 0.468 |
| DBPedia Entity | 0.377 |
| FEVER | 0.820 |
| FiQA-2018 | 0.639 |
| HotpotQA | 0.560 |
| MS MARCO | 0.845 |
| NFCorpus | 0.287 |
| NQ | 0.756 |
| Quora | 0.992 |
| SCIDOCS | 0.456 |
| SciFact | 0.906 |
| TREC-COVID | 0.100 |
| Webis-Touche-2020 | 0.413 |
We evaluated the model on the datasets of the [MIRACL benchmark](https://github.com/project-miracl/miracl) to test its multilingual capacities. Note that not all training languages are part of the benchmark, so we only report the metrics for the existing languages.
| Language | Recall@100 |
|:----------------------|-----------:|
| French | 0.650 |
| German | 0.528 |
| Spanish | 0.602 |
| Japanese | 0.614 |
| Chinese (simplified) | 0.680 |
|