Initial model card (#2)

- initial model card (1a675a3de99c5a46842fbc3fa450de10d6db6f8b)

README.md

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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
+
+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        | Batch size 1 (at query time) | Batch size 32 (at indexing) |
+|:-----------|-----------------------------:|----------------------------:|
+| NVIDIA A10 |                         2 ms |                       19 ms |
+| NVIDIA T4  |                         4 ms |                       52 ms |
+
+The inference times only measure the time the model takes to process a single batch, it does not include pre- or
+post-processing steps like the tokenization.
+
+## Requirements
+
+- Minimal Sinequa version: 11.10.0
+- GPU memory usage: 610 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.
+
+## 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  |      0.680 |