Create README.md

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
+  - pl
+---
+
+# Model Card for `vectorizer.guava`
+
+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.guava`
+
+## Supported Languages
+
+The model was trained and tested in the following languages:
+
+- English
+- French
+- German
+- Spanish
+- Italian
+- Dutch
+- Japanese
+- Portuguese
+- Chinese (simplified)
+- Chinese (traditional)
+- Polish
+
+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 |
+|:-------------------------------|------:|
+| English Relevance (Recall@100) | 0.616 |
+
+Note that the relevance scores are computed as an average over several 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 first 9 aforementioned languages.
+It has also been trained on [this dataset](https://huggingface.co/datasets/clarin-knext/msmarco-pl) for polish capacities, and a translated version of msmarco-zh for traditional chinese capacities.
+
+### Evaluation Metrics
+
+#### English
+
+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           |      0.616 |
+|                   |            |
+| Arguana           |      0.956 |
+| CLIMATE-FEVER     |      0.471 |
+| DBPedia Entity    |      0.379 |
+| FEVER             |      0.824 |
+| FiQA-2018         |      0.642 |
+| HotpotQA          |      0.579 |
+| MS MARCO          |      0.85 |
+| NFCorpus          |      0.289 |
+| NQ                |      0.765 |
+| Quora             |      0.993 |
+| SCIDOCS           |      0.467 |
+| SciFact           |      0.899 |
+| TREC-COVID        |      0.104 |
+| Webis-Touche-2020 |      0.407 |
+
+#### Polish
+
+This model has traditional chinese capacities, that are being evaluated over the same dev set at msmarco-zh, translated in traditional chinese.
+
+| Dataset                          | Recall@100 |
+|:---------------------------------|-----------:|
+| msmarco-zh-traditional           |      0.738 |
+
+In comparison, raspberry scores a 0.693 on this dataset.
+
+
+#### Other languages
+
+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                |      0.672 |
+| German                |      0.594 |
+| Spanish               |      0.632 |
+| Japanese              |      0.603 |
+| Chinese (simplified)  |      0.702 |