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README.md

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+# About Sinequa
+
+Sinequa provides an Enterprise Search solution that lets you search through your company's internal documents. It uses Neural Search to provide the most relevant content for your search request.
+
+# Neural Search models
+
+Sinequa Search uses on a technology called Neural Search. Neural Search is an hybrid search solution based on both Keyword Search and Vector Searched.
+This search workflow implies two types of models for which we deliver various version here.
+
+The two collections below bring together the recommended model combinations for English only and multilingual content.
+
+## Vectorizer
+
+Vectorizers are models which produce 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.
+
+Here is an overview of the models we deliver publicly.
+
+| Model                          | Languages                   | Relevance | Inference Time | GPU Memory |
+|--------------------------------|-----------------------------|-----------|----------------|------------|
+| vectorizer-v1-S-en             | en                          | 0.456     | 52 ms          | 330 MiB    |
+| vectorizer-v1-S-multilingual   | de, en, es, fr             | 0.448     | 51 ms          | 580 MiB    |
+| vectorizer.vanilla             | en                          | 0.639     | 53 ms          | 330 MiB    |
+| vectorizer.raspberry           | de, en, es, fr, it, ja, nl, pt, zs | 0.613     | 52 ms          | 610 MiB    |
+| vectorizer.hazelnut            | de, en, es, fr, it, ja, nl, pt, zs, pl | 0.590     | 52 ms          | 610 MiB    |
+| vectorizer.guava               | de, en, es, fr, it, ja, nl, pt, zs, zh-trad, pl | 0.616     | 52 ms          | 610 MiB    |
+
+## Passage Ranker
+
+Passage Rankers are models which produce a relevance score given a query-passage pair and is used to order search results coming from Keyword and Vector search.
+
+Here is an overview of the models we deliver publicly.
+
+| Model                           | Languages                   | Relevance | Inference Time | GPU Memory |
+|---------------------------------|-----------------------------|-----------|----------------|------------|
+| passage-ranker-v1-XS-en         | en                          | 0.438     | 20 ms          | 170 MiB    |
+| passage-ranker-v1-XS-multilingual | de, en, es, fr             | 0.453     | 21 ms          | 300 MiB    |
+| passage-ranker-v1-L-en          | en                          | 0.466     | 356 ms         | 1060 MiB   |
+| passage-ranker-v1-L-multilingual | de, en, es, fr             | 0.471     | 357 ms         | 1130 MiB   |
+| passage-ranker.chocolate        | en                          | 0.484     | 64 ms          | 550 MiB    |
+| passage-ranker.strawberry       | de, en, es, fr, it, ja, nl, pt, zs | 0.451     | 63 ms          | 1060 MiB   |
+| passage-ranker.mango            | de, en, es, fr, it, ja, nl, pt, zs | 0.480     | 358 ms         | 1070 MiB   |
+| passage-ranker.pistachio        | de, en, es, fr, it, ja, nl, pt, zs, pl | 0.380     | 358 ms         | 1070 MiB   |