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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.
+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 requests.
 
-# Neural Search models
+# Neural Search Models
 
-Sinequa Search uses a technology called Neural Search. Neural Search is a hybrid search solution based on both Keyword Search and Vector Search.
-This search workflow implies two types of models for which we deliver various versions here.
+Sinequa Search uses a technology called Neural Search. Neural Search is a hybrid search solution based on both Keyword
+Search and Vector Search. This search workflow implies two types of models for which we deliver various versions here.
 
-The two collections below bring together the recommended model combinations for English only content and multilingual content.
+The two collections below bring together the recommended model combinations for:
+
+- [English only content](https://huggingface.co/collections/sinequa/best-neural-search-models-for-english-content-673f2d584d396ce427ade232)
+- [multilingual content](https://huggingface.co/collections/sinequa/best-neural-search-models-for-multilingual-content-673f2ec7c6fb004642a24444)
 
 ## 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.
+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.
+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    |
+| Model                                                                                       | Languages                                       | Relevance | Inference Time | GPU Memory |
+|---------------------------------------------------------------------------------------------|-------------------------------------------------|-----------|----------------|------------|
+| [vectorizer-v1-S-en](https://huggingface.co/sinequa/vectorizer-v1-S-en)                     | en                                              | 0.456     | 52 ms          | 330 MiB    |
+| [vectorizer-v1-S-multilingual](https://huggingface.co/sinequa/vectorizer-v1-S-multilingual) | de, en, es, fr                                  | 0.448     | 51 ms          | 580 MiB    |
+| [vectorizer.vanilla](https://huggingface.co/sinequa/vectorizer.vanilla)                     | en                                              | 0.639     | 53 ms          | 330 MiB    |
+| [vectorizer.raspberry](https://huggingface.co/sinequa/vectorizer.raspberry)                 | de, en, es, fr, it, ja, nl, pt, zs              | 0.613     | 52 ms          | 610 MiB    |
+| [vectorizer.hazelnut](https://huggingface.co/sinequa/vectorizer.hazelnut)                   | de, en, es, fr, it, ja, nl, pl, pt, zs          | 0.590     | 52 ms          | 610 MiB    |
+| [vectorizer.guava](https://huggingface.co/sinequa/vectorizer.guava)                         | de, en, es, fr, it, ja, nl, pl, pt, zh-trad, zs | 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   |
+Passage Rankers are models which produce a relevance score given a query-passage pair and are 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](https://huggingface.co/sinequa/passage-ranker-v1-XS-en)                     | en                                     | 0.438     | 20 ms          | 170 MiB    |
+| [passage-ranker-v1-XS-multilingual](https://huggingface.co/sinequa/passage-ranker-v1-XS-multilingual) | de, en, es, fr                         | 0.453     | 21 ms          | 300 MiB    |
+| [passage-ranker-v1-L-en](https://huggingface.co/sinequa/passage-ranker-v1-L-en)                       | en                                     | 0.466     | 356 ms         | 1060 MiB   |
+| [passage-ranker-v1-L-multilingual](https://huggingface.co/sinequa/passage-ranker-v1-L-multilingual)   | de, en, es, fr                         | 0.471     | 357 ms         | 1130 MiB   |
+| [passage-ranker.chocolate](https://huggingface.co/sinequa/passage-ranker.chocolate)                   | en                                     | 0.484     | 64 ms          | 550 MiB    |
+| [passage-ranker.strawberry](https://huggingface.co/sinequa/passage-ranker.strawberry)                 | de, en, es, fr, it, ja, nl, pt, zs     | 0.451     | 63 ms          | 1060 MiB   |
+| [passage-ranker.mango](https://huggingface.co/sinequa/passage-ranker.mango)                           | de, en, es, fr, it, ja, nl, pt, zs     | 0.480     | 358 ms         | 1070 MiB   |
+| [passage-ranker.pistachio](https://huggingface.co/sinequa/passage-ranker.pistachio)                   | de, en, es, fr, it, ja, nl, pl, pt, zs | 0.380     | 358 ms         | 1070 MiB   |