Steffen's review

README.md

@@ -7,31 +7,23 @@ sdk: static
 pinned: false
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-Sinequa Hugging Face homepage
-
 # About Sinequa
 
-Sinequa transforms how work gets done. Sinequa’s Assistants augment your company by augmenting employees with a knowledgeable, 
-accurate, secure work partner so they are more effective, more informed, more productive, and less stressed. Best of all, 
-Sinequa Assistants streamline workflows and automatically navigate the chaotic enterprise information landscape, so that employees 
-can skip the grind and focus on doing the kind of work that makes the most impact. Sinequa’s Assistants achieve this by combining 
-the power of comprehensive enterprise search with the ease of generative AI in a configurable and easily managed Assistant framework, 
-for an accurate, traceable, and fully secure conversational experience. Deploy an out-of-the-box Assistant or configure a tailored 
-experience and specialized workflow to augment your people and your company. For more information, visit www.sinequa.com.
+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 relies on a technology called Neural Search. Neural Search is an hybrid search solution based on both Key Word Search and Vector Searched.
-This search workflow implies three types of models for which we deliver various version here.
+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 context.
+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 model we deliver publicly here.
+Here is an overview of the models we deliver publicly.
 
 | Model                          | Languages                   | Relevance | Inference Time | GPU Memory |
 |--------------------------------|-----------------------------|-----------|----------------|------------|
@@ -44,9 +36,9 @@ Here is an overview of the model we deliver publicly here.
 
 ## 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 Key Word and Vector search.
+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 model we deliver publicly here.
+Here is an overview of the models we deliver publicly.
 
 | Model                           | Languages                   | Relevance | Inference Time | GPU Memory |
 |---------------------------------|-----------------------------|-----------|----------------|------------|
@@ -58,23 +50,3 @@ Here is an overview of the model we deliver publicly here.
 | 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   |
-
-
-## Answer Finder
-
-Answer Finder are extractive question answering models developed by Sinequa. Given a query and a passage, they produce two lists of logit scores corresponding 
-to the start token and end token of an answer.
-
-Here is an overview of the model we deliver publicly here.
-
-| Model                          | Languages       | de   | en   | es   | fr   | ja   |
-|--------------------------------|-----------------|------|------|------|------|------|
-| answer-finder-v1-S-en          | en              | 70.6 | 79.5 | 54.1 | 0.5  | X    |
-| answer-finder-v1-L-multilingual | de, en, es, fr | 90.8 | 75.0 | 67.1 | 73.4 | X    |
-| answer-finder.yuzu             | ja              | X    | X    | X    | X    | 91.5 |
-
-| Model                          | Inference Time | GPU Memory |
-|--------------------------------|----------------|------------|
-| answer-finder-v1-S-en          | 128 ms         | 560 MiB    |
-| answer-finder-v1-L-multilingual | 362 ms         | 1060 MiB   |
-| answer-finder.yuzu             | 361 ms         | 1320 MiB   |