Update README.md

README.md

@@ -22,6 +22,8 @@ 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)
 
+We also deliver Question Answering models (a.k.a. Answer Finders) to be used on top of Neural Search to extract short answers from the most relevant contents.
+
 ## Vectorizer
 
 Vectorizers are models which produce an embedding vector given a passage or a query. The passage vectors are stored in
@@ -55,3 +57,13 @@ Here is an overview of the models we deliver publicly:
 | [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, zh-trad     | 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, zh-trad | 0.380     | 358 ms         | 1070 MiB   |
+
+## Answer Finder
+
+Answer Finder are models which extract a short answer from a given passage given a query. These models tend to be less used due to the wide adoption of LLMs of answer generation usecases.
+
+|Model                                                                                             |Languages     |de  |en  |es  |fr  |ja  | Inference Time | GPU Memory |
+|--------------------------------------------------------------------------------------------------|--------------|----|----|----|----|----|----------------|------------|
+|[answer-finder-v1-S-en](https://huggingface.co/sinequa/answer-finder-v1-S-en)                     |en            |70.6|79.5|54.1|0.5 |X   | 128 ms         |560 MiB     |
+|[answer-finder-v1-L-multilingual](https://huggingface.co/sinequa/answer-finder-v1-L-multilingual) |de, en, es, fr|90.8|75.0|67.1|73.4|X   | 362 ms         |1060 MiB    |
+|[answer-finder.yuzu](https://huggingface.co/sinequa/answer-finder.yuzu)                           |ja            |X   |X   |X   |X   |91.5| 361 ms         |1320 MiB    |