diff --git a/docs/tasks.md b/docs/tasks.md
index 706ada1068..35b2b89623 100644
--- a/docs/tasks.md
+++ b/docs/tasks.md
@@ -437,6 +437,7 @@ The following tables give you an overview of the tasks in MTEB.
 | [MIRACLReranking](https://project-miracl.github.io/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Reranking | s2s | [Encyclopaedic, Written] | None | None |
 | [MIRACLRetrieval](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
 | [MIRACLRetrievalHardNegatives](http://miracl.ai/) (Zhang et al., 2023) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
+| [MIRACLVisionRetrieval](https://arxiv.org/pdf/2407.01449) (Radek Osmulski, 2025) | ['ara', 'ben', 'deu', 'eng', 'fas', 'fin', 'fra', 'hin', 'ind', 'jpn', 'kor', 'rus', 'spa', 'swa', 'tel', 'tha', 'yor', 'zho'] | DocumentUnderstanding | t2i | [Encyclopaedic] | None | None |
 | [MLQARetrieval](https://huggingface.co/datasets/mlqa) (Lewis et al., 2019) | ['ara', 'deu', 'eng', 'hin', 'spa', 'vie', 'zho'] | Retrieval | s2p | [Encyclopaedic, Written] | None | None |
 | [MLQuestions](https://github.com/McGill-NLP/MLQuestions) (Kulshreshtha et al., 2021) | ['eng'] | Retrieval | s2p | [Academic, Encyclopaedic, Written] | None | None |
 | [MLSUMClusteringP2P.v2](https://huggingface.co/datasets/mteb/mlsum) (Scialom et al., 2020) | ['deu', 'fra', 'rus', 'spa'] | Clustering | p2p | [News, Written] | None | None |
@@ -984,7 +985,7 @@ The following tables give you an overview of the tasks in MTEB.
 | apu | Apurinã | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | apw | Western Apache | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | apz | Safeyoka | Angan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ara | Arabic | Unclassified | 0 | 2 | 0 | 4 | 12 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 10 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 36 |
+| ara | Arabic | Unclassified | 0 | 2 | 0 | 4 | 12 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 10 | 2 | 0 | 0 | 0 | 0 | 2 | 0 | 37 |
 | arb | Standard Arabic | Afro-Asiatic | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
 | are | Western Arrarnta | Pama-Nyungan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | arl | Arabela | Zaparoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1031,7 +1032,7 @@ The following tables give you an overview of the tasks in MTEB.
 | bef | Benabena | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | bel | Belarusian | Indo-European | 0 | 0 | 0 | 4 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
 | bem | Bemba (Zambia) | Atlantic-Congo | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| ben | Bengali | Indo-European | 0 | 1 | 0 | 9 | 9 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 32 |
+| ben | Bengali | Indo-European | 0 | 1 | 0 | 9 | 9 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 33 |
 | beo | Beami | Bosavi | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | ber | Berber (Other) | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | beu | Blagar | Timor-Alor-Pantar | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1173,7 +1174,7 @@ The following tables give you an overview of the tasks in MTEB.
 | dah | Gwahatike | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | dan | Danish | Indo-European | 0 | 2 | 0 | 8 | 10 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 31 |
 | ded | Dedua | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| deu | German | Indo-European | 0 | 2 | 0 | 8 | 14 | 7 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 7 | 2 | 20 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 72 |
+| deu | German | Indo-European | 0 | 2 | 0 | 8 | 14 | 7 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 7 | 2 | 20 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 73 |
 | dgc | Casiguran Dumagat Agta | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | dgr | Dogrib | Athabaskan-Eyak-Tlingit | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | dgz | Daga | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1201,7 +1202,7 @@ The following tables give you an overview of the tasks in MTEB.
 | ell | Modern Greek (1453-) | Indo-European | 0 | 2 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 21 |
 | emi | Mussau-Emira | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | emp | Northern Emberá | Chocoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| eng | English | Indo-European | 0 | 3 | 49 | 20 | 161 | 21 | 7 | 14 | 22 | 5 | 0 | 3 | 1 | 13 | 9 | 113 | 13 | 2 | 1 | 6 | 7 | 3 | 24 | 497 |
+| eng | English | Indo-European | 0 | 3 | 49 | 20 | 161 | 21 | 7 | 15 | 22 | 5 | 0 | 3 | 1 | 13 | 9 | 113 | 13 | 2 | 1 | 6 | 7 | 3 | 24 | 498 |
 | enq | Enga | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | epo | Esperanto | Artificial Language | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
 | eri | Ogea | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1215,14 +1216,14 @@ The following tables give you an overview of the tasks in MTEB.
 | fai | Faiwol | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | fao | Faroese | Indo-European | 0 | 0 | 0 | 3 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
 | far | Fataleka | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fas | Persian | Indo-European | 0 | 1 | 0 | 6 | 28 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 | 2 | 41 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 94 |
+| fas | Persian | Indo-European | 0 | 1 | 0 | 6 | 28 | 5 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 8 | 2 | 41 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 95 |
 | ffm | Maasina Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | fij | Fijian | Austronesian | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
 | fil | Filipino | Austronesian | 0 | 1 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
-| fin | Finnish | Uralic | 0 | 1 | 0 | 5 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 24 |
+| fin | Finnish | Uralic | 0 | 1 | 0 | 5 | 6 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 6 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
 | fon | Fon | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
 | for | Fore | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| fra | French | Indo-European | 0 | 1 | 0 | 9 | 13 | 8 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 6 | 3 | 17 | 4 | 0 | 1 | 0 | 0 | 4 | 0 | 70 |
+| fra | French | Indo-European | 0 | 1 | 0 | 9 | 13 | 8 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 6 | 3 | 17 | 4 | 0 | 1 | 0 | 0 | 4 | 0 | 71 |
 | fry | Western Frisian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | fuc | Pulaar | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | fue | Borgu Fulfulde | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1279,7 +1280,7 @@ The following tables give you an overview of the tasks in MTEB.
 | hch | Huichol | Uto-Aztecan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | heb | Hebrew | Afro-Asiatic | 0 | 1 | 0 | 6 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 |
 | heg | Helong | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| hin | Hindi | Indo-European | 0 | 1 | 0 | 11 | 12 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 11 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 43 |
+| hin | Hindi | Indo-European | 0 | 1 | 0 | 11 | 12 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 11 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 44 |
 | hix | Hixkaryána | Cariban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | hla | Halia | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | hlt | Matu Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1311,7 +1312,7 @@ The following tables give you an overview of the tasks in MTEB.
 | imo | Imbongu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | ina | Interlingua (International Auxiliary Language Association) | Artificial Language | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | inb | Inga | Quechuan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| ind | Indonesian | Austronesian | 0 | 3 | 0 | 8 | 7 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 27 |
+| ind | Indonesian | Austronesian | 0 | 3 | 0 | 8 | 7 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 28 |
 | ino | Inoke-Yate | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | iou | Tuma-Irumu | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | ipi | Ipili | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1328,7 +1329,7 @@ The following tables give you an overview of the tasks in MTEB.
 | jid | Bu (Kaduna State) | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | jiv | Shuar | Chicham | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | jni | Janji | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| jpn | Japanese | Japonic | 0 | 3 | 0 | 7 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 14 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 42 |
+| jpn | Japanese | Japonic | 0 | 3 | 0 | 7 | 8 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 3 | 14 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 43 |
 | jvn | Caribbean Javanese | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | kab | Kabyle | Afro-Asiatic | 0 | 0 | 0 | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
 | kac | Kachin | Sino-Tibetan | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 |
@@ -1384,7 +1385,7 @@ The following tables give you an overview of the tasks in MTEB.
 | knj | Western Kanjobal | Mayan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | knv | Tabo | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | kon | Kongo | Unclassified | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
-| kor | Korean | Koreanic | 0 | 2 | 0 | 6 | 8 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 10 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 39 |
+| kor | Korean | Koreanic | 0 | 2 | 0 | 6 | 8 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 3 | 1 | 10 | 3 | 0 | 0 | 0 | 0 | 2 | 0 | 40 |
 | kos | Kosraean | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | kpf | Komba | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | kpg | Kapingamarangi | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1709,7 +1710,7 @@ The following tables give you an overview of the tasks in MTEB.
 | ruf | Luguru | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | rug | Roviana | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | run | Rundi | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
-| rus | Russian | Indo-European | 0 | 2 | 0 | 7 | 18 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 17 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 64 |
+| rus | Russian | Indo-European | 0 | 2 | 0 | 7 | 18 | 6 | 0 | 1 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 17 | 4 | 0 | 0 | 0 | 0 | 2 | 0 | 65 |
 | rwo | Rawa | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | sab | Buglere | Chibchan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | sag | Sango | Atlantic-Congo | 0 | 0 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 |
@@ -1748,7 +1749,7 @@ The following tables give you an overview of the tasks in MTEB.
 | soq | Kanasi | Dagan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | sot | Southern Sotho | Atlantic-Congo | 0 | 0 | 0 | 1 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 |
 | soy | Miyobe | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| spa | Spanish | Indo-European | 0 | 2 | 0 | 6 | 13 | 4 | 0 | 3 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 15 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 57 |
+| spa | Spanish | Indo-European | 0 | 2 | 0 | 6 | 13 | 4 | 0 | 4 | 0 | 0 | 0 | 0 | 1 | 3 | 2 | 15 | 4 | 0 | 0 | 0 | 0 | 4 | 0 | 58 |
 | spl | Selepet | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | spm | Akukem | Ramu | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | spp | Supyire Senoufo | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1772,7 +1773,7 @@ The following tables give you an overview of the tasks in MTEB.
 | sus | Susu | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | suz | Sunwar | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | svk | Slovakian Sign Language | Sign Language | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| swa | Swahili (macrolanguage) | Atlantic-Congo | 0 | 1 | 0 | 1 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| swa | Swahili (macrolanguage) | Atlantic-Congo | 0 | 1 | 0 | 1 | 7 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 |
 | swe | Swedish | Indo-European | 0 | 1 | 0 | 6 | 9 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 |
 | swg | Swabian | Indo-European | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | swh | Swahili (individual language) | Atlantic-Congo | 0 | 0 | 0 | 3 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 |
@@ -1797,7 +1798,7 @@ The following tables give you an overview of the tasks in MTEB.
 | tcz | Thado Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tdt | Tetun Dili | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tee | Huehuetla Tepehua | Totonacan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tel | Telugu | Dravidian | 0 | 1 | 0 | 7 | 7 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
+| tel | Telugu | Dravidian | 0 | 1 | 0 | 7 | 7 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 5 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
 | ter | Tereno | Arawakan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tet | Tetum | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tew | Tewa (USA) | Kiowa-Tanoan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1806,7 +1807,7 @@ The following tables give you an overview of the tasks in MTEB.
 | tgl | Tagalog | Austronesian | 0 | 0 | 0 | 5 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 11 |
 | tgo | Sudest | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tgp | Tangoa | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| tha | Thai | Tai-Kadai | 0 | 1 | 0 | 6 | 8 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 25 |
+| tha | Thai | Tai-Kadai | 0 | 1 | 0 | 6 | 8 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
 | tif | Tifal | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tim | Timbe | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | tir | Tigrinya | Afro-Asiatic | 0 | 0 | 0 | 2 | 2 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 8 |
@@ -1930,7 +1931,7 @@ The following tables give you an overview of the tasks in MTEB.
 | yle | Yele | Unclassified | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | yml | Iamalele | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | yon | Yongkom | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| yor | Yoruba | Atlantic-Congo | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 16 |
+| yor | Yoruba | Atlantic-Congo | 0 | 0 | 0 | 4 | 5 | 3 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 |
 | yrb | Yareba | Yareban | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | yre | Yaouré | Mande | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | yss | Yessan-Mayo | Sepik | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1955,7 +1956,7 @@ The following tables give you an overview of the tasks in MTEB.
 | zaw | Mitla Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | zca | Coatecas Altas Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | zga | Kinga | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| zho | Chinese | Unclassified | 0 | 2 | 0 | 4 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 |
+| zho | Chinese | Unclassified | 0 | 2 | 0 | 4 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27 |
 | zia | Zia | Nuclear Trans New Guinea | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | ziw | Zigula | Atlantic-Congo | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | zlm | Malay (individual language) | Austronesian | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
@@ -1974,7 +1975,7 @@ The following tables give you an overview of the tasks in MTEB.
 | zty | Yatee Zapotec | Otomanguean | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
 | zul | Zulu | Atlantic-Congo | 0 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 |
 | zyp | Zyphe Chin | Sino-Tibetan | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 |
-| Total | None | None | None | 0 | 55 | 49 | 1494 | 846 | 316 | 7 | 23 | 22 | 5 | 0 | 3 | 28 | 92 | 56 | 593 | 88 | 2 | 2 | 6 | 7 | 37 | 24 |
+| Total | None | None | None | 0 | 55 | 49 | 1494 | 846 | 316 | 7 | 41 | 22 | 5 | 0 | 3 | 28 | 92 | 56 | 593 | 88 | 2 | 2 | 6 | 7 | 37 | 24 |
 
 <!-- TASK LANG TABLE END -->
 
diff --git a/docs/usage/usage.md b/docs/usage/usage.md
index a5b9319311..d6c06a540b 100644
--- a/docs/usage/usage.md
+++ b/docs/usage/usage.md
@@ -456,12 +456,15 @@ There are times you may want to cache the embeddings so you can re-use them. Thi
 
 ```python
 # define your task(s) and model above as normal
-...
+task = mteb.get_task("LccSentimentClassification")
+model = mteb.get_model("minishlab/M2V_base_glove_subword")
+evaluation = mteb.MTEB(tasks=[task])
+
 # wrap the model with the cache wrapper
 from mteb.models.cache_wrapper import CachedEmbeddingWrapper
-model_with_cached_emb = CachedEmbeddingWrapper(model, cache_path='<path_to_cache_dir>')
+model_with_cached_emb = CachedEmbeddingWrapper(model, cache_path='path_to_cache_dir')
 # run as normal
-evaluation.run(model, ...)
+evaluation.run(model_with_cached_emb)
 ```
 
 If you want to directly access the cached embeddings (e.g. for subsequent analyses) follow this example:
@@ -471,8 +474,8 @@ import numpy as np
 from mteb.models.cache_wrapper import TextVectorMap
 
 # Access the memory-mapped file and convert to array
-vector_map = TextVectorMap("<path_to_cache_dir>/AppsRetrieval")
-vector_map.load(name="AppsRetrieval")
+vector_map = TextVectorMap("path_to_cache_dir/LccSentimentClassification")
+vector_map.load(name="LccSentimentClassification")
 vectors = np.asarray(vector_map.vectors)
 
 # Remove all "placeholders" in the embedding cache
diff --git a/mteb/leaderboard/table.py b/mteb/leaderboard/table.py
index fbc01496e8..5286680dc9 100644
--- a/mteb/leaderboard/table.py
+++ b/mteb/leaderboard/table.py
@@ -258,7 +258,7 @@ def apply_styling(
     joint_table[score_columns] = joint_table[score_columns].map(format_scores)
     joint_table_style = joint_table.style.format(
         {
-            **{column: "{:.2f}" for column in score_columns},
+            **dict.fromkeys(score_columns, "{:.2f}"),
             "Rank (Borda)": "{:.0f}",
         },
         na_rep="",
diff --git a/mteb/models/cache_wrapper.py b/mteb/models/cache_wrapper.py
index f27e58e48a..fe38ea6310 100644
--- a/mteb/models/cache_wrapper.py
+++ b/mteb/models/cache_wrapper.py
@@ -155,8 +155,8 @@ def load(self, name: str | None = None) -> None:
                     self.vectors = np.memmap(
                         self.vectors_file, dtype="float32", mode="r+"
                     )
-                    self.vectors = self.vectors.reshape(-1, self.vector_dim)
-                    logger.info(f"Loaded vectors file with shape: {self.vectors.shape}")
+                    self.vectors = self.vectors.reshape(-1, self.vector_dim)  # type: ignore
+                    logger.info(f"Loaded vectors file with shape: {self.vectors.shape}")  # type: ignore
                 else:
                     logger.warning(
                         "Vector dimension not set. Unable to load vectors file."
@@ -214,22 +214,30 @@ def __init__(self, model: Encoder, cache_path: str | Path):
         logger.info("Initialized CachedEmbeddingWrapper")
 
     def encode(
-        self, texts: list[str], batch_size: int = 32, task_name: str = None, **kwargs
+        self,
+        texts: list[str],
+        batch_size: int = 32,
+        task_name: str | None = None,
+        **kwargs,
     ) -> np.ndarray:
         """Encode texts using the wrapped model, with caching"""
+        _task_name = task_name or "no_task_name"
+
         try:
             results = []
             uncached_texts = []
             uncached_indices = []
 
             # Initialize cache
-            if task_name not in self.cache_dict:
-                self.cache_dict[task_name] = TextVectorMap(self.cache_path / task_name)
-                self.cache_dict[task_name].load(name=task_name)
+            if _task_name not in self.cache_dict:
+                self.cache_dict[_task_name] = TextVectorMap(
+                    self.cache_path / _task_name
+                )
+                self.cache_dict[_task_name].load(name=_task_name)
 
             # Check cache for each text
             for i, text in enumerate(texts):
-                vector = self.cache_dict[task_name].get_vector(text)
+                vector = self.cache_dict[_task_name].get_vector(text)
                 if vector is not None:
                     results.append(vector)
                 else:
@@ -240,16 +248,19 @@ def encode(
             if uncached_texts:
                 logger.info(f"Encoding {len(uncached_texts)} new texts")
                 new_vectors = self._model.encode(
-                    uncached_texts, batch_size=batch_size, **kwargs
+                    uncached_texts,
+                    batch_size=batch_size,
+                    task_name=task_name,  # type: ignore
+                    **kwargs,
                 )
                 if isinstance(new_vectors, torch.Tensor):
                     new_vectors = new_vectors.cpu().numpy()
 
                 # Add new vectors to cache
                 for text, vector in zip(uncached_texts, new_vectors):
-                    self.cache_dict[task_name].add(text, vector)
+                    self.cache_dict[_task_name].add(text, vector)
                 results.extend(new_vectors)
-                self.cache_dict[task_name].save()
+                self.cache_dict[_task_name].save()
             else:
                 logger.info("All texts found in cache")
 
@@ -287,7 +298,7 @@ def __getattr__(self, name: str) -> Any:
 
     def __dir__(self) -> list[str]:
         """Return all attributes from both this class and the wrapped model"""
-        return list(set(super().__dir__() + dir(self._model)))
+        return list(set(super().__dir__() + dir(self._model)))  # type: ignore
 
     def __del__(self):
         self.close()
diff --git a/mteb/models/cadet_models.py b/mteb/models/cadet_models.py
index 978f542271..b144dcee7c 100644
--- a/mteb/models/cadet_models.py
+++ b/mteb/models/cadet_models.py
@@ -1,7 +1,8 @@
 from __future__ import annotations
 
-from mteb.model_meta import ModelMeta, sentence_transformers_loader
+from mteb.model_meta import ModelMeta
 from mteb.models.bge_models import bge_m3_training_data
+from mteb.models.sentence_transformers_models import sentence_transformers_loader
 
 cadet_training_data = {
     # we train with the corpora of FEVER, MSMARCO, and DBPEDIA. We only train with synthetic generated queries.
diff --git a/mteb/models/overview.py b/mteb/models/overview.py
index 0f59cb1b7b..fe72be01fe 100644
--- a/mteb/models/overview.py
+++ b/mteb/models/overview.py
@@ -68,6 +68,7 @@
     promptriever_models,
     qodo_models,
     qtack_models,
+    qwen3_models,
     repllama_models,
     rerankers_custom,
     rerankers_monot5_based,
@@ -144,6 +145,7 @@
     promptriever_models,
     qodo_models,
     qtack_models,
+    qwen3_models,
     repllama_models,
     rerankers_custom,
     rerankers_monot5_based,
diff --git a/mteb/models/qwen3_models.py b/mteb/models/qwen3_models.py
new file mode 100644
index 0000000000..e70e01b3e0
--- /dev/null
+++ b/mteb/models/qwen3_models.py
@@ -0,0 +1,186 @@
+from __future__ import annotations
+
+from mteb.encoder_interface import Encoder, PromptType
+from mteb.model_meta import ModelMeta
+from mteb.models.instruct_wrapper import InstructSentenceTransformerModel
+
+
+def instruction_template(
+    instruction: str, prompt_type: PromptType | None = None
+) -> str:
+    if not instruction or prompt_type == PromptType.passage:
+        return ""
+    if isinstance(instruction, dict):
+        if prompt_type is None:
+            instruction = list(instruction.values())[0]  # TODO
+        else:
+            instruction = instruction[prompt_type]
+    return f"Instruct: {instruction}\nQuery:"
+
+
+multilingual_langs = [
+    "afr-Latn",
+    "ara-Arab",
+    "aze-Latn",
+    "bel-Cyrl",
+    "bul-Cyrl",
+    "ben-Beng",
+    "cat-Latn",
+    "ceb-Latn",
+    "ces-Latn",
+    "cym-Latn",
+    "dan-Latn",
+    "deu-Latn",
+    "ell-Grek",
+    "eng-Latn",
+    "spa-Latn",
+    "est-Latn",
+    "eus-Latn",
+    "fas-Arab",
+    "fin-Latn",
+    "fra-Latn",
+    "glg-Latn",
+    "guj-Gujr",
+    "heb-Hebr",
+    "hin-Deva",
+    "hrv-Latn",
+    "hat-Latn",
+    "hun-Latn",
+    "hye-Armn",
+    "ind-Latn",
+    "isl-Latn",
+    "ita-Latn",
+    "jpn-Jpan",
+    "jav-Latn",
+    "kat-Geor",
+    "kaz-Cyrl",
+    "khm-Khmr",
+    "kan-Knda",
+    "kor-Hang",
+    "kir-Cyrl",
+    "lao-Laoo",
+    "lit-Latn",
+    "lav-Latn",
+    "mkd-Cyrl",
+    "mal-Mlym",
+    "mon-Cyrl",
+    "mar-Deva",
+    "msa-Latn",
+    "mya-Mymr",
+    "nep-Deva",
+    "nld-Latn",
+    "nor-Latn",
+    "pan-Guru",
+    "pol-Latn",
+    "por-Latn",
+    "que-Latn",
+    "ron-Latn",
+    "rus-Cyrl",
+    "sin-Sinh",
+    "slk-Latn",
+    "slv-Latn",
+    "swa-Latn",
+    "tam-Taml",
+    "tel-Telu",
+    "tha-Thai",
+    "tgl-Latn",
+    "tur-Latn",
+    "ukr-Cyrl",
+    "urd-Arab",
+    "vie-Latn",
+    "yor-Latn",
+    "zho-Hans",
+]
+
+training_data = {
+    "T2Retrieval": ["train"],
+    "DuRetrieval": ["train"],
+    "MMarcoReranking": ["train"],
+    "CMedQAv2-reranking": ["train"],
+    "NQ": ["train"],
+    "MSMARCO": ["train"],
+    "HotpotQA": ["train"],
+    "FEVER": ["train"],
+    "MrTidyRetrieval": ["train"],
+    "MIRACLRetrieval": ["train"],
+    "CodeSearchNet": ["train"],
+}
+
+
+def q3e_instruct_loader(model_name_or_path: str, revision: str, **kwargs) -> Encoder:
+    model = InstructSentenceTransformerModel(
+        model_name_or_path,
+        revision=revision,
+        instruction_template=instruction_template,
+        apply_instruction_to_passages=False,
+        **kwargs,
+    )
+    encoder = model.model._first_module()
+    if encoder.auto_model.config._attn_implementation == "flash_attention_2":
+        # The Qwen3 code only use left padding in flash_attention_2 mode.
+        encoder.tokenizer.padding_side = "left"
+    return model
+
+
+Qwen3_Embedding_0B6 = ModelMeta(
+    loader=q3e_instruct_loader,
+    name="Qwen/Qwen3-Embedding-0.6B",
+    languages=multilingual_langs,
+    open_weights=True,
+    revision="b22da495047858cce924d27d76261e96be6febc0",  # Commit of @tomaarsen
+    release_date="2025-06-05",
+    n_parameters=595776512,
+    memory_usage_mb=2272,
+    embed_dim=1024,
+    max_tokens=32768,
+    license="apache-2.0",
+    reference="https://huggingface.co/Qwen/Qwen3-Embedding-0.6B",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=training_data,
+)
+
+Qwen3_Embedding_4B = ModelMeta(
+    loader=q3e_instruct_loader,
+    name="Qwen/Qwen3-Embedding-4B",
+    languages=multilingual_langs,
+    open_weights=True,
+    revision="636cd9bf47d976946cdbb2b0c3ca0cb2f8eea5ff",  # Commit of @tomaarsen
+    release_date="2025-06-05",
+    n_parameters=4021774336,
+    memory_usage_mb=15341,
+    embed_dim=2560,
+    max_tokens=32768,
+    license="apache-2.0",
+    reference="https://huggingface.co/Qwen/Qwen3-Embedding-4B",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=training_data,
+)
+
+Qwen3_Embedding_8B = ModelMeta(
+    loader=q3e_instruct_loader,
+    name="Qwen/Qwen3-Embedding-8B",
+    languages=multilingual_langs,
+    open_weights=True,
+    revision="4e423935c619ae4df87b646a3ce949610c66241c",  # Commit of @tomaarsen
+    release_date="2025-06-05",
+    n_parameters=7567295488,
+    memory_usage_mb=28866,
+    embed_dim=4096,
+    max_tokens=32768,
+    license="apache-2.0",
+    reference="https://huggingface.co/Qwen/Qwen3-Embedding-8B",
+    similarity_fn_name="cosine",
+    framework=["Sentence Transformers", "PyTorch"],
+    use_instructions=True,
+    public_training_code=None,
+    public_training_data=None,
+    training_datasets=training_data,
+)
diff --git a/mteb/tasks/Image/Any2AnyRetrieval/multilingual/MIRACLVisionRetrieval.py b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/MIRACLVisionRetrieval.py
new file mode 100644
index 0000000000..0f9415aff1
--- /dev/null
+++ b/mteb/tasks/Image/Any2AnyRetrieval/multilingual/MIRACLVisionRetrieval.py
@@ -0,0 +1,178 @@
+from __future__ import annotations
+
+import datasets
+
+from mteb.abstasks.Image.AbsTaskAny2AnyRetrieval import AbsTaskAny2AnyRetrieval
+from mteb.abstasks.MultilingualTask import MultilingualTask
+from mteb.abstasks.TaskMetadata import TaskMetadata
+
+_EVAL_SPLIT = "default"
+
+_LANGUAGES = {
+    "ar": ["ara-Arab"],
+    "bn": ["ben-Beng"],
+    "de": ["deu-Latn"],
+    "en": ["eng-Latn"],
+    "es": ["spa-Latn"],
+    "fa": ["fas-Arab"],
+    "fi": ["fin-Latn"],
+    "fr": ["fra-Latn"],
+    "hi": ["hin-Deva"],
+    "id": ["ind-Latn"],
+    "ja": ["jpn-Jpan"],
+    "ko": ["kor-Kore"],
+    "ru": ["rus-Cyrl"],
+    "sw": ["swa-Latn"],
+    "te": ["tel-Telu"],
+    "th": ["tha-Thai"],
+    "yo": ["yor-Latn"],
+    "zh": ["zho-Hans"],
+}
+
+
+def _load_miracl_data(
+    path: str,
+    langs: list,
+    splits: str,
+    cache_dir: str | None = None,
+    revision: str | None = None,
+    trust_remote_code: bool = False,
+):
+    corpus = {lang: dict.fromkeys(splits) for lang in langs}
+    queries = {lang: dict.fromkeys(splits) for lang in langs}
+    relevant_docs = {lang: dict.fromkeys(splits) for lang in langs}
+
+    split = _EVAL_SPLIT
+
+    for lang in langs:
+        # Load corpus data (Can be several millions for languages)
+        corpus_identifier = f"corpus-{lang}"
+        corpus_data = datasets.load_dataset(
+            path,
+            corpus_identifier,
+            cache_dir=cache_dir,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+        )
+
+        images_identifier = f"images-{lang}"
+        images_data = datasets.load_dataset(
+            path,
+            images_identifier,
+            cache_dir=cache_dir,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+        )
+
+        # For text data, it would look like this, just use _id column
+
+        imgid2docid = {
+            str(ex["image_id"]): str(ex["_id"])
+            for ex in corpus_data[split]  # e.g. “train”, “validation”, etc.
+        }
+
+        images_data = images_data.map(
+            lambda x: {
+                "id": imgid2docid[str(x["file_name"])],
+                # "modality": "text",
+                "modality": "image",
+                "text": None,
+            },
+            remove_columns=["file_name"],
+        )
+
+        corpus[lang][split] = images_data[split]
+
+        # Load queries data
+        queries_identifier = f"queries-{lang}"
+        queries_data = datasets.load_dataset(
+            path,
+            queries_identifier,
+            cache_dir=cache_dir,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+        )
+        queries_data = queries_data.map(
+            lambda x: {
+                "id": str(x["_id"]),
+                "text": x["text"],
+                "modality": "text",
+                "image": None,
+            },
+            remove_columns=["_id"],
+        )
+        queries[lang][split] = queries_data[split]
+
+        # Load relevant documents data
+        qrels_identifier = f"qrels-{lang}"
+        qrels_data = datasets.load_dataset(
+            path,
+            qrels_identifier,
+            cache_dir=cache_dir,
+            revision=revision,
+            trust_remote_code=trust_remote_code,
+        )
+        relevant_docs[lang][split] = {}
+        for row in qrels_data[split]:
+            query_id = str(row["query-id"])
+            doc_id = str(row["corpus-id"])
+            score = row["score"]
+            if query_id not in relevant_docs[lang][split]:
+                relevant_docs[lang][split][query_id] = {}
+            relevant_docs[lang][split][query_id][doc_id] = score
+
+    corpus = datasets.DatasetDict(corpus)
+    queries = datasets.DatasetDict(queries)
+    relevant_docs = datasets.DatasetDict(relevant_docs)
+
+    return corpus, queries, relevant_docs
+
+
+class MIRACLVisionRetrieval(MultilingualTask, AbsTaskAny2AnyRetrieval):
+    metadata = TaskMetadata(
+        name="MIRACLVisionRetrieval",
+        description="Retrieve associated pages according to questions.",
+        reference="https://arxiv.org/pdf/2407.01449",
+        dataset={
+            "path": "nvidia/miracl-vision",
+            "revision": "309e1696433408fbd555959cf1da968f3814f8b6",
+        },
+        type="DocumentUnderstanding",
+        category="t2i",
+        eval_splits=["default"],
+        eval_langs=_LANGUAGES,
+        main_score="ndcg_at_5",
+        date=("2025-03-01", "2025-06-01"),
+        domains=["Encyclopaedic"],
+        task_subtypes=["Image Text Retrieval"],
+        license="cc-by-sa-4.0",
+        annotations_creators="derived",
+        dialect=[],
+        modalities=["text", "image"],
+        sample_creation="created",
+        bibtex_citation=r"""
+@article{osmulski2025miraclvisionlargemultilingualvisual,
+  author = {Radek Osmulski and Gabriel de Souza P. Moreira and Ronay Ak and Mengyao Xu and Benedikt Schifferer and Even Oldridge},
+  eprint = {2505.11651},
+  journal = {arxiv},
+  title = {{MIRACL-VISION: A Large, multilingual, visual document retrieval benchmark}},
+  url = {https://arxiv.org/abs/2505.11651},
+  year = {2025},
+}
+""",
+        prompt={"query": "Find a screenshot that is relevant to the user's query."},
+    )
+
+    def load_data(self, **kwargs):
+        if self.data_loaded:
+            return
+
+        self.corpus, self.queries, self.relevant_docs = _load_miracl_data(
+            path=self.metadata_dict["dataset"]["path"],
+            splits=self.metadata_dict["eval_splits"],
+            langs=self.hf_subsets,
+            cache_dir=kwargs.get("cache_dir", None),
+            revision=self.metadata_dict["dataset"]["revision"],
+        )
+
+        self.data_loaded = True
diff --git a/mteb/tasks/Image/ImageClassification/eng/Caltech101Classification.py b/mteb/tasks/Image/ImageClassification/eng/Caltech101Classification.py
index 85bfda8a39..580e42dcf4 100644
--- a/mteb/tasks/Image/ImageClassification/eng/Caltech101Classification.py
+++ b/mteb/tasks/Image/ImageClassification/eng/Caltech101Classification.py
@@ -15,7 +15,7 @@ class Caltech101Classification(AbsTaskAnyClassification):
         reference="https://ieeexplore.ieee.org/document/1384978",
         dataset={
             "path": "mteb/Caltech101",
-            "revision": "52439cf6d4f6ebf563d8cdc7f2c5371d9efd2686",
+            "revision": "011e51e5fb01f0c820824734edb7a539ab8e6650",
         },
         type="ImageClassification",
         category="i2c",
diff --git a/mteb/tasks/Image/ZeroShotClassification/eng/Caltech101.py b/mteb/tasks/Image/ZeroShotClassification/eng/Caltech101.py
index 72f35e06d4..7fc16519cd 100644
--- a/mteb/tasks/Image/ZeroShotClassification/eng/Caltech101.py
+++ b/mteb/tasks/Image/ZeroShotClassification/eng/Caltech101.py
@@ -13,7 +13,7 @@ class Caltech101ZeroShotClassification(AbsTaskZeroShotClassification):
         reference="https://ieeexplore.ieee.org/document/1384978",
         dataset={
             "path": "mteb/Caltech101",
-            "revision": "52439cf6d4f6ebf563d8cdc7f2c5371d9efd2686",
+            "revision": "011e51e5fb01f0c820824734edb7a539ab8e6650",
         },
         type="ZeroShotClassification",
         category="i2t",