fix: Standartize results folders (#34)

* before add instruct

* udpate paths

* fix test

results.py

@@ -1,4 +1,5 @@
 """MTEB Results"""
+
 from __future__ import annotations
 
 import json
@@ -24,7 +25,205 @@ _DESCRIPTION = """Results on MTEB"""
 
 URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json"
 VERSION = datasets.Version("1.0.1")
-EVAL_LANGS = ['af', 'afr-eng', 'am', "amh", 'amh-eng', 'ang-eng', 'ar', 'ar-ar', 'ara-eng', 'arq-eng', 'arz-eng', 'ast-eng', 'awa-eng', 'az', 'aze-eng', 'bel-eng', 'ben-eng', 'ber-eng', 'bn', 'bos-eng', 'bre-eng', 'bul-eng', 'cat-eng', 'cbk-eng', 'ceb-eng', 'ces-eng', 'cha-eng', 'cmn-eng', 'cor-eng', 'csb-eng', 'cy', 'cym-eng', 'da', 'dan-eng', 'de', 'de-fr', 'de-pl', 'deu-eng', 'dsb-eng', 'dtp-eng', 'el', 'ell-eng', 'en', 'en-ar', 'en-de', 'en-en', 'en-tr', 'eng', 'epo-eng', 'es', 'es-en', 'es-es', 'es-it', 'est-eng', 'eus-eng', 'fa', 'fao-eng', 'fi', 'fin-eng', 'fr', 'fr-en', 'fr-pl', 'fra', 'fra-eng', 'fry-eng', 'gla-eng', 'gle-eng', 'glg-eng', 'gsw-eng', 'hau', 'he', 'heb-eng', 'hi', 'hin-eng', 'hrv-eng', 'hsb-eng', 'hu', 'hun-eng', 'hy', 'hye-eng', 'ibo', 'id', 'ido-eng', 'ile-eng', 'ina-eng', 'ind-eng', 'is', 'isl-eng', 'it', 'it-en', 'ita-eng', 'ja', 'jav-eng', 'jpn-eng', 'jv', 'ka', 'kab-eng', 'kat-eng', 'kaz-eng', 'khm-eng', 'km', 'kn', 'ko', 'ko-ko', 'kor-eng', 'kur-eng', 'kzj-eng', 'lat-eng', 'lfn-eng', 'lit-eng', 'lin', 'lug', 'lv', 'lvs-eng', 'mal-eng', 'mar-eng', 'max-eng', 'mhr-eng', 'mkd-eng', 'ml', 'mn', 'mon-eng', 'ms', 'my', 'nb', 'nds-eng', 'nl', 'nl-ende-en', 'nld-eng', 'nno-eng', 'nob-eng', 'nov-eng', 'oci-eng', 'orm', 'orv-eng', 'pam-eng', 'pcm', 'pes-eng', 'pl', 'pl-en', 'pms-eng', 'pol-eng', 'por-eng', 'pt', 'ro', 'ron-eng', 'ru', 'run', 'rus-eng', 'sl', 'slk-eng', 'slv-eng', 'spa-eng', 'sna', 'som', 'sq', 'sqi-eng', 'srp-eng', 'sv', 'sw', 'swa', 'swe-eng', 'swg-eng', 'swh-eng', 'ta', 'tam-eng', 'tat-eng', 'te', 'tel-eng', 'tgl-eng', 'th', 'tha-eng', 'tir', 'tl', 'tr', 'tuk-eng', 'tur-eng', 'tzl-eng', 'uig-eng', 'ukr-eng', 'ur', 'urd-eng', 'uzb-eng', 'vi', 'vie-eng', 'war-eng', 'wuu-eng', 'xho', 'xho-eng', 'yid-eng', 'yor', 'yue-eng', 'zh', 'zh-CN', 'zh-TW', 'zh-en', 'zsm-eng']
+EVAL_LANGS = [
+    "af",
+    "afr-eng",
+    "am",
+    "amh",
+    "amh-eng",
+    "ang-eng",
+    "ar",
+    "ar-ar",
+    "ara-eng",
+    "arq-eng",
+    "arz-eng",
+    "ast-eng",
+    "awa-eng",
+    "az",
+    "aze-eng",
+    "bel-eng",
+    "ben-eng",
+    "ber-eng",
+    "bn",
+    "bos-eng",
+    "bre-eng",
+    "bul-eng",
+    "cat-eng",
+    "cbk-eng",
+    "ceb-eng",
+    "ces-eng",
+    "cha-eng",
+    "cmn-eng",
+    "cor-eng",
+    "csb-eng",
+    "cy",
+    "cym-eng",
+    "da",
+    "dan-eng",
+    "de",
+    "de-fr",
+    "de-pl",
+    "deu-eng",
+    "dsb-eng",
+    "dtp-eng",
+    "el",
+    "ell-eng",
+    "en",
+    "en-ar",
+    "en-de",
+    "en-en",
+    "en-tr",
+    "eng",
+    "epo-eng",
+    "es",
+    "es-en",
+    "es-es",
+    "es-it",
+    "est-eng",
+    "eus-eng",
+    "fa",
+    "fao-eng",
+    "fi",
+    "fin-eng",
+    "fr",
+    "fr-en",
+    "fr-pl",
+    "fra",
+    "fra-eng",
+    "fry-eng",
+    "gla-eng",
+    "gle-eng",
+    "glg-eng",
+    "gsw-eng",
+    "hau",
+    "he",
+    "heb-eng",
+    "hi",
+    "hin-eng",
+    "hrv-eng",
+    "hsb-eng",
+    "hu",
+    "hun-eng",
+    "hy",
+    "hye-eng",
+    "ibo",
+    "id",
+    "ido-eng",
+    "ile-eng",
+    "ina-eng",
+    "ind-eng",
+    "is",
+    "isl-eng",
+    "it",
+    "it-en",
+    "ita-eng",
+    "ja",
+    "jav-eng",
+    "jpn-eng",
+    "jv",
+    "ka",
+    "kab-eng",
+    "kat-eng",
+    "kaz-eng",
+    "khm-eng",
+    "km",
+    "kn",
+    "ko",
+    "ko-ko",
+    "kor-eng",
+    "kur-eng",
+    "kzj-eng",
+    "lat-eng",
+    "lfn-eng",
+    "lit-eng",
+    "lin",
+    "lug",
+    "lv",
+    "lvs-eng",
+    "mal-eng",
+    "mar-eng",
+    "max-eng",
+    "mhr-eng",
+    "mkd-eng",
+    "ml",
+    "mn",
+    "mon-eng",
+    "ms",
+    "my",
+    "nb",
+    "nds-eng",
+    "nl",
+    "nl-ende-en",
+    "nld-eng",
+    "nno-eng",
+    "nob-eng",
+    "nov-eng",
+    "oci-eng",
+    "orm",
+    "orv-eng",
+    "pam-eng",
+    "pcm",
+    "pes-eng",
+    "pl",
+    "pl-en",
+    "pms-eng",
+    "pol-eng",
+    "por-eng",
+    "pt",
+    "ro",
+    "ron-eng",
+    "ru",
+    "run",
+    "rus-eng",
+    "sl",
+    "slk-eng",
+    "slv-eng",
+    "spa-eng",
+    "sna",
+    "som",
+    "sq",
+    "sqi-eng",
+    "srp-eng",
+    "sv",
+    "sw",
+    "swa",
+    "swe-eng",
+    "swg-eng",
+    "swh-eng",
+    "ta",
+    "tam-eng",
+    "tat-eng",
+    "te",
+    "tel-eng",
+    "tgl-eng",
+    "th",
+    "tha-eng",
+    "tir",
+    "tl",
+    "tr",
+    "tuk-eng",
+    "tur-eng",
+    "tzl-eng",
+    "uig-eng",
+    "ukr-eng",
+    "ur",
+    "urd-eng",
+    "uzb-eng",
+    "vi",
+    "vie-eng",
+    "war-eng",
+    "wuu-eng",
+    "xho",
+    "xho-eng",
+    "yid-eng",
+    "yor",
+    "yue-eng",
+    "zh",
+    "zh-CN",
+    "zh-TW",
+    "zh-en",
+    "zsm-eng",
+]
 
 # v_measures key is somehow present in voyage-2-law results and is a list
 SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"]
@@ -32,9 +231,35 @@ SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures",
 # Use "train" split instead
 TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
 # Use "validation" split instead
-VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "LEMBSummScreenFDRetrieval", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli", "TNews"]
+VALIDATION_SPLIT = [
+    "AFQMC",
+    "Cmnli",
+    "IFlyTek",
+    "LEMBSummScreenFDRetrieval",
+    "MSMARCO",
+    "MSMARCO-PL",
+    "MultilingualSentiment",
+    "Ocnli",
+    "TNews",
+]
 # Use "dev" split instead
-DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval", "TERRa", "MIRACLReranking", "MIRACLRetrieval"]
+DEV_SPLIT = [
+    "CmedqaRetrieval",
+    "CovidRetrieval",
+    "DuRetrieval",
+    "EcomRetrieval",
+    "MedicalRetrieval",
+    "MMarcoReranking",
+    "MMarcoRetrieval",
+    "MSMARCO",
+    "MSMARCO-PL",
+    "T2Reranking",
+    "T2Retrieval",
+    "VideoRetrieval",
+    "TERRa",
+    "MIRACLReranking",
+    "MIRACLRetrieval",
+]
 # Use "test.full" split
 TESTFULL_SPLIT = ["OpusparcusPC"]
 # Use "standard" split
@@ -43,219 +268,237 @@ STANDARD_SPLIT = ["BrightRetrieval"]
 DEVTEST_SPLIT = ["FloresBitextMining"]
 
 TEST_AVG_SPLIT = {
-    "LEMBNeedleRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
-    "LEMBPasskeyRetrieval": ["test_256", "test_512", "test_1024", "test_2048", "test_4096", "test_8192", "test_16384", "test_32768"],
+    "LEMBNeedleRetrieval": [
+        "test_256",
+        "test_512",
+        "test_1024",
+        "test_2048",
+        "test_4096",
+        "test_8192",
+        "test_16384",
+        "test_32768",
+    ],
+    "LEMBPasskeyRetrieval": [
+        "test_256",
+        "test_512",
+        "test_1024",
+        "test_2048",
+        "test_4096",
+        "test_8192",
+        "test_16384",
+        "test_32768",
+    ],
 }
 
 MODELS = [
-    "Baichuan-text-embedding",
-    "Cohere-embed-english-v3.0",
-    "Cohere-embed-english-v3.0-instruct",
-    "Cohere-embed-multilingual-light-v3.0",
-    "Cohere-embed-multilingual-v3.0",
-    "DanskBERT",
-    "FollowIR-7B",
-    "GritLM-7B",
-    "GritLM-7B-noinstruct",
-    "LASER2",
-    "LLM2Vec-Llama-2-supervised",
-    "LLM2Vec-Llama-2-unsupervised",
-    "LLM2Vec-Meta-Llama-3-supervised",
-    "LLM2Vec-Meta-Llama-3-unsupervised",
-    "LLM2Vec-Mistral-supervised",
-    "LLM2Vec-Mistral-unsupervised",
-    "LLM2Vec-Sheared-Llama-supervised",
-    "LLM2Vec-Sheared-Llama-unsupervised",
-    "LaBSE",
-    "OpenSearch-text-hybrid",
-    "SFR-Embedding-Mistral",
-    "all-MiniLM-L6-v2",
-    "all-MiniLM-L6-v2-instruct",
-    "all-mpnet-base-v2",
-    "all-mpnet-base-v2-instruct",
-    "allenai-specter",
-    "bert-base-10lang-cased",
-    "bert-base-15lang-cased",
-    "bert-base-25lang-cased",
-    "bert-base-multilingual-cased",
-    "bert-base-multilingual-uncased",
-    "bert-base-swedish-cased",
-    "bert-base-uncased",
-    "bge-base-en-v1.5",
-    "bge-base-en-v1.5-instruct",
-    "bge-base-en",
-    "bge-base-zh",
-    "bge-base-zh-v1.5",
-    "bge-large-en",
-    "bge-large-en-v1.5",
-    "bge-large-en-v1.5-instruct",
-    "bge-large-zh",
-    "bge-large-zh-noinstruct",
-    "bge-large-zh-v1.5",
-    "bge-m3",
-    "bge-m3-instruct",
-    "bge-small-en-v1.5",
-    "bge-small-en-v1.5-instruct",
-    "bge-small-zh",
-    "bge-small-zh-v1.5",
+    "Alibaba-NLP__gte-Qwen1.5-7B-instruct",
+    "Alibaba-NLP__gte-Qwen2-7B-instruct",
+    "BAAI__bge-base-en",
+    "BAAI__bge-base-en-v1.5",
+    "BAAI__bge-base-en-v1.5-instruct",
+    "BAAI__bge-base-zh",
+    "BAAI__bge-base-zh-v1.5",
+    "BAAI__bge-large-en",
+    "BAAI__bge-large-en-v1.5",
+    "BAAI__bge-large-en-v1.5-instruct",
+    "BAAI__bge-large-zh",
+    "BAAI__bge-large-zh-noinstruct",
+    "BAAI__bge-large-zh-v1.5",
+    "BAAI__bge-m3",
+    "BAAI__bge-m3-instruct",
+    "BAAI__bge-small-en-v1.5",
+    "BAAI__bge-small-en-v1.5-instruct",
+    "BAAI__bge-small-zh",
+    "BAAI__bge-small-zh-v1.5",
+    "Cohere__Cohere-embed-english-v3.0",
+    "Cohere__Cohere-embed-english-v3.0-instruct",
+    "Cohere__Cohere-embed-multilingual-light-v3.0",
+    "Cohere__Cohere-embed-multilingual-v3.0",
+    "DeepPavlov__distilrubert-small-cased-conversational",
+    "DeepPavlov__rubert-base-cased",
+    "DeepPavlov__rubert-base-cased-sentence",
+    "FacebookAI__xlm-roberta-base",
+    "FacebookAI__xlm-roberta-large",
+    "Geotrend__bert-base-10lang-cased",
+    "Geotrend__bert-base-15lang-cased",
+    "Geotrend__bert-base-25lang-cased",
+    "Geotrend__distilbert-base-25lang-cased",
+    "Geotrend__distilbert-base-en-fr-cased",
+    "Geotrend__distilbert-base-en-fr-es-pt-it-cased",
+    "Geotrend__distilbert-base-fr-cased",
+    "GritLM__GritLM-7B",
+    "GritLM__GritLM-7B-noinstruct",
+    "KBLab__electra-small-swedish-cased-discriminator",
+    "KBLab__sentence-bert-swedish-cased",
+    "KB__bert-base-swedish-cased",
+    "McGill-NLP__LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised",
+    "McGill-NLP__LLM2Vec-Llama-2-unsupervised",
+    "McGill-NLP__LLM2Vec-Meta-Llama-3-supervised",
+    "McGill-NLP__LLM2Vec-Meta-Llama-3-unsupervised",
+    "McGill-NLP__LLM2Vec-Mistral-supervised",
+    "McGill-NLP__LLM2Vec-Mistral-unsupervised",
+    "McGill-NLP__LLM2Vec-Sheared-Llama-supervised",
+    "McGill-NLP__LLM2Vec-Sheared-Llama-unsupervised",
+    "Muennighoff__SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
+    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit",
+    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-doc",
+    "Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-que",
+    "Muennighoff__SGPT-125M-weightedmean-nli-bitfit",
+    "Muennighoff__SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
+    "Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
+    "Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit-que",
+    "Muennighoff__SGPT-5.8B-weightedmean-nli-bitfit",
+    "NbAiLab__nb-bert-base",
+    "NbAiLab__nb-bert-large",
+    "Salesforce__SFR-Embedding-Mistral",
+    "T-Systems-onsite__cross-en-de-roberta-sentence-transformer",
+    "Wissam42__sentence-croissant-llm-base",
+    "ai-forever__sbert_large_mt_nlu_ru",
+    "ai-forever__sbert_large_nlu_ru",
+    "aliyun__OpenSearch-text-hybrid",
+    "almanach__camembert-base",
+    "almanach__camembert-large",
+    "amazon__titan-embed-text-v1",
+    "baichuan-ai__text-embedding",
+    "bigscience-data__sgpt-bloom-1b7-nli",
+    "bigscience-data__sgpt-bloom-7b1-msmarco",
     "bm25",
     "bm25s",
-    "camembert-base",
-    "camembert-large",
-    "contriever",
-    "contriever-instruct",
-    "contriever-base-msmarco",
-    "cross-en-de-roberta-sentence-transformer",
-    "dfm-encoder-large-v1",
-    "distilbert-base-25lang-cased",
-    "distilbert-base-en-fr-cased",
-    "distilbert-base-en-fr-es-pt-it-cased",
-    "distilbert-base-fr-cased",
-    "distilbert-base-uncased",
-    "distiluse-base-multilingual-cased-v2",
-    "dragon-plus",
-    "dragon-plus-instruct",
-    "e5-base",
-    "e5-base-4k",
-    "e5-base-v2",
-    "e5-large",
-    "e5-large-v2",
-    "e5-mistral-7b-instruct",
-    "e5-mistral-7b-instruct-noinstruct",
-    "e5-small",
-    "e5-small-v2",
-    "electra-small-nordic",
-    "electra-small-swedish-cased-discriminator",
-    "elser-v2",
-    "embedder-100p",
-    "facebook-dpr-ctx_encoder-multiset-base",
-    "facebookdragon-plus-context-encoder",
-    "flan-t5-base",
-    "flan-t5-large",
-    "flaubert_base_cased",
-    "flaubert_base_uncased",
-    "flaubert_large_cased",
-    "gbert-base",
-    "gbert-large",
-    "gelectra-base",
-    "gelectra-large",
-    "glove.6B.300d",
-    "google-gecko-256.text-embedding-preview-0409",
-    "google-gecko.text-embedding-preview-0409",
-    "gottbert-base",
-    "gte-Qwen1.5-7B-instruct",
-    "gte-Qwen2-7B-instruct",
-    "gtr-t5-base",
-    "gtr-t5-large",
-    "gtr-t5-xl",
-    "gtr-t5-xxl",
-    "herbert-base-retrieval-v2",
-    "instructor-base",
-    "instructor-large",
-    "instructor-xl",
-    "jina-embeddings-v2-base-en",
-    "komninos",
-    "llama-2-7b-chat",
-    "luotuo-bert-medium",
-    "m3e-base",
-    "m3e-large",
-    "mistral-7b-instruct-v0.2",
-    "mistral-embed",
-    "monobert-large-msmarco",
-    "monot5-3b-msmarco-10k",
-    "monot5-base-msmarco-10k",
-    "msmarco-bert-co-condensor",
-    "multi-qa-MiniLM-L6-cos-v1",
-    "multilingual-e5-base",
-    "multilingual-e5-large",
-    "multilingual-e5-large-instruct",
-    "multilingual-e5-small",
-    "mxbai-embed-large-v1",
-    "nb-bert-base",
-    "nb-bert-large",
-    "nomic-embed-text-v1",
-    "nomic-embed-text-v1.5-128",
-    "nomic-embed-text-v1.5-256",
-    "nomic-embed-text-v1.5-512",
-    "nomic-embed-text-v1.5-64",
-    "norbert3-base",
-    "norbert3-large",
-    "paraphrase-multilingual-MiniLM-L12-v2",
-    "paraphrase-multilingual-mpnet-base-v2",
-    "rubert-tiny",
-    "rubert-tiny2",
-    "sbert_large_mt_nlu_ru",
-    "sbert_large_nlu_ru",
-    "sentence-bert-swedish-cased",
-    "sentence-camembert-base",
-    "sentence-camembert-large",
-    "sentence-croissant-llm-base",
-    "sentence-t5-base",
-    "sentence-t5-large",
-    "sentence-t5-xl",
-    "sentence-t5-xxl",
-    "all-MiniLM-L12-v2",
-    "sgpt-bloom-1b7-nli",
-    "sgpt-bloom-7b1-msmarco",
-    "SGPT-125M-weightedmean-nli-bitfit",
-    "SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit-que",
-    "SGPT-5.8B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-5.8B-weightedmean-nli-bitfit",
-    "SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit-que",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit-doc",
-    "SGPT-125M-weightedmean-msmarco-specb-bitfit",
-    "silver-retriever-base-v1",
-    "st-polish-paraphrase-from-distilroberta",
-    "st-polish-paraphrase-from-mpnet",
-    "sup-simcse-bert-base-uncased",
-    "tart-dual-contriever-msmarco",
-    "tart-full-flan-t5-xl",
-    "text-embedding-3-large",
-    "text-embedding-3-large-instruct",
-    "text-embedding-3-large-256",
-    "text-embedding-3-small",
-    "text-embedding-3-small-instruct",
-    "text-embedding-ada-002",
-    "text-embedding-ada-002-instruct",
-    "text-search-ada-001",
-    "text-search-ada-doc-001",
-    "text-search-babbage-001",
-    "text-search-curie-001",
-    "text-search-davinci-001",
-    "text-similarity-ada-001",
-    "text-similarity-babbage-001",
-    "text-similarity-curie-001",
-    "text-similarity-davinci-001",
-    "text2vec-base-chinese",
-    "text2vec-base-multilingual",
-    "text2vec-large-chinese",
-    "titan-embed-text-v1",
-    "udever-bloom-1b1",
-    "udever-bloom-560m",
-    "universal-sentence-encoder-multilingual-3",
-    "universal-sentence-encoder-multilingual-large-3",
-    "unsup-simcse-bert-base-uncased",
-    "use-cmlm-multilingual",
-    "voyage-2",
-    "voyage-code-2",
-    "voyage-large-2-instruct",
-    "voyage-law-2",
-    "voyage-lite-01-instruct",
-    "voyage-lite-02-instruct",
-    "voyage-multilingual-2",
-    "xlm-roberta-base",
-    "xlm-roberta-large",
-    "deberta-v1-base",
-    "USER-bge-m3",
-    "USER-base",
-    "rubert-tiny-turbo",
-    "LaBSE-ru-turbo",
-    "distilrubert-small-cased-conversational",
-    "rubert-base-cased",
-    "rubert-base-cased-sentence",
-    "LaBSE-en-ru",
+    "castorini__monobert-large-msmarco",
+    "castorini__monot5-3b-msmarco-10k",
+    "castorini__monot5-base-msmarco-10k",
+    "chcaa__dfm-encoder-large-v1",
+    "cointegrated__LaBSE-en-ru",
+    "cointegrated__rubert-tiny",
+    "cointegrated__rubert-tiny2",
+    "dangvantuan__sentence-camembert-base",
+    "dangvantuan__sentence-camembert-large",
+    "deepfile__embedder-100p",
+    "deepset__gbert-base",
+    "deepset__gbert-large",
+    "deepset__gelectra-base",
+    "deepset__gelectra-large",
+    "deepvk__USER-base",
+    "deepvk__USER-bge-m3",
+    "deepvk__deberta-v1-base",
+    "distilbert__distilbert-base-uncased",
+    "dwzhu__e5-base-4k",
+    "elastic__elser-v2",
+    "facebook__contriever",
+    "facebook__contriever-instruct",
+    "facebook__dpr-ctx_encoder-multiset-base",
+    "facebook__dragon-plus-context-encoder",
+    "facebook__tart-full-flan-t5-xl",
+    "facebookresearch__LASER2",
+    "facebookresearch__dragon-plus",
+    "facebookresearch__dragon-plus-instruct",
+    "flaubert__flaubert_base_cased",
+    "flaubert__flaubert_base_uncased",
+    "flaubert__flaubert_large_cased",
+    "google-bert__bert-base-multilingual-cased",
+    "google-bert__bert-base-multilingual-uncased",
+    "google-bert__bert-base-uncased",
+    "google-gecko__text-embedding-preview-0409",
+    "google-gecko__text-embedding-preview-0409-256",
+    "google__flan-t5-base",
+    "google__flan-t5-large",
+    "hkunlp__instructor-base",
+    "hkunlp__instructor-large",
+    "hkunlp__instructor-xl",
+    "intfloat__e5-base",
+    "intfloat__e5-base-v2",
+    "intfloat__e5-large",
+    "intfloat__e5-large-v2",
+    "intfloat__e5-mistral-7b-instruct",
+    "intfloat__e5-mistral-7b-instruct-noinstruct",
+    "intfloat__e5-small",
+    "intfloat__e5-small-v2",
+    "intfloat__multilingual-e5-base",
+    "intfloat__multilingual-e5-large",
+    "intfloat__multilingual-e5-large-instruct",
+    "intfloat__multilingual-e5-small",
+    "ipipan__herbert-base-retrieval-v2",
+    "ipipan__silver-retriever-base-v1",
+    "izhx__udever-bloom-1b1",
+    "izhx__udever-bloom-560m",
+    "jhu-clsp__FollowIR-7B",
+    "jinaai__jina-embeddings-v2-base-en",
+    "jonfd__electra-small-nordic",
+    "ltg__norbert3-base",
+    "ltg__norbert3-large",
+    "meta-llama__llama-2-7b-chat",
+    "mistral__mistral-embed",
+    "mistralai__mistral-7b-instruct-v0.2",
+    "mixedbread-ai__mxbai-embed-large-v1",
+    "moka-ai__m3e-base",
+    "moka-ai__m3e-large",
+    "nomic-ai__nomic-embed-text-v1",
+    "nomic-ai__nomic-embed-text-v1.5-128",
+    "nomic-ai__nomic-embed-text-v1.5-256",
+    "nomic-ai__nomic-embed-text-v1.5-512",
+    "nomic-ai__nomic-embed-text-v1.5-64",
+    "nthakur__contriever-base-msmarco",
+    "openai__text-embedding-3-large",
+    "openai__text-embedding-3-large-256",
+    "openai__text-embedding-3-large-instruct",
+    "openai__text-embedding-3-small-instruct",
+    "openai__text-embedding-ada-002",
+    "openai__text-embedding-ada-002-instruct",
+    "openai__text-search-ada-001",
+    "openai__text-search-ada-doc-001",
+    "openai__text-search-babbage-001",
+    "openai__text-search-curie-001",
+    "openai__text-search-davinci-001",
+    "openai__text-similarity-ada-001",
+    "openai__text-similarity-babbage-001",
+    "openai__text-similarity-curie-001",
+    "openai__text-similarity-davinci-001",
+    "openai__text-embedding-3-small",
+    "orionweller__tart-dual-contriever-msmarco",
+    "princeton-nlp__sup-simcse-bert-base-uncased",
+    "princeton-nlp__unsup-simcse-bert-base-uncased",
+    "sdadas__st-polish-paraphrase-from-distilroberta",
+    "sdadas__st-polish-paraphrase-from-mpnet",
+    "sentence-transformers__LaBSE",
+    "sentence-transformers__all-MiniLM-L12-v2",
+    "sentence-transformers__all-MiniLM-L6-v2",
+    "sentence-transformers__all-MiniLM-L6-v2-instruct",
+    "sentence-transformers__all-mpnet-base-v2",
+    "sentence-transformers__all-mpnet-base-v2-instruct",
+    "sentence-transformers__allenai-specter",
+    "sentence-transformers__average_word_embeddings_glove.6B.300d",
+    "sentence-transformers__average_word_embeddings_komninos",
+    "sentence-transformers__distiluse-base-multilingual-cased-v2",
+    "sentence-transformers__gtr-t5-base",
+    "sentence-transformers__gtr-t5-large",
+    "sentence-transformers__gtr-t5-xl",
+    "sentence-transformers__gtr-t5-xxl",
+    "sentence-transformers__msmarco-bert-co-condensor",
+    "sentence-transformers__multi-qa-MiniLM-L6-cos-v1",
+    "sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2",
+    "sentence-transformers__paraphrase-multilingual-mpnet-base-v2",
+    "sentence-transformers__sentence-t5-base",
+    "sentence-transformers__sentence-t5-large",
+    "sentence-transformers__sentence-t5-xl",
+    "sentence-transformers__sentence-t5-xxl",
+    "sentence-transformers__use-cmlm-multilingual",
+    "sergeyzh__LaBSE-ru-turbo",
+    "sergeyzh__rubert-tiny-turbo",
+    "shibing624__text2vec-base-chinese",
+    "shibing624__text2vec-base-multilingual",
+    "shibing624__text2vec-large-chinese",
+    "silk-road__luotuo-bert-medium",
+    "uklfr__gottbert-base",
+    "vesteinn__DanskBERT",
+    "voyageai__voyage-2",
+    "voyageai__voyage-code-2",
+    "voyageai__voyage-large-2-instruct",
+    "voyageai__voyage-law-2",
+    "voyageai__voyage-lite-01-instruct",
+    "voyageai__voyage-lite-02-instruct",
+    "voyageai__voyage-multilingual-2",
+    "vprelovac__universal-sentence-encoder-multilingual-3",
+    "vprelovac__universal-sentence-encoder-multilingual-large-3",
 ]
 
 
@@ -269,6 +512,7 @@ def get_model_for_current_dir(dir_name: str) -> str | None:
 # Needs to be run whenever new files are added
 def get_paths():
     import collections, json, os
+
     files = collections.defaultdict(list)
     for model_dir in os.listdir("results"):
         results_model_dir = os.path.join("results", model_dir)
@@ -283,7 +527,9 @@ def get_paths():
             if not os.path.isdir(os.path.join(results_model_dir, revision_folder)):
                 continue
             for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)):
-                if (res_file.endswith(".json")) and not(res_file.endswith(("overall_results.json", "model_meta.json"))):
+                if (res_file.endswith(".json")) and not (
+                    res_file.endswith(("overall_results.json", "model_meta.json"))
+                ):
                     results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
                     files[model_name].append(results_model_file)
     with open("paths.json", "w") as f:
@@ -327,12 +573,7 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
         with open(path_file) as f:
             files = json.load(f)
         downloaded_files = dl_manager.download_and_extract(files[self.config.name])
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={'filepath': downloaded_files}
-            )
-        ]
+        return [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files})]
 
     def _generate_examples(self, filepath):
         """This function returns the examples in the raw (text) form."""
@@ -356,7 +597,7 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                     split = "dev"
                 elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
                     split = "test.full"
-                elif (ds_name in STANDARD_SPLIT):
+                elif ds_name in STANDARD_SPLIT:
                     split = []
                     if "standard" in res_dict:
                         split += ["standard"]
@@ -364,7 +605,7 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                         split += ["long"]
                 elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict):
                     split = "devtest"
-                elif (ds_name in TEST_AVG_SPLIT):
+                elif ds_name in TEST_AVG_SPLIT:
                     # Average splits
                     res_dict = {}
                     for split in TEST_AVG_SPLIT[ds_name]:
@@ -385,7 +626,8 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                             for k, v in res_dict[split][0].items():
                                 if k in ["hf_subset", "languages"]:
                                     res_dict[k] = v
-                                if not isinstance(v, float): continue
+                                if not isinstance(v, float):
+                                    continue
                                 v /= len(TEST_AVG_SPLIT[ds_name])
                                 if k not in res_dict:
                                     res_dict[k] = v
@@ -414,41 +656,48 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                             if not lang:
                                 lang = subset
                             for metric, score in res.items():
-                                if metric in SKIP_KEYS: continue
+                                if metric in SKIP_KEYS:
+                                    continue
                                 if isinstance(score, dict):
                                     # Legacy format with e.g. {cosine: {spearman: ...}}
                                     # Now it is {cosine_spearman: ...}
                                     for k, v in score.items():
                                         if not isinstance(v, float):
-                                            print(f'WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}')
+                                            print(f"WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}")
                                             continue
-                                        if metric in SKIP_KEYS: continue
-                                        out.append({
-                                            "mteb_dataset_name": ds_name,
-                                            "eval_language": lang,
-                                            "metric": metric + "_" + k,
-                                            "score": v * 100,
-                                            "hf_subset": subset,
-                                        })
+                                        if metric in SKIP_KEYS:
+                                            continue
+                                        out.append(
+                                            {
+                                                "mteb_dataset_name": ds_name,
+                                                "eval_language": lang,
+                                                "metric": metric + "_" + k,
+                                                "score": v * 100,
+                                                "hf_subset": subset,
+                                            }
+                                        )
                                 else:
                                     if not isinstance(score, float):
-                                        print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric}')
+                                        print(f"WARNING: Expected float, got {score} for {ds_name} {lang} {metric}")
                                         continue
-                                    out.append({
-                                        "mteb_dataset_name": ds_name,
-                                        "eval_language": lang,
-                                        "metric": metric,
-                                        "score": score * 100,
-                                        "split": split,
-                                        "hf_subset": subset,
-                                    })
+                                    out.append(
+                                        {
+                                            "mteb_dataset_name": ds_name,
+                                            "eval_language": lang,
+                                            "metric": metric,
+                                            "score": score * 100,
+                                            "split": split,
+                                            "hf_subset": subset,
+                                        }
+                                    )
 
                     ### Old MTEB format ###
                     else:
                         is_multilingual = any(x in res_dict for x in EVAL_LANGS)
                         langs = res_dict.keys() if is_multilingual else ["en"]
                         for lang in langs:
-                            if lang in SKIP_KEYS: continue
+                            if lang in SKIP_KEYS:
+                                continue
                             test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
                             subset = test_result_lang.pop("hf_subset", "")
                             if subset == "" and is_multilingual:
@@ -457,16 +706,20 @@ class MTEBResults(datasets.GeneratorBasedBuilder):
                                 if not isinstance(score, dict):
                                     score = {metric: score}
                                 for sub_metric, sub_score in score.items():
-                                    if any(x in sub_metric for x in SKIP_KEYS): continue
-                                    if isinstance(sub_score, dict): continue
-                                    out.append({
-                                        "mteb_dataset_name": ds_name,
-                                        "eval_language": lang if is_multilingual else "",
-                                        "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
-                                        "score": sub_score * 100,
-                                        "split": split,
-                                        "hf_subset": subset,
-                                    })
+                                    if any(x in sub_metric for x in SKIP_KEYS):
+                                        continue
+                                    if isinstance(sub_score, dict):
+                                        continue
+                                    out.append(
+                                        {
+                                            "mteb_dataset_name": ds_name,
+                                            "eval_language": lang if is_multilingual else "",
+                                            "metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric,
+                                            "score": sub_score * 100,
+                                            "split": split,
+                                            "hf_subset": subset,
+                                        }
+                                    )
         for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
             yield idx, row