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"""MTEB Results"""
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """@article{muennighoff2022mteb,
doi = {10.48550/ARXIV.2210.07316},
url = {https://arxiv.org/abs/2210.07316},
author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
title = {MTEB: Massive Text Embedding Benchmark},
publisher = {arXiv},
journal={arXiv preprint arXiv:2210.07316},
year = {2022}
}
"""
_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']
# 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"]
# Use "train" split instead
TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
# Use "validation" split instead
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"]
# Use "test.full" split
TESTFULL_SPLIT = ["OpusparcusPC"]
# Use "standard" split
STANDARD_SPLIT = ["BrightRetrieval"]
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"],
}
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-L12-v2",
"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",
"bm25",
"bm25s",
"camembert-base",
"camembert-large",
"contriever",
"contriever-instruct",
"contriever-base-msmarco",
"cross-en-de-roberta-sentence-transformer",
"dfm-encoder-large-v1",
"dfm-sentence-encoder-large-1",
"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",
"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",
"sgpt-bloom-1b7-nli",
"sgpt-bloom-7b1-msmarco",
"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",
]
# 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)
if not os.path.isdir(results_model_dir):
print(f"Skipping {results_model_dir}")
continue
for revision_folder in os.listdir(results_model_dir):
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"))):
results_model_file = os.path.join(results_model_dir, revision_folder, res_file)
files[model_dir].append(results_model_file)
with open("paths.json", "w") as f:
json.dump(files, f, indent=2)
return files
class MTEBResults(datasets.GeneratorBasedBuilder):
"""MTEBResults"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name=model,
description=f"{model} MTEB results",
version=VERSION,
)
for model in MODELS
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"mteb_dataset_name": datasets.Value("string"),
"eval_language": datasets.Value("string"),
"metric": datasets.Value("string"),
"score": datasets.Value("float"),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path_file = dl_manager.download_and_extract(URL)
# Local debugging:
#with open("/Users/muennighoff/Desktop/results/paths.json") as f:
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}
)
]
def _generate_examples(self, filepath):
"""This function returns the examples in the raw (text) form."""
logger.info(f"Generating examples from {filepath}")
out = []
for path in filepath:
with open(path, encoding="utf-8") as f:
res_dict = json.load(f)
# Naming changed from mteb_dataset_name to task_name
ds_name = res_dict.get("mteb_dataset_name", res_dict.get("task_name"))
# New MTEB format uses scores
res_dict = res_dict.get("scores", res_dict)
split = "test"
if (ds_name in TRAIN_SPLIT) and ("train" in res_dict):
split = "train"
elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict):
split = "validation"
elif (ds_name in DEV_SPLIT) and ("dev" in res_dict):
split = "dev"
elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict):
split = "test.full"
elif (ds_name in STANDARD_SPLIT) and ("standard" in res_dict):
split = "standard"
elif (ds_name in TEST_AVG_SPLIT):
# Average splits
res_dict["test_avg"] = {}
for split in TEST_AVG_SPLIT[ds_name]:
# Old MTEB format
if isinstance(res_dict.get(split), dict):
for k, v in res_dict.get(split, {}).items():
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict["test_avg"]:
res_dict["test_avg"][k] = v
else:
res_dict["test_avg"][k] += v
# New MTEB format
elif isinstance(res_dict.get(split), list):
assert len(res_dict[split]) == 1, "Only single-lists supported for now"
for k, v in res_dict[split][0].items():
if not isinstance(v, float): continue
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict["test_avg"]:
res_dict["test_avg"][k] = v
else:
res_dict["test_avg"][k] += v
split = "test_avg"
elif "test" not in res_dict:
print(f"Skipping {ds_name} as split {split} not present.")
continue
res_dict = res_dict.get(split)
### New MTEB format ###
if isinstance(res_dict, list):
for res in res_dict:
lang = res.pop("languages", [""])
subset = res.pop("hf_subset", "")
if len(lang) == 1:
lang = lang[0].replace("eng-Latn", "")
else:
lang = "_".join(lang)
if not lang:
lang = subset
for metric, score in res.items():
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}')
continue
if metric in SKIP_KEYS: continue
out.append({
"mteb_dataset_name": ds_name,
"eval_language": lang,
"metric": metric + "_" + k,
"score": v * 100,
})
else:
if not isinstance(score, float):
print(f'WARNING: Expected float, got {score} for {ds_name} {lang} {metric} {k}')
continue
out.append({
"mteb_dataset_name": ds_name,
"eval_language": lang,
"metric": metric,
"score": score * 100,
})
### 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
test_result_lang = res_dict.get(lang) if is_multilingual else res_dict
for metric, score in test_result_lang.items():
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,
})
for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])):
yield idx, row
# NOTE: for generating the new paths
if __name__ == "__main__":
get_paths() |