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results.py
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results.py
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"""MTEB Results"""
from __future__ import annotations
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",
"TERRa",
"MIRACLReranking",
"MIRACLRetrieval",
]
# Use "test.full" split
TESTFULL_SPLIT = ["OpusparcusPC"]
# Use "standard" split
STANDARD_SPLIT = ["BrightRetrieval"]
# Use "devtest" split
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",
],
}
MODELS = [
"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",
"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-004",
"google-gecko__text-embedding-004-256",
"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",
"voyageai__voyage-3",
"voyageai__voyage-3-lite",
"vprelovac__universal-sentence-encoder-multilingual-3",
"vprelovac__universal-sentence-encoder-multilingual-large-3",
]
def get_model_for_current_dir(dir_name: str) -> str | None:
for model in MODELS:
if model == dir_name or ("__" in dir_name and dir_name.split("__")[1] == model):
return model
return 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)
if not os.path.isdir(results_model_dir):
print(f"Skipping {results_model_dir}")
continue
model_name = get_model_for_current_dir(model_dir)
if model_name is None:
print(f"Skipping {model_dir} 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_name].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"),
"split": datasets.Value("string"),
"hf_subset": datasets.Value("string"),
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
path_file = dl_manager.download_and_extract(URL)
# Local debugging help
# with open("/path/to/local/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:
split = []
if "standard" in res_dict:
split += ["standard"]
if "long" in res_dict:
split += ["long"]
elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict):
split = "devtest"
elif ds_name in TEST_AVG_SPLIT:
# Average splits
res_dict = {}
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():
if k in ["hf_subset", "languages"]:
res_dict[k] = v
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict:
res_dict[k] = v
else:
res_dict[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 k in ["hf_subset", "languages"]:
res_dict[k] = v
if not isinstance(v, float):
continue
v /= len(TEST_AVG_SPLIT[ds_name])
if k not in res_dict:
res_dict[k] = v
else:
res_dict[k] += v
split = "test_avg"
res_dict = {split: [res_dict]}
elif "test" not in res_dict:
print(f"Skipping {ds_name} as split {split} not present.")
continue
splits = [split] if not isinstance(split, list) else split
full_res_dict = res_dict
for split in splits:
res_dict = full_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,
"hf_subset": subset,
}
)
else:
if not isinstance(score, float):
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,
}
)
### 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
subset = test_result_lang.pop("hf_subset", "")
if subset == "" and is_multilingual:
subset = lang
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,
"split": split,
"hf_subset": subset,
}
)
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()