|
"""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-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', '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-eng', 'fry-eng', 'gla-eng', 'gle-eng', 'glg-eng', 'gsw-eng', 'he', 'heb-eng', 'hi', 'hin-eng', 'hrv-eng', 'hsb-eng', 'hu', 'hun-eng', 'hy', 'hye-eng', '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', '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', 'orv-eng', 'pam-eng', 'pes-eng', 'pl', 'pl-en', 'pms-eng', 'pol-eng', 'por-eng', 'pt', 'ro', 'ron-eng', 'ru', 'rus-eng', 'sl', 'slk-eng', 'slv-eng', 'spa-eng', 'sq', 'sqi-eng', 'srp-eng', 'sv', 'sw', 'swe-eng', 'swg-eng', 'swh-eng', 'ta', 'tam-eng', 'tat-eng', 'te', 'tel-eng', 'tgl-eng', 'th', 'tha-eng', '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-eng', 'yid-eng', 'yue-eng', 'zh', 'zh-CN', 'zh-TW', 'zh-en', 'zsm-eng'] |
|
|
|
SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"] |
|
|
|
|
|
TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"] |
|
|
|
VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "TNews", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli"] |
|
|
|
DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"] |
|
|
|
MODELS = [ |
|
"all-MiniLM-L12-v2", |
|
"all-MiniLM-L6-v2", |
|
"all-mpnet-base-v2", |
|
"allenai-specter", |
|
"bert-base-swedish-cased", |
|
"bert-base-uncased", |
|
"bge-base-zh", |
|
"bge-base-zh-v1.5", |
|
"bge-large-zh", |
|
"bge-large-zh-noinstruct", |
|
"bge-large-zh-v1.5", |
|
"bge-small-zh", |
|
"bge-small-zh-v1.5", |
|
"contriever-base-msmarco", |
|
"cross-en-de-roberta-sentence-transformer", |
|
"dfm-encoder-large-v1", |
|
"dfm-sentence-encoder-large-1", |
|
"distiluse-base-multilingual-cased-v2", |
|
"DanskBERT", |
|
"e5-base", |
|
"e5-large", |
|
"e5-small", |
|
"electra-small-nordic", |
|
"electra-small-swedish-cased-discriminator", |
|
"embedder-100p", |
|
"gbert-base", |
|
"gbert-large", |
|
"gelectra-base", |
|
"gelectra-large", |
|
"gottbert-base", |
|
"glove.6B.300d", |
|
"gtr-t5-base", |
|
"gtr-t5-large", |
|
"gtr-t5-xl", |
|
"gtr-t5-xxl", |
|
"herbert-base-retrieval-v2", |
|
"komninos", |
|
"luotuo-bert-medium", |
|
"LaBSE", |
|
"LASER2", |
|
"msmarco-bert-co-condensor", |
|
"m3e-base", |
|
"m3e-large", |
|
"multilingual-e5-base", |
|
"multilingual-e5-large", |
|
"multilingual-e5-small", |
|
"nb-bert-base", |
|
"nb-bert-large", |
|
"nomic-embed-text-v1.5-64", |
|
"nomic-embed-text-v1.5-128", |
|
"nomic-embed-text-v1.5-256", |
|
"nomic-embed-text-v1.5-512", |
|
"norbert3-base", |
|
"norbert3-large", |
|
"paraphrase-multilingual-MiniLM-L12-v2", |
|
"paraphrase-multilingual-mpnet-base-v2", |
|
"sentence-bert-swedish-cased", |
|
"sentence-t5-base", |
|
"sentence-t5-large", |
|
"sentence-t5-xl", |
|
"sentence-t5-xxl", |
|
"sgpt-bloom-1b7-nli", |
|
"sgpt-bloom-7b1-msmarco", |
|
"sup-simcse-bert-base-uncased", |
|
"st-polish-paraphrase-from-distilroberta", |
|
"st-polish-paraphrase-from-mpnet", |
|
"text2vec-base-chinese", |
|
"text2vec-large-chinese", |
|
"text-embedding-3-large", |
|
"text-embedding-3-large-256", |
|
"text-embedding-3-small", |
|
"text-embedding-ada-002", |
|
"text-similarity-ada-001", |
|
"text-similarity-babbage-001", |
|
"text-similarity-curie-001", |
|
"text-similarity-davinci-001", |
|
"text-search-ada-doc-001", |
|
"text-search-ada-001", |
|
"text-search-babbage-001", |
|
"text-search-curie-001", |
|
"text-search-davinci-001", |
|
"titan-embed-text-v1", |
|
"unsup-simcse-bert-base-uncased", |
|
"use-cmlm-multilingual", |
|
"voyage-lite-01-instruct", |
|
"voyage-lite-02-instruct", |
|
"xlm-roberta-base", |
|
"xlm-roberta-large", |
|
"Baichuan-text-embedding", |
|
"OpenSearch-text-hybrid", |
|
] |
|
|
|
|
|
|
|
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 res_file in os.listdir(results_model_dir): |
|
if res_file.endswith(".json"): |
|
results_model_file = os.path.join(results_model_dir, res_file) |
|
files[model_dir].append(results_model_file) |
|
with open("paths.json", "w") as f: |
|
json.dump(files, f) |
|
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) |
|
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) |
|
ds_name = res_dict["mteb_dataset_name"] |
|
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 "test" not in res_dict: |
|
print(f"Skipping {ds_name} as split {split} not present.") |
|
continue |
|
res_dict = res_dict.get(split) |
|
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 |
|
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 |
|
|
