"""MTEB Results""" | |
import io | |
import json | |
import datasets | |
logger = datasets.logging.get_logger(__name__) | |
_CITATION = """ | |
TODO | |
""" | |
_DESCRIPTION = """\ | |
Results on MTEB | |
""" | |
URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json" | |
VERSION = datasets.Version("1.0.0") | |
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-CN', 'zh-TW', 'zh-en', 'zsm-eng'] | |
SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"] | |
MODELS = [ | |
"LASER2", | |
"LaBSE", | |
"all-MiniLM-L12-v2", | |
"all-MiniLM-L6-v2", | |
"all-mpnet-base-v2", | |
"allenai-specter", | |
"bert-base-uncased", | |
"contriever-base-msmarco", | |
"glove.6B.300d", | |
"gtr-t5-base", | |
"gtr-t5-large", | |
"gtr-t5-xl", | |
"gtr-t5-xxl", | |
"komninos", | |
"msmarco-bert-co-condensor", | |
"paraphrase-multilingual-MiniLM-L12-v2", | |
"paraphrase-multilingual-mpnet-base-v2", | |
"sentence-t5-base", | |
"sentence-t5-large", | |
"sentence-t5-xl", | |
"sentence-t5-xxl", | |
"sgpt-bloom-1b3-nli", | |
"sgpt-bloom-7b1-msmarco", | |
"sgpt-nli-bloom-1b3", | |
"sup-simcse-bert-base-uncased", | |
"text-similarity-ada-001", | |
"unsup-simcse-bert-base-uncased", | |
] | |
# Needs to be run whenever new files are added | |
def get_paths(): | |
import json, os | |
files = {} | |
for model_dir in os.listdir("results"): | |
results_model_dir = os.path.join("results", model_dir) | |
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.setdefault(model_dir, []) | |
files[model_dir].append(results_model_file) | |
with open(f"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, "r") 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("Generating examples from {}".format(filepath)) | |
out = [] | |
for path in filepath: | |
with io.open(path, "r", encoding="utf-8") as f: | |
res_dict = json.load(f) | |
ds_name = res_dict["mteb_dataset_name"] | |
split = "test" | |
if ds_name == "MSMARCO": | |
split = "dev" if "dev" in res_dict else "validation" | |
if split not in res_dict: | |
print(f"Skipping {ds_name} as split {split} not present.") | |
continue | |
res_dict = res_dict.get(split) | |
is_multilingual = True if any([x in res_dict for x in EVAL_LANGS]) else False | |
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 | |