File size: 15,243 Bytes
46c0fca |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 |
"""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"]
# 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"]
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-multilingual-light-v3.0",
"Cohere-embed-multilingual-v3.0",
"DanskBERT",
"FollowIR-7B",
"GritLM-7B",
"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",
"all-MiniLM-L12-v2",
"all-MiniLM-L6-v2",
"all-mpnet-base-v2",
"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",
"bge-base-zh",
"bge-base-zh-v1.5",
"bge-large-en",
"bge-large-en-v1.5",
"bge-large-zh",
"bge-large-zh-noinstruct",
"bge-large-zh-v1.5",
"bge-m3",
"bge-small-zh",
"bge-small-zh-v1.5",
"bm25",
"camembert-base",
"camembert-large",
"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",
"e5-base",
"e5-base-4k",
"e5-base-v2",
"e5-large",
"e5-large-v2",
"e5-mistral-7b-instruct",
"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",
"gtr-t5-base",
"gtr-t5-large",
"gtr-t5-xl",
"gtr-t5-xxl",
"herbert-base-retrieval-v2",
"instructor-base",
"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-256",
"text-embedding-3-small",
"text-embedding-ada-002",
"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")):
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, 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)
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 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.get("languages", [""])
assert len(lang) == 1, "Only single-languages supported for now"
lang = lang[0].replace("eng-Latn", "")
for metric, score in res.items():
if metric in SKIP_KEYS: 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() |