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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 Portuguese"""
URL = "https://huggingface.co/datasets/projetomemoreba/results/resolve/main/paths.json"
VERSION = datasets.Version("1.0.1")
EVAL_LANGS = ['pt']
SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"]
# Use "train" split instead
TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"]
# Use "validation" split instead
VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "TNews", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli"]
# Use "dev" split instead
DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"]
MODELS = [
"instructor-base",
"xlm-roberta-large",
"gtr-t5-large",
"sentence-t5-xxl",
"GIST-Embedding-v0",
"e5-base",
"mxbai-embed-2d-large-v1",
"SGPT-5.8B-weightedmean-nli-bitfit",
"jina-embeddings-v2-base-de",
"gte-base",
"jina-embedding-b-en-v1",
"LaBSE",
"sgpt-bloom-7b1-msmarco",
"bi-cse",
"distilbert-base-uncased",
"bert-base-10lang-cased",
"sentence-t5-large",
"jina-embeddings-v2-small-en",
"e5-mistral-7b-instruct",
"bge-base-en-v1.5",
"ember-v1",
"e5-large-v2",
"lodestone-base-4096-v1",
"all-mpnet-base-v2",
"sentence-t5-xl",
"distilbert-base-en-fr-cased",
"gte-tiny",
"text2vec-base-multilingual",
"GIST-all-MiniLM-L6-v2",
"jina-embeddings-v2-base-es",
"bert-base-multilingual-uncased",
"distiluse-base-multilingual-cased-v2",
"sup-simcse-bert-base-uncased",
"e5-small-v2",
"GritLM-7B",
"sentence-t5-base",
"SFR-Embedding-Mistral",
"mxbai-embed-large-v1",
"stella-base-en-v2",
"udever-bloom-3b",
"bert-base-multilingual-cased",
"all-MiniLM-L12-v2",
"sf_model_e5",
"bert-base-portuguese-cased",
"bge-small-en-v1.5",
"SGPT-125M-weightedmean-msmarco-specb-bitfit",
"udever-bloom-560m",
"gtr-t5-base",
"fin-mpnet-base",
"SGPT-2.7B-weightedmean-msmarco-specb-bitfit",
"xlm-roberta-base",
"GIST-small-Embedding-v0",
"gte-large",
"ALL_862873",
"e5-large",
"distilbert-base-en-fr-es-pt-it-cased",
"dfm-sentence-encoder-large-v1",
"bge-micro",
"instructor-large",
"average_word_embeddings_glove.6B.300d",
"multilingual-e5-large-instruct",
"msmarco-bert-co-condensor",
"multilingual-e5-small",
"UAE-Large-V1",
"udever-bloom-1b1",
"distilbert-base-fr-cased",
"instructor-xl",
"bert-base-uncased",
"all-MiniLM-L6-v2",
"e5-base-v2",
"jina-embedding-l-en-v1",
"gtr-t5-xl",
"gte-small",
"bge-small-4096",
"average_word_embeddings_komninos",
"unsup-simcse-bert-base-uncased",
"bert-base-15lang-cased",
"paraphrase-multilingual-MiniLM-L12-v2",
"distilbert-base-25lang-cased",
"contriever-base-msmarco",
"multilingual-e5-large",
"luotuo-bert-medium",
"GIST-large-Embedding-v0",
"bge-large-en-v1.5",
"cai-lunaris-text-embeddings",
"gtr-t5-xxl",
"multilingual-e5-base",
"paraphrase-multilingual-mpnet-base-v2",
"SGPT-1.3B-weightedmean-msmarco-specb-bitfit",
"e5-dansk-test-0.1",
"allenai-specter"
]
from pathlib import Path
# 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 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
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