"""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', '.DS_Store', '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