"""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