| """MTEB Results""" |
|
|
| import json |
|
|
| import datasets |
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| logger = datasets.logging.get_logger(__name__) |
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| _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"] |
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| |
| TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"] |
| |
| VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "TNews", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli"] |
| |
| DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"] |
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| 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 |
|
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| |
| 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 |
|
|
