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"""MTEB Results""" |
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import io |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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TODO |
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""" |
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_DESCRIPTION = """\ |
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Results on MTEB |
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""" |
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URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json" |
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VERSION = datasets.Version("1.0.0") |
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EVAL_LANGS = [ |
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"en", |
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"de", |
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"es", |
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"fr", |
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"hi", |
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"th", |
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"af", |
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"am", |
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"ar", |
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"az", |
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"bn", |
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"cy", |
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"da", |
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"de", |
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"el", |
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"en", |
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"es", |
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"fa", |
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"fi", |
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"fr", |
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"he", |
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"hi", |
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"hu", |
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"hy", |
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"id", |
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"is", |
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"it", |
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"ja", |
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"jv", |
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"ka", |
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"km", |
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"kn", |
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"ko", |
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"lv", |
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"ml", |
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"mn", |
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"ms", |
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"my", |
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"nb", |
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"nl", |
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"pl", |
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"pt", |
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"ro", |
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"ru", |
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"sl", |
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"sq", |
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"sv", |
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"sw", |
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"ta", |
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"te", |
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"th", |
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"tl", |
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"tr", |
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"ur", |
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"vi", |
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"zh-CN", |
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"zh-TW", |
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"ko-ko", "ar-ar", "en-ar", "en-de", "en-en", "en-tr", "es-en", "es-es", "fr-en", "it-en", "nl-en" |
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"de-en", |
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"es-en", |
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"it", |
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"pl-en", |
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"zh-en", |
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"es-it", |
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"de-fr", |
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"de-pl", |
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"fr-pl", |
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] |
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SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"] |
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MODELS = [ |
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"LASER2", |
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"LaBSE", |
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] |
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""" |
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README.md |
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SGPT-1.3B-weightedmean-msmarco-specb-bitfit |
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SGPT-125M-weightedmean-msmarco-specb-bitfit |
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SGPT-125M-weightedmean-msmarco-specb-bitfit-doc |
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SGPT-125M-weightedmean-msmarco-specb-bitfit-que |
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SGPT-125M-weightedmean-nli-bitfit |
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SGPT-2.7B-weightedmean-msmarco-specb-bitfit |
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SGPT-5.8B-weightedmean-msmarco-specb-bitfit |
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SGPT-5.8B-weightedmean-msmarco-specb-bitfit-que |
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SGPT-5.8B-weightedmean-nli-bitfit |
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all-MiniLM-L12-v2 |
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all-MiniLM-L6-v2 |
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all-mpnet-base-v2 |
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allenai-specter |
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bert-base-uncased |
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contriever-base-msmarco |
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glove.6B.300d |
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gtr-t5-base |
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gtr-t5-large |
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gtr-t5-xl |
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gtr-t5-xxl |
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komninos |
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msmarco-bert-co-condensor |
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paraphrase-multilingual-MiniLM-L12-v2 |
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paraphrase-multilingual-mpnet-base-v2 |
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results.py |
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sentence-t5-base |
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sentence-t5-large |
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sentence-t5-xl |
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sentence-t5-xxl |
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sgpt-bloom-1b3-nli |
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sgpt-bloom-7b1-msmarco |
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sgpt-nli-bloom-1b3 |
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sup-simcse-bert-base-uncased |
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text-similarity-ada-001 |
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unsup-simcse-bert-base-uncased |
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""" |
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def get_paths(): |
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import json, os |
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files = {} |
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for model_dir in os.listdir("results"): |
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results_model_dir = os.path.join("results", model_dir) |
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for res_file in os.listdir(results_model_dir): |
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if res_file.endswith(".json"): |
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results_model_file = os.path.join(results_model_dir, res_file) |
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files.setdefault(model_dir, []) |
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files[model_dir].append(results_model_file) |
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with open(f"paths.json", "w") as f: |
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json.dump(files, f) |
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return files |
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class MTEBResults(datasets.GeneratorBasedBuilder): |
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"""MTEBResults""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=model, |
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description=f"{model} MTEB results", |
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version=VERSION, |
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) |
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for model in MODELS |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"dataset": datasets.Value("string"), |
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"metric": datasets.Value("string"), |
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"score": datasets.Value("float"), |
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} |
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), |
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supervised_keys=None, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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path_file = dl_manager.download_and_extract(URL) |
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with open(path_file, "r") as f: |
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files = json.load(f) |
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downloaded_files = dl_manager.download_and_extract(files[self.config.name]) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={'filepath': downloaded_files} |
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) |
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] |
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def _generate_examples(self, filepath): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("Generating examples from {}".format(filepath)) |
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out = [] |
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for path in filepath: |
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with io.open(path, "r", encoding="utf-8") as f: |
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res_dict = json.load(f) |
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ds_name = res_dict["mteb_dataset_name"] |
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split = "test" |
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if ds_name == "MSMARCO": |
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split = "dev" if "dev" in res_dict else "validation" |
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if split not in res_dict: |
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print(f"Skipping {ds_name} as split {split} not present.") |
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continue |
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res_dict = res_dict.get(split) |
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is_multilingual = True if any([x in res_dict for x in EVAL_LANGS]) else False |
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langs = res_dict.keys() if is_multilingual else ["en"] |
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for lang in langs: |
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if lang in SKIP_KEYS: continue |
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ds_name += f" ({lang})" if is_multilingual else "" |
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test_result_lang = res_dict.get(lang) if is_multilingual else res_dict |
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for (metric, score) in test_result_lang.items(): |
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if not isinstance(score, dict): |
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score = {metric: score} |
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for sub_metric, sub_score in score.items(): |
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if any([x in sub_metric for x in SKIP_KEYS]): continue |
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out.append({ |
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"mteb_dataset_name": ds_name, |
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"mteb_dataset_name": ds_name, |
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"metric": f"{metric}_{sub_metric}" if metric != sub_metric else metric, |
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"score": sub_score * 100, |
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}) |
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for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])): |
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yield idx, row |
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