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"""MTEB Results""" |
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from __future__ import annotations |
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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 = """@article{muennighoff2022mteb, |
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doi = {10.48550/ARXIV.2210.07316}, |
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url = {https://arxiv.org/abs/2210.07316}, |
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author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils}, |
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title = {MTEB: Massive Text Embedding Benchmark}, |
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publisher = {arXiv}, |
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journal={arXiv preprint arXiv:2210.07316}, |
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year = {2022} |
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} |
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""" |
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_DESCRIPTION = """Results on MTEB""" |
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URL = "https://huggingface.co/datasets/mteb/results/resolve/main/paths.json" |
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VERSION = datasets.Version("1.0.1") |
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EVAL_LANGS = [ |
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"af", |
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"afr-eng", |
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"am", |
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"amh", |
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"amh-eng", |
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"ang-eng", |
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"ar", |
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"ar-ar", |
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"ara-eng", |
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"arq-eng", |
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"arz-eng", |
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"ast-eng", |
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"awa-eng", |
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"az", |
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"aze-eng", |
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"bel-eng", |
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"ben-eng", |
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"ber-eng", |
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"bn", |
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"bos-eng", |
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"bre-eng", |
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"bul-eng", |
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"cat-eng", |
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"cbk-eng", |
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"ceb-eng", |
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"ces-eng", |
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"cha-eng", |
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"cmn-eng", |
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"cor-eng", |
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"csb-eng", |
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"cy", |
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"cym-eng", |
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"da", |
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"dan-eng", |
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"de", |
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"de-fr", |
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"de-pl", |
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"deu-eng", |
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"dsb-eng", |
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"dtp-eng", |
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"el", |
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"ell-eng", |
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"en", |
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"en-ar", |
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"en-de", |
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"en-en", |
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"en-tr", |
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"eng", |
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"epo-eng", |
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"es", |
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"es-en", |
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"es-es", |
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"es-it", |
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"est-eng", |
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"eus-eng", |
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"fa", |
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"fao-eng", |
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"fi", |
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"fin-eng", |
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"fr", |
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"fr-en", |
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"fr-pl", |
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"fra", |
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"fra-eng", |
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"fry-eng", |
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"gla-eng", |
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"gle-eng", |
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"glg-eng", |
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"gsw-eng", |
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"hau", |
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"he", |
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"heb-eng", |
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"hi", |
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"hin-eng", |
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"hrv-eng", |
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"hsb-eng", |
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"hu", |
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"hun-eng", |
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"hy", |
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"hye-eng", |
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"ibo", |
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"id", |
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"ido-eng", |
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"ile-eng", |
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"ina-eng", |
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"ind-eng", |
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"is", |
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"isl-eng", |
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"it", |
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"it-en", |
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"ita-eng", |
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"ja", |
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"jav-eng", |
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"jpn-eng", |
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"jv", |
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"ka", |
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"kab-eng", |
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"kat-eng", |
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"kaz-eng", |
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"khm-eng", |
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"km", |
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"kn", |
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"ko", |
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"ko-ko", |
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"kor-eng", |
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"kur-eng", |
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"kzj-eng", |
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"lat-eng", |
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"lfn-eng", |
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"lit-eng", |
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"lin", |
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"lug", |
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"lv", |
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"lvs-eng", |
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"mal-eng", |
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"mar-eng", |
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"max-eng", |
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"mhr-eng", |
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"mkd-eng", |
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"ml", |
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"mn", |
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"mon-eng", |
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"ms", |
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"my", |
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"nb", |
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"nds-eng", |
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"nl", |
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"nl-ende-en", |
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"nld-eng", |
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"nno-eng", |
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"nob-eng", |
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"nov-eng", |
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"oci-eng", |
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"orm", |
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"orv-eng", |
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"pam-eng", |
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"pcm", |
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"pes-eng", |
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"pl", |
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"pl-en", |
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"pms-eng", |
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"pol-eng", |
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"por-eng", |
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"pt", |
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"ro", |
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"ron-eng", |
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"ru", |
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"run", |
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"rus-eng", |
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"sl", |
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"slk-eng", |
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"slv-eng", |
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"spa-eng", |
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"sna", |
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"som", |
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"sq", |
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"sqi-eng", |
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"srp-eng", |
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"sv", |
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"sw", |
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"swa", |
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"swe-eng", |
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"swg-eng", |
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"swh-eng", |
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"ta", |
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"tam-eng", |
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"tat-eng", |
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"te", |
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"tel-eng", |
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"tgl-eng", |
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"th", |
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"tha-eng", |
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"tir", |
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"tl", |
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"tr", |
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"tuk-eng", |
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"tur-eng", |
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"tzl-eng", |
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"uig-eng", |
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"ukr-eng", |
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"ur", |
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"urd-eng", |
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"uzb-eng", |
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"vi", |
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"vie-eng", |
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"war-eng", |
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"wuu-eng", |
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"xho", |
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"xho-eng", |
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"yid-eng", |
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"yor", |
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"yue-eng", |
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"zh", |
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"zh-CN", |
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"zh-TW", |
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"zh-en", |
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"zsm-eng", |
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] |
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SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold", "v_measures", "scores_per_experiment"] |
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TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"] |
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VALIDATION_SPLIT = [ |
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"AFQMC", |
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"Cmnli", |
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"IFlyTek", |
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"LEMBSummScreenFDRetrieval", |
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"MSMARCO", |
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"MSMARCO-PL", |
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"MultilingualSentiment", |
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"Ocnli", |
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"TNews", |
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] |
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DEV_SPLIT = [ |
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"CmedqaRetrieval", |
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"CovidRetrieval", |
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"DuRetrieval", |
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"EcomRetrieval", |
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"MedicalRetrieval", |
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"MMarcoReranking", |
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"MMarcoRetrieval", |
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"MSMARCO", |
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"MSMARCO-PL", |
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"T2Reranking", |
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"T2Retrieval", |
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"VideoRetrieval", |
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"TERRa", |
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"MIRACLReranking", |
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"MIRACLRetrieval", |
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] |
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TESTFULL_SPLIT = ["OpusparcusPC"] |
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STANDARD_SPLIT = ["BrightRetrieval"] |
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DEVTEST_SPLIT = ["FloresBitextMining"] |
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TEST_AVG_SPLIT = { |
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"LEMBNeedleRetrieval": [ |
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"test_256", |
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"test_512", |
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"test_1024", |
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"test_2048", |
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"test_4096", |
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"test_8192", |
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"test_16384", |
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"test_32768", |
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], |
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"LEMBPasskeyRetrieval": [ |
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"test_256", |
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"test_512", |
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"test_1024", |
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"test_2048", |
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"test_4096", |
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"test_8192", |
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"test_16384", |
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"test_32768", |
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], |
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} |
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MODELS = [ |
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"Alibaba-NLP__gte-Qwen1.5-7B-instruct", |
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"Alibaba-NLP__gte-Qwen2-7B-instruct", |
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"BAAI__bge-base-en", |
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"BAAI__bge-base-en-v1.5", |
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"BAAI__bge-base-en-v1.5-instruct", |
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"BAAI__bge-base-zh", |
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"BAAI__bge-base-zh-v1.5", |
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"BAAI__bge-large-en", |
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"BAAI__bge-large-en-v1.5", |
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"BAAI__bge-large-en-v1.5-instruct", |
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"BAAI__bge-large-zh", |
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"BAAI__bge-large-zh-noinstruct", |
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"BAAI__bge-large-zh-v1.5", |
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"BAAI__bge-m3", |
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"BAAI__bge-m3-instruct", |
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"BAAI__bge-small-en-v1.5", |
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"BAAI__bge-small-en-v1.5-instruct", |
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"BAAI__bge-small-zh", |
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"BAAI__bge-small-zh-v1.5", |
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"Cohere__Cohere-embed-english-v3.0", |
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"Cohere__Cohere-embed-english-v3.0-instruct", |
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"Cohere__Cohere-embed-multilingual-light-v3.0", |
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"Cohere__Cohere-embed-multilingual-v3.0", |
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"DeepPavlov__distilrubert-small-cased-conversational", |
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"DeepPavlov__rubert-base-cased", |
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"DeepPavlov__rubert-base-cased-sentence", |
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"FacebookAI__xlm-roberta-base", |
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"FacebookAI__xlm-roberta-large", |
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"Geotrend__bert-base-10lang-cased", |
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"Geotrend__bert-base-15lang-cased", |
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"Geotrend__bert-base-25lang-cased", |
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"Geotrend__distilbert-base-25lang-cased", |
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"Geotrend__distilbert-base-en-fr-cased", |
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"Geotrend__distilbert-base-en-fr-es-pt-it-cased", |
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"Geotrend__distilbert-base-fr-cased", |
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"GritLM__GritLM-7B", |
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"GritLM__GritLM-7B-noinstruct", |
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"KBLab__electra-small-swedish-cased-discriminator", |
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"KBLab__sentence-bert-swedish-cased", |
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"KB__bert-base-swedish-cased", |
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"McGill-NLP__LLM2Vec-Llama-2-7b-chat-hf-mntp-supervised", |
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"McGill-NLP__LLM2Vec-Llama-2-unsupervised", |
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"McGill-NLP__LLM2Vec-Meta-Llama-3-supervised", |
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"McGill-NLP__LLM2Vec-Meta-Llama-3-unsupervised", |
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"McGill-NLP__LLM2Vec-Mistral-supervised", |
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"McGill-NLP__LLM2Vec-Mistral-unsupervised", |
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"McGill-NLP__LLM2Vec-Sheared-Llama-supervised", |
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"McGill-NLP__LLM2Vec-Sheared-Llama-unsupervised", |
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"Muennighoff__SGPT-1.3B-weightedmean-msmarco-specb-bitfit", |
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"Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit", |
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"Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-doc", |
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"Muennighoff__SGPT-125M-weightedmean-msmarco-specb-bitfit-que", |
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"Muennighoff__SGPT-125M-weightedmean-nli-bitfit", |
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"Muennighoff__SGPT-2.7B-weightedmean-msmarco-specb-bitfit", |
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"Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit", |
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"Muennighoff__SGPT-5.8B-weightedmean-msmarco-specb-bitfit-que", |
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"Muennighoff__SGPT-5.8B-weightedmean-nli-bitfit", |
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"NbAiLab__nb-bert-base", |
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"NbAiLab__nb-bert-large", |
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"Salesforce__SFR-Embedding-Mistral", |
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"T-Systems-onsite__cross-en-de-roberta-sentence-transformer", |
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"Wissam42__sentence-croissant-llm-base", |
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"ai-forever__sbert_large_mt_nlu_ru", |
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"ai-forever__sbert_large_nlu_ru", |
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"aliyun__OpenSearch-text-hybrid", |
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"almanach__camembert-base", |
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"almanach__camembert-large", |
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"amazon__titan-embed-text-v1", |
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"baichuan-ai__text-embedding", |
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"bigscience-data__sgpt-bloom-1b7-nli", |
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"bigscience-data__sgpt-bloom-7b1-msmarco", |
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"bm25", |
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"bm25s", |
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"castorini__monobert-large-msmarco", |
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"castorini__monot5-3b-msmarco-10k", |
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"castorini__monot5-base-msmarco-10k", |
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"chcaa__dfm-encoder-large-v1", |
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"cointegrated__LaBSE-en-ru", |
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"cointegrated__rubert-tiny", |
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"cointegrated__rubert-tiny2", |
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"dangvantuan__sentence-camembert-base", |
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"dangvantuan__sentence-camembert-large", |
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"deepfile__embedder-100p", |
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"deepset__gbert-base", |
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"deepset__gbert-large", |
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"deepset__gelectra-base", |
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"deepset__gelectra-large", |
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"deepvk__USER-base", |
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"deepvk__USER-bge-m3", |
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"deepvk__deberta-v1-base", |
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"distilbert__distilbert-base-uncased", |
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"dwzhu__e5-base-4k", |
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"elastic__elser-v2", |
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"facebook__contriever", |
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"facebook__contriever-instruct", |
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"facebook__dpr-ctx_encoder-multiset-base", |
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"facebook__dragon-plus-context-encoder", |
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"facebook__tart-full-flan-t5-xl", |
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"facebookresearch__LASER2", |
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"facebookresearch__dragon-plus", |
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"facebookresearch__dragon-plus-instruct", |
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"flaubert__flaubert_base_cased", |
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"flaubert__flaubert_base_uncased", |
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"flaubert__flaubert_large_cased", |
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"google-bert__bert-base-multilingual-cased", |
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"google-bert__bert-base-multilingual-uncased", |
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"google-bert__bert-base-uncased", |
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"google-gecko__text-embedding-004", |
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"google-gecko__text-embedding-004-256", |
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"google__flan-t5-base", |
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"google__flan-t5-large", |
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"hkunlp__instructor-base", |
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"hkunlp__instructor-large", |
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"hkunlp__instructor-xl", |
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"intfloat__e5-base", |
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"intfloat__e5-base-v2", |
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"intfloat__e5-large", |
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"intfloat__e5-large-v2", |
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"intfloat__e5-mistral-7b-instruct", |
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"intfloat__e5-mistral-7b-instruct-noinstruct", |
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"intfloat__e5-small", |
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"intfloat__e5-small-v2", |
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"intfloat__multilingual-e5-base", |
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"intfloat__multilingual-e5-large", |
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"intfloat__multilingual-e5-large-instruct", |
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"intfloat__multilingual-e5-small", |
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"ipipan__herbert-base-retrieval-v2", |
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"ipipan__silver-retriever-base-v1", |
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"izhx__udever-bloom-1b1", |
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"izhx__udever-bloom-560m", |
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"jhu-clsp__FollowIR-7B", |
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"jinaai__jina-embeddings-v2-base-en", |
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"jonfd__electra-small-nordic", |
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"ltg__norbert3-base", |
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"ltg__norbert3-large", |
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"meta-llama__llama-2-7b-chat", |
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"mistral__mistral-embed", |
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"mistralai__mistral-7b-instruct-v0.2", |
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"mixedbread-ai__mxbai-embed-large-v1", |
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"moka-ai__m3e-base", |
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"moka-ai__m3e-large", |
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"nomic-ai__nomic-embed-text-v1", |
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"nomic-ai__nomic-embed-text-v1.5-128", |
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"nomic-ai__nomic-embed-text-v1.5-256", |
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"nomic-ai__nomic-embed-text-v1.5-512", |
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"nomic-ai__nomic-embed-text-v1.5-64", |
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"nthakur__contriever-base-msmarco", |
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"openai__text-embedding-3-large", |
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"openai__text-embedding-3-large-256", |
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"openai__text-embedding-3-large-instruct", |
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"openai__text-embedding-3-small-instruct", |
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"openai__text-embedding-ada-002", |
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"openai__text-embedding-ada-002-instruct", |
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"openai__text-search-ada-001", |
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"openai__text-search-ada-doc-001", |
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"openai__text-search-babbage-001", |
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"openai__text-search-curie-001", |
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"openai__text-search-davinci-001", |
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"openai__text-similarity-ada-001", |
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"openai__text-similarity-babbage-001", |
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"openai__text-similarity-curie-001", |
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"openai__text-similarity-davinci-001", |
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"openai__text-embedding-3-small", |
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"orionweller__tart-dual-contriever-msmarco", |
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"princeton-nlp__sup-simcse-bert-base-uncased", |
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"princeton-nlp__unsup-simcse-bert-base-uncased", |
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"sdadas__st-polish-paraphrase-from-distilroberta", |
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"sdadas__st-polish-paraphrase-from-mpnet", |
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"sentence-transformers__LaBSE", |
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"sentence-transformers__all-MiniLM-L12-v2", |
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"sentence-transformers__all-MiniLM-L6-v2", |
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"sentence-transformers__all-MiniLM-L6-v2-instruct", |
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"sentence-transformers__all-mpnet-base-v2", |
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"sentence-transformers__all-mpnet-base-v2-instruct", |
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"sentence-transformers__allenai-specter", |
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"sentence-transformers__average_word_embeddings_glove.6B.300d", |
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"sentence-transformers__average_word_embeddings_komninos", |
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"sentence-transformers__distiluse-base-multilingual-cased-v2", |
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"sentence-transformers__gtr-t5-base", |
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"sentence-transformers__gtr-t5-large", |
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"sentence-transformers__gtr-t5-xl", |
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"sentence-transformers__gtr-t5-xxl", |
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"sentence-transformers__msmarco-bert-co-condensor", |
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"sentence-transformers__multi-qa-MiniLM-L6-cos-v1", |
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"sentence-transformers__paraphrase-multilingual-MiniLM-L12-v2", |
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"sentence-transformers__paraphrase-multilingual-mpnet-base-v2", |
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"sentence-transformers__sentence-t5-base", |
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"sentence-transformers__sentence-t5-large", |
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"sentence-transformers__sentence-t5-xl", |
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"sentence-transformers__sentence-t5-xxl", |
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"sentence-transformers__use-cmlm-multilingual", |
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"sergeyzh__LaBSE-ru-turbo", |
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"sergeyzh__rubert-tiny-turbo", |
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"shibing624__text2vec-base-chinese", |
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"shibing624__text2vec-base-multilingual", |
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"shibing624__text2vec-large-chinese", |
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"silk-road__luotuo-bert-medium", |
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"uklfr__gottbert-base", |
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"vesteinn__DanskBERT", |
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"voyageai__voyage-2", |
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"voyageai__voyage-code-2", |
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"voyageai__voyage-large-2-instruct", |
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"voyageai__voyage-law-2", |
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"voyageai__voyage-lite-01-instruct", |
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"voyageai__voyage-lite-02-instruct", |
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"voyageai__voyage-multilingual-2", |
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"voyageai__voyage-3", |
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"voyageai__voyage-3-lite", |
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"vprelovac__universal-sentence-encoder-multilingual-3", |
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"vprelovac__universal-sentence-encoder-multilingual-large-3", |
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] |
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|
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def get_model_for_current_dir(dir_name: str) -> str | None: |
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for model in MODELS: |
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if model == dir_name or ("__" in dir_name and dir_name.split("__")[1] == model): |
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return model |
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return None |
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|
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def get_paths(): |
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import collections, json, os |
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|
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files = collections.defaultdict(list) |
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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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if not os.path.isdir(results_model_dir): |
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print(f"Skipping {results_model_dir}") |
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continue |
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model_name = get_model_for_current_dir(model_dir) |
|
if model_name is None: |
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print(f"Skipping {model_dir} model dir") |
|
continue |
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for revision_folder in os.listdir(results_model_dir): |
|
if not os.path.isdir(os.path.join(results_model_dir, revision_folder)): |
|
continue |
|
for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)): |
|
if (res_file.endswith(".json")) and not ( |
|
res_file.endswith(("overall_results.json", "model_meta.json")) |
|
): |
|
results_model_file = os.path.join(results_model_dir, revision_folder, res_file) |
|
files[model_name].append(results_model_file) |
|
with open("paths.json", "w") as f: |
|
json.dump(files, f, indent=2) |
|
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"), |
|
"split": datasets.Value("string"), |
|
"hf_subset": datasets.Value("string"), |
|
} |
|
), |
|
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.get("mteb_dataset_name", res_dict.get("task_name")) |
|
|
|
res_dict = res_dict.get("scores", res_dict) |
|
|
|
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 (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict): |
|
split = "test.full" |
|
elif ds_name in STANDARD_SPLIT: |
|
split = [] |
|
if "standard" in res_dict: |
|
split += ["standard"] |
|
if "long" in res_dict: |
|
split += ["long"] |
|
elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict): |
|
split = "devtest" |
|
elif ds_name in TEST_AVG_SPLIT: |
|
|
|
res_dict = {} |
|
for split in TEST_AVG_SPLIT[ds_name]: |
|
|
|
if isinstance(res_dict.get(split), dict): |
|
for k, v in res_dict.get(split, {}).items(): |
|
if k in ["hf_subset", "languages"]: |
|
res_dict[k] = v |
|
|
|
v /= len(TEST_AVG_SPLIT[ds_name]) |
|
if k not in res_dict: |
|
res_dict[k] = v |
|
else: |
|
res_dict[k] += v |
|
|
|
elif isinstance(res_dict.get(split), list): |
|
assert len(res_dict[split]) == 1, "Only single-lists supported for now" |
|
for k, v in res_dict[split][0].items(): |
|
if k in ["hf_subset", "languages"]: |
|
res_dict[k] = v |
|
if not isinstance(v, float): |
|
continue |
|
v /= len(TEST_AVG_SPLIT[ds_name]) |
|
if k not in res_dict: |
|
res_dict[k] = v |
|
else: |
|
res_dict[k] += v |
|
split = "test_avg" |
|
res_dict = {split: [res_dict]} |
|
elif "test" not in res_dict: |
|
print(f"Skipping {ds_name} as split {split} not present.") |
|
continue |
|
|
|
splits = [split] if not isinstance(split, list) else split |
|
full_res_dict = res_dict |
|
for split in splits: |
|
res_dict = full_res_dict.get(split) |
|
|
|
|
|
if isinstance(res_dict, list): |
|
for res in res_dict: |
|
lang = res.pop("languages", [""]) |
|
subset = res.pop("hf_subset", "") |
|
if len(lang) == 1: |
|
lang = lang[0].replace("eng-Latn", "") |
|
else: |
|
lang = "_".join(lang) |
|
if not lang: |
|
lang = subset |
|
for metric, score in res.items(): |
|
if metric in SKIP_KEYS: |
|
continue |
|
if isinstance(score, dict): |
|
|
|
|
|
for k, v in score.items(): |
|
if not isinstance(v, float): |
|
print(f"WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}") |
|
continue |
|
if metric in SKIP_KEYS: |
|
continue |
|
out.append( |
|
{ |
|
"mteb_dataset_name": ds_name, |
|
"eval_language": lang, |
|
"metric": metric + "_" + k, |
|
"score": v * 100, |
|
"hf_subset": subset, |
|
} |
|
) |
|
else: |
|
if not isinstance(score, float): |
|
print(f"WARNING: Expected float, got {score} for {ds_name} {lang} {metric}") |
|
continue |
|
out.append( |
|
{ |
|
"mteb_dataset_name": ds_name, |
|
"eval_language": lang, |
|
"metric": metric, |
|
"score": score * 100, |
|
"split": split, |
|
"hf_subset": subset, |
|
} |
|
) |
|
|
|
|
|
else: |
|
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 |
|
subset = test_result_lang.pop("hf_subset", "") |
|
if subset == "" and is_multilingual: |
|
subset = lang |
|
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 |
|
if isinstance(sub_score, dict): |
|
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, |
|
"split": split, |
|
"hf_subset": subset, |
|
} |
|
) |
|
for idx, row in enumerate(sorted(out, key=lambda x: x["mteb_dataset_name"])): |
|
yield idx, row |
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
get_paths() |
|
|
