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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 os |
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from pathlib import Path |
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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 = sorted(list(set([str(file).split('/')[-1] for file in (Path(__file__).parent / "results").glob("*") if file.is_dir()]))) |
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def get_paths(): |
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import collections, json, os |
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files = collections.defaultdict(list) |
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for model_dir in MODELS: |
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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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for revision_folder in os.listdir(results_model_dir): |
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if not os.path.isdir(os.path.join(results_model_dir, revision_folder)): |
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continue |
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if revision_folder == "external": |
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continue |
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for res_file in os.listdir(os.path.join(results_model_dir, revision_folder)): |
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if (res_file.endswith(".json")) and not ( |
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res_file.endswith(("overall_results.json", "model_meta.json")) |
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): |
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results_model_file = os.path.join(results_model_dir, revision_folder, res_file) |
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files[model_dir].append(results_model_file) |
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with open("paths.json", "w") as f: |
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json.dump(files, f, indent=2) |
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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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"mteb_dataset_name": datasets.Value("string"), |
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"eval_language": datasets.Value("string"), |
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"metric": datasets.Value("string"), |
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"score": datasets.Value("float"), |
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"split": datasets.Value("string"), |
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"hf_subset": datasets.Value("string"), |
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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) 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 [datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files})] |
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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(f"Generating examples from {filepath}") |
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out = [] |
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for path in filepath: |
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with open(path, encoding="utf-8") as f: |
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res_dict = json.load(f) |
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ds_name = res_dict.get("mteb_dataset_name", res_dict.get("task_name")) |
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res_dict = res_dict.get("scores", res_dict) |
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split = "test" |
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if (ds_name in TRAIN_SPLIT) and ("train" in res_dict): |
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split = "train" |
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elif (ds_name in VALIDATION_SPLIT) and ("validation" in res_dict): |
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split = "validation" |
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elif (ds_name in DEV_SPLIT) and ("dev" in res_dict): |
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split = "dev" |
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elif (ds_name in TESTFULL_SPLIT) and ("test.full" in res_dict): |
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split = "test.full" |
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elif ds_name in STANDARD_SPLIT: |
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split = [] |
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if "standard" in res_dict: |
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split += ["standard"] |
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if "long" in res_dict: |
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split += ["long"] |
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elif (ds_name in DEVTEST_SPLIT) and ("devtest" in res_dict): |
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split = "devtest" |
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elif ds_name in TEST_AVG_SPLIT: |
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res_dict = {} |
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for split in TEST_AVG_SPLIT[ds_name]: |
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if isinstance(res_dict.get(split), dict): |
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for k, v in res_dict.get(split, {}).items(): |
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if k in ["hf_subset", "languages"]: |
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res_dict[k] = v |
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v /= len(TEST_AVG_SPLIT[ds_name]) |
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if k not in res_dict: |
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res_dict[k] = v |
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else: |
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res_dict[k] += v |
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elif isinstance(res_dict.get(split), list): |
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assert len(res_dict[split]) == 1, "Only single-lists supported for now" |
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for k, v in res_dict[split][0].items(): |
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if k in ["hf_subset", "languages"]: |
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res_dict[k] = v |
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if not isinstance(v, float): |
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continue |
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v /= len(TEST_AVG_SPLIT[ds_name]) |
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if k not in res_dict: |
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res_dict[k] = v |
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else: |
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res_dict[k] += v |
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split = "test_avg" |
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res_dict = {split: [res_dict]} |
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elif "test" 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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splits = [split] if not isinstance(split, list) else split |
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full_res_dict = res_dict |
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for split in splits: |
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res_dict = full_res_dict.get(split) |
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if isinstance(res_dict, list): |
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for res in res_dict: |
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lang = res.pop("languages", [""]) |
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subset = res.pop("hf_subset", "") |
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if len(lang) == 1: |
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lang = lang[0].replace("eng-Latn", "") |
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else: |
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lang = "_".join(lang) |
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if not lang: |
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lang = subset |
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for metric, score in res.items(): |
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if metric in SKIP_KEYS: |
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continue |
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if isinstance(score, dict): |
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for k, v in score.items(): |
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if not isinstance(v, float): |
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print(f"WARNING: Expected float, got {v} for {ds_name} {lang} {metric} {k}") |
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continue |
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if metric in SKIP_KEYS: |
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continue |
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out.append( |
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{ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang, |
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"metric": metric + "_" + k, |
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"score": v * 100, |
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"hf_subset": subset, |
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} |
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) |
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else: |
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if not isinstance(score, float): |
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print(f"WARNING: Expected float, got {score} for {ds_name} {lang} {metric}") |
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continue |
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out.append( |
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{ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang, |
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"metric": metric, |
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"score": score * 100, |
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"split": split, |
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"hf_subset": subset, |
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} |
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) |
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else: |
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is_multilingual = any(x in res_dict for x in EVAL_LANGS) |
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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: |
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continue |
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test_result_lang = res_dict.get(lang) if is_multilingual else res_dict |
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subset = test_result_lang.pop("hf_subset", "") |
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if subset == "" and is_multilingual: |
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subset = lang |
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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): |
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continue |
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if isinstance(sub_score, dict): |
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continue |
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out.append( |
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{ |
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"mteb_dataset_name": ds_name, |
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"eval_language": lang if is_multilingual else "", |
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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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"split": split, |
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"hf_subset": subset, |
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} |
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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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if __name__ == "__main__": |
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get_paths() |
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