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"""MTEB Results""" |
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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 Portuguese""" |
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URL = "https://huggingface.co/datasets/projetomemoreba/results/resolve/main/paths.json" |
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VERSION = datasets.Version("1.0.1") |
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EVAL_LANGS = ['pt'] |
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SKIP_KEYS = ["std", "evaluation_time", "main_score", "threshold"] |
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TRAIN_SPLIT = ["DanishPoliticalCommentsClassification"] |
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VALIDATION_SPLIT = ["AFQMC", "Cmnli", "IFlyTek", "TNews", "MSMARCO", "MSMARCO-PL", "MultilingualSentiment", "Ocnli"] |
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DEV_SPLIT = ["CmedqaRetrieval", "CovidRetrieval", "DuRetrieval", "EcomRetrieval", "MedicalRetrieval", "MMarcoReranking", "MMarcoRetrieval", "MSMARCO", "MSMARCO-PL", "T2Reranking", "T2Retrieval", "VideoRetrieval"] |
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MODELS = [ |
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"instructor-base", |
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"xlm-roberta-large", |
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"gtr-t5-large", |
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"sentence-t5-xxl", |
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"GIST-Embedding-v0", |
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"e5-base", |
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"mxbai-embed-2d-large-v1", |
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"SGPT-5.8B-weightedmean-nli-bitfit", |
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"jina-embeddings-v2-base-de", |
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"gte-base", |
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"jina-embedding-b-en-v1", |
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"LaBSE", |
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"sgpt-bloom-7b1-msmarco", |
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"bi-cse", |
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"distilbert-base-uncased", |
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"bert-base-10lang-cased", |
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"sentence-t5-large", |
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"jina-embeddings-v2-small-en", |
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"e5-mistral-7b-instruct", |
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"bge-base-en-v1.5", |
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"ember-v1", |
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"e5-large-v2", |
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"lodestone-base-4096-v1", |
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"all-mpnet-base-v2", |
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"sentence-t5-xl", |
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"distilbert-base-en-fr-cased", |
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"gte-tiny", |
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"text2vec-base-multilingual", |
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"GIST-all-MiniLM-L6-v2", |
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"jina-embeddings-v2-base-es", |
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"bert-base-multilingual-uncased", |
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"distiluse-base-multilingual-cased-v2", |
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"sup-simcse-bert-base-uncased", |
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"e5-small-v2", |
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"GritLM-7B", |
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"sentence-t5-base", |
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"SFR-Embedding-Mistral", |
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"mxbai-embed-large-v1", |
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"stella-base-en-v2", |
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"udever-bloom-3b", |
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"bert-base-multilingual-cased", |
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"all-MiniLM-L12-v2", |
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"sf_model_e5", |
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"bert-base-portuguese-cased", |
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"bge-small-en-v1.5", |
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"SGPT-125M-weightedmean-msmarco-specb-bitfit", |
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"udever-bloom-560m", |
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"gtr-t5-base", |
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"fin-mpnet-base", |
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"SGPT-2.7B-weightedmean-msmarco-specb-bitfit", |
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"xlm-roberta-base", |
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"GIST-small-Embedding-v0", |
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"gte-large", |
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"ALL_862873", |
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"e5-large", |
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"distilbert-base-en-fr-es-pt-it-cased", |
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"dfm-sentence-encoder-large-v1", |
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"bge-micro", |
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"instructor-large", |
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"average_word_embeddings_glove.6B.300d", |
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"multilingual-e5-large-instruct", |
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"msmarco-bert-co-condensor", |
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"multilingual-e5-small", |
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"UAE-Large-V1", |
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"udever-bloom-1b1", |
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"distilbert-base-fr-cased", |
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"instructor-xl", |
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"bert-base-uncased", |
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"all-MiniLM-L6-v2", |
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"e5-base-v2", |
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"jina-embedding-l-en-v1", |
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"gtr-t5-xl", |
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"gte-small", |
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"bge-small-4096", |
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"average_word_embeddings_komninos", |
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"unsup-simcse-bert-base-uncased", |
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"bert-base-15lang-cased", |
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"paraphrase-multilingual-MiniLM-L12-v2", |
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"distilbert-base-25lang-cased", |
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"contriever-base-msmarco", |
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"multilingual-e5-large", |
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"luotuo-bert-medium", |
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"GIST-large-Embedding-v0", |
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"bge-large-en-v1.5", |
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"cai-lunaris-text-embeddings", |
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"gtr-t5-xxl", |
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"multilingual-e5-base", |
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"paraphrase-multilingual-mpnet-base-v2", |
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"SGPT-1.3B-weightedmean-msmarco-specb-bitfit", |
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"e5-dansk-test-0.1", |
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"allenai-specter" |
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] |
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from pathlib import Path |
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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 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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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[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) |
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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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} |
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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 [ |
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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(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["mteb_dataset_name"] |
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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 "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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res_dict = res_dict.get(split) |
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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: continue |
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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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"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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}) |
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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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