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"""CURRICULUM Benchmark""" |
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import json |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """\ |
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@misc{https://doi.org/10.48550/arxiv.2204.06283, |
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doi = {10.48550/ARXIV.2204.06283}, |
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url = {https://arxiv.org/abs/2204.06283}, |
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author = {Chen, Zeming and Gao, Qiyue}, |
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keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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""" |
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_DESCRIPTION = """\ |
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We introduce Curriculum as a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena. |
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Curriculum contains a collection of datasets that covers 36 types of major linguistic phenomena and an evaluation procedure |
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for diagnosing how well a language model captures reasoning skills for distinct types of linguistic phenomena. |
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We show that this linguistic-phenomena-driven benchmark can serve as an effective tool for diagnosing |
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model behavior and verifying model learning quality. |
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""" |
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_HOMEPAGE = "https://github.com/eric11eca/curriculum-ling" |
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_LICENSE = "CC BY-SA 3.0" |
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_URL = "https://github.com/eric11eca/curriculum-ling/blob/main/benchmark/tasks/" |
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_DESCRIPTION_MAP = { |
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"analytic": "analytical thinking.", |
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"atomic": "reasoning on commonsense knowledge graph.", |
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} |
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_TAKS_NAMES = ["analytic", "defeasible", "boolean", "comparative", |
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"conditional", "context_align", "control", "coreference", |
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"cosmoqa", "counterfactual", "counting", "drop", |
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"entailment_tree", "ester", "hellaswag", "hypernymy", |
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"hyponymy", "kg_relations", "lexical", "logiqa", |
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"monotonicity_infer", "negation", "ner", "physicalqa", |
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"puns", "quantifier", "sentiment", "socialqa", |
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"spatial", "sprl", "syntactic_alternation", "syntactic_variation", |
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"temporal", "transitive", "verbcorner", "verbnet"] |
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task_label_dict = { |
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"lexical": ["entailed", "not-entailed"], |
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"transitive": ["entailed", "not-entailed"], |
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"hypernymy": ["entailed", "not-entailed"], |
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"hyponymy": ["entailed", "not-entailed"], |
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"ner": ["entailed", "not-entailed"], |
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"verbnet": ["entailed", "not-entailed"], |
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"verbcorner": ["entailed", "not-entailed"], |
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"syntactic_alternation": ["entailed", "not-entailed"], |
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"syntactic_variation": ["entailed", "not-entailed"], |
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"boolean": ["entailment", "contradiction", "neutral"], |
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"comparative": ["entailment", "contradiction", "neutral"], |
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"conditional": ["entailment", "contradiction", "neutral"], |
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"counting": ["entailment", "contradiction", "neutral"], |
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"negation": ["entailment", "contradiction", "neutral"], |
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"quantifier": ["entailment", "contradiction", "neutral"], |
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"monotonicity_infer": ["entailed", "not-entailed"], |
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"sentiment": ["entailed", "not-entailed"], |
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"kg_relations": ["entailed", "not-entailed"], |
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"puns": ["entailed", "not-entailed"], |
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"coreference": ["entailed", "not-entailed"], |
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"context_align": ["entailed", "not-entailed"], |
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"sprl": ["entailed", "not-entailed"], |
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"analytic": ["entailed", "not-entailed"], |
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"entailment_tree": ["entailed", "not-entailed"], |
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"socialqa": ["entailed", "not-entailed"], |
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"physicalqa": ["entailed", "not-entailed"], |
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"hellaswag": ["entailed", "not-entailed"], |
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"cosmoqa": ["entailed", "not-entailed"], |
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"logiqa": ["entailed", "not-entailed"], |
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"ester": ["entailed", "not-entailed"], |
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"drop": ["entailed", "not-entailed"], |
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"control": ["entailment", "contradiction", "neutral"], |
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"spatial": ["entailed", "not-entailed"], |
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"temporal": ["entailed", "not-entailed"], |
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"defeasible": ["entailed", "not-entailed"], |
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"counterfactual": ["entailed", "not-entailed"] |
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} |
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def read_file(path, mode="r", **kwargs): |
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with open(path, mode=mode, **kwargs) as f: |
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return f.read() |
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def write_file(data, path, mode="w", **kwargs): |
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with open(path, mode=mode, **kwargs) as f: |
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f.write(data) |
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def read_json(path, mode="r", **kwargs): |
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return json.loads(read_file(path, mode=mode, **kwargs)) |
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def write_json(data, path): |
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return write_file(json.dumps(data, indent=2), path) |
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def read_jsonl(path, mode="r", **kwargs): |
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ls = [] |
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with open(path, mode, **kwargs) as f: |
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for line in f: |
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ls.append(json.loads(line)) |
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return ls |
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def write_jsonl(data, path): |
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assert isinstance(data, list) |
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lines = [to_jsonl(elem) for elem in data] |
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write_file("\n".join(lines), path) |
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def to_jsonl(data): |
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return json.dumps(data).replace("\n", "") |
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class CurriculumConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Curriculum.""" |
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def __init__(self, features, data_url, citation, url, label_classes=["entailed", "not-entailed"], **kwargs): |
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"""BuilderConfig for Curriculum. |
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Args: |
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features: `list[string]`, list of the features that will appear in the |
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feature dict. Should not include "label". |
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data_url: `string`, url to download the zip file from. |
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citation: `string`, citation for the data set. |
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url: `string`, url for information about the data set. |
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label_classes: `list[string]`, the list of classes for the label if the |
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label is present as a string. Non-string labels will be cast to either |
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'False' or 'True'. |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(CurriculumConfig, self).__init__( |
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version=datasets.Version("1.0.0"), **kwargs) |
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self.features = features |
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self.label_classes = label_classes |
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self.data_url = data_url |
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self.citation = citation |
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self.url = url |
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class CurriculumBenchmark(datasets.GeneratorBasedBuilder): |
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"""Curriculum Benchmark. Version 1.0.0""" |
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BUILDER_CONFIGS = [ |
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CurriculumConfig( |
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name=task_name, |
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description=_DESCRIPTION, |
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label_classes=task_label_dict[task_name], |
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features=["premise", "hypothesis", "idx", "gold_label"], |
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data_url=f"https://github.com/eric11eca/curriculum-ling/raw/main/benchmark/tasks/{task_name}.zip", |
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citation=_CITATION, |
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url="https://github.com/eric11eca/curriculum-ling/", |
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) for task_name in _TAKS_NAMES |
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] |
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def _info(self): |
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features = {feature: datasets.Value( |
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"string") for feature in self.config.features} |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features(features), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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@staticmethod |
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def _get_filepath(dl_dir, split): |
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return os.path.join(dl_dir, split + ".jsonl") |
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def _split_generators(self, dl_manager): |
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dl_dir = dl_manager.download_and_extract(self.config.data_url) or "" |
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task_name = _get_task_name_from_data_url(self.config.data_url) |
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dl_dir = os.path.join(dl_dir, task_name) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"data_file": os.path.join(dl_dir, "train.jsonl"), |
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"split": datasets.Split.TRAIN, |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"data_file": os.path.join(dl_dir, "val.jsonl"), |
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"split": datasets.Split.VALIDATION, |
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}, |
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) |
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] |
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def _generate_examples(self, data_file, split): |
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"""This function returns the examples in the raw (text) form.""" |
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logger.info("generating examples from = %s", data_file) |
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dataset = read_jsonl(data_file) |
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for id_, data in enumerate(dataset): |
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yield id_, { |
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"premise": data["premise"], |
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"hypothesis": data["hypothesis"], |
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"gold_label": data["gold_label"], |
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"idx": id_ |
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} |
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def _get_task_name_from_data_url(data_url): |
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return data_url.split("/")[-1].split(".")[0] |
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