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| # Lint as: python3 | |
| """CURRICULUM Benchmark""" | |
| import json | |
| import os | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @misc{https://doi.org/10.48550/arxiv.2204.06283, | |
| doi = {10.48550/ARXIV.2204.06283}, | |
| url = {https://arxiv.org/abs/2204.06283}, | |
| author = {Chen, Zeming and Gao, Qiyue}, | |
| keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences}, | |
| title = {Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding}, | |
| publisher = {arXiv}, | |
| year = {2022}, | |
| copyright = {Creative Commons Attribution 4.0 International} | |
| } | |
| """ | |
| _DESCRIPTION = """\ | |
| We introduce Curriculum as a new format of NLI benchmark for evaluation of broad-coverage linguistic phenomena. | |
| Curriculum contains a collection of datasets that covers 36 types of major linguistic phenomena and an evaluation procedure | |
| for diagnosing how well a language model captures reasoning skills for distinct types of linguistic phenomena. | |
| We show that this linguistic-phenomena-driven benchmark can serve as an effective tool for diagnosing | |
| model behavior and verifying model learning quality. | |
| """ | |
| _HOMEPAGE = "https://github.com/eric11eca/curriculum-ling" | |
| _LICENSE = "CC BY-SA 3.0" | |
| _URL = "https://github.com/eric11eca/curriculum-ling/blob/main/benchmark/tasks/" | |
| _DESCRIPTION_MAP = { | |
| "analytic": "analytical thinking.", | |
| "atomic": "reasoning on commonsense knowledge graph.", | |
| } | |
| _TAKS_NAMES = ["analytic", "defeasible", "boolean", "comparative", | |
| "conditional", "context_align", "control", "coreference", | |
| "cosmoqa", "counterfactual", "counting", "drop", | |
| "entailment_tree", "ester", "hellaswag", "hypernymy", | |
| "hyponymy", "kg_relations", "lexical", "logiqa", | |
| "monotonicity_infer", "negation", "ner", "physicalqa", | |
| "puns", "quantifier", "sentiment", "socialqa", | |
| "spatial", "sprl", "syntactic_alternation", "syntactic_variation", | |
| "temporal", "transitive", "verbcorner", "verbnet"] | |
| task_label_dict = { | |
| "lexical": ["entailed", "not-entailed"], | |
| "transitive": ["entailed", "not-entailed"], | |
| "hypernymy": ["entailed", "not-entailed"], | |
| "hyponymy": ["entailed", "not-entailed"], | |
| "ner": ["entailed", "not-entailed"], | |
| "verbnet": ["entailed", "not-entailed"], | |
| "verbcorner": ["entailed", "not-entailed"], | |
| "syntactic_alternation": ["entailed", "not-entailed"], | |
| "syntactic_variation": ["entailed", "not-entailed"], | |
| "boolean": ["entailment", "contradiction", "neutral"], | |
| "comparative": ["entailment", "contradiction", "neutral"], | |
| "conditional": ["entailment", "contradiction", "neutral"], | |
| "counting": ["entailment", "contradiction", "neutral"], | |
| "negation": ["entailment", "contradiction", "neutral"], | |
| "quantifier": ["entailment", "contradiction", "neutral"], | |
| "monotonicity_infer": ["entailed", "not-entailed"], | |
| "sentiment": ["entailed", "not-entailed"], | |
| "kg_relations": ["entailed", "not-entailed"], | |
| "puns": ["entailed", "not-entailed"], | |
| "coreference": ["entailed", "not-entailed"], | |
| "context_align": ["entailed", "not-entailed"], | |
| "sprl": ["entailed", "not-entailed"], | |
| "analytic": ["entailed", "not-entailed"], | |
| "entailment_tree": ["entailed", "not-entailed"], | |
| "socialqa": ["entailed", "not-entailed"], | |
| "physicalqa": ["entailed", "not-entailed"], | |
| "hellaswag": ["entailed", "not-entailed"], | |
| "cosmoqa": ["entailed", "not-entailed"], | |
| "logiqa": ["entailed", "not-entailed"], | |
| "ester": ["entailed", "not-entailed"], | |
| "drop": ["entailed", "not-entailed"], | |
| "control": ["entailment", "contradiction", "neutral"], | |
| "spatial": ["entailed", "not-entailed"], | |
| "temporal": ["entailed", "not-entailed"], | |
| "defeasible": ["entailed", "not-entailed"], | |
| "counterfactual": ["entailed", "not-entailed"] | |
| } | |
| def read_file(path, mode="r", **kwargs): | |
| with open(path, mode=mode, **kwargs) as f: | |
| return f.read() | |
| def write_file(data, path, mode="w", **kwargs): | |
| with open(path, mode=mode, **kwargs) as f: | |
| f.write(data) | |
| def read_json(path, mode="r", **kwargs): | |
| return json.loads(read_file(path, mode=mode, **kwargs)) | |
| def write_json(data, path): | |
| return write_file(json.dumps(data, indent=2), path) | |
| def read_jsonl(path, mode="r", **kwargs): | |
| # Manually open because .splitlines is different from iterating over lines | |
| ls = [] | |
| with open(path, mode, **kwargs) as f: | |
| for line in f: | |
| ls.append(json.loads(line)) | |
| return ls | |
| def write_jsonl(data, path): | |
| assert isinstance(data, list) | |
| lines = [to_jsonl(elem) for elem in data] | |
| write_file("\n".join(lines), path) | |
| def to_jsonl(data): | |
| return json.dumps(data).replace("\n", "") | |
| class CurriculumConfig(datasets.BuilderConfig): | |
| """BuilderConfig for Curriculum.""" | |
| def __init__(self, features, data_url, citation, url, label_classes=["entailed", "not-entailed"], **kwargs): | |
| """BuilderConfig for Curriculum. | |
| Args: | |
| features: `list[string]`, list of the features that will appear in the | |
| feature dict. Should not include "label". | |
| data_url: `string`, url to download the zip file from. | |
| citation: `string`, citation for the data set. | |
| url: `string`, url for information about the data set. | |
| label_classes: `list[string]`, the list of classes for the label if the | |
| label is present as a string. Non-string labels will be cast to either | |
| 'False' or 'True'. | |
| **kwargs: keyword arguments forwarded to super. | |
| """ | |
| # Version history: | |
| # 1.0.0: Initial version. | |
| super(CurriculumConfig, self).__init__( | |
| version=datasets.Version("1.0.0"), **kwargs) | |
| self.features = features | |
| self.label_classes = label_classes | |
| self.data_url = data_url | |
| self.citation = citation | |
| self.url = url | |
| class CurriculumBenchmark(datasets.GeneratorBasedBuilder): | |
| """Curriculum Benchmark. Version 1.0.0""" | |
| BUILDER_CONFIGS = [ | |
| CurriculumConfig( | |
| name=task_name, | |
| description=_DESCRIPTION, | |
| label_classes=task_label_dict[task_name], | |
| features=["premise", "hypothesis", "idx", "gold_label"], | |
| data_url=f"https://github.com/eric11eca/curriculum-ling/raw/main/benchmark/tasks/{task_name}.zip", | |
| citation=_CITATION, | |
| url="https://github.com/eric11eca/curriculum-ling/", | |
| ) for task_name in _TAKS_NAMES | |
| ] | |
| def _info(self): | |
| features = {feature: datasets.Value( | |
| "string") for feature in self.config.features} | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features(features), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _get_filepath(dl_dir, split): | |
| return os.path.join(dl_dir, split + ".jsonl") | |
| def _split_generators(self, dl_manager): | |
| dl_dir = dl_manager.download_and_extract(self.config.data_url) or "" | |
| task_name = _get_task_name_from_data_url(self.config.data_url) | |
| dl_dir = os.path.join(dl_dir, task_name) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "data_file": os.path.join(dl_dir, "train.jsonl"), | |
| "split": datasets.Split.TRAIN, | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "data_file": os.path.join(dl_dir, "val.jsonl"), | |
| "split": datasets.Split.VALIDATION, | |
| }, | |
| ) | |
| ] | |
| def _generate_examples(self, data_file, split): | |
| """This function returns the examples in the raw (text) form.""" | |
| logger.info("generating examples from = %s", data_file) | |
| dataset = read_jsonl(data_file) | |
| for id_, data in enumerate(dataset): | |
| yield id_, { | |
| "premise": data["premise"], | |
| "hypothesis": data["hypothesis"], | |
| "gold_label": data["gold_label"], | |
| "idx": id_ | |
| } | |
| def _get_task_name_from_data_url(data_url): | |
| return data_url.split("/")[-1].split(".")[0] | |