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README.md

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----
-license: mit
----

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curriculum_benchmark.py

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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,
-        )
-
-    @staticmethod
-    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]

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