Upload curriculum_benchmark.py

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]