# 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]