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README.md

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----
-license: cc-by-4.0
----

default/qa_adj-test.parquet

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default/qa_adj-train.parquet

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default/qa_adj-validation.parquet

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full/qa_adj-propbank.parquet

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full/qa_adj-validation.parquet

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qa_adj.py

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""A Dataset loading script for the QA-Adj dataset."""
-
-
-from dataclasses import dataclass
-from typing import Optional, Tuple, Union, Iterable, Set
-from pathlib import Path
-import itertools
-import pandas as pd
-import datasets
-
-
-_DESCRIPTION = """\
-The dataset contains question-answer pairs to capture adjectival semantics.  
-This dataset was annotated by selected workers from Amazon Mechanical Turk.
-"""
-
-_LICENSE = """MIT License
-
-Copyright (c) 2022 Ayal Klein (kleinay)
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE."""
-
-URL = "https://github.com/kleinay/QA-Adj-Dataset/raw/main/QAADJ_Dataset.zip"
-
-SUPPOERTED_DOMAINS = {"wikinews", "wikipedia"}
-
-@dataclass
-class QAAdjBuilderConfig(datasets.BuilderConfig):
-    domains: Union[str, Iterable[str]] = "all" # can provide also a subset of acceptable domains.
-    full_dataset: bool = False
-
-class QaAdj(datasets.GeneratorBasedBuilder):
-    """QAAdj: Question-Answer based semantics for adjectives.
-    """
-
-    VERSION = datasets.Version("1.0.0")
-    
-    BUILDER_CONFIG_CLASS = QAAdjBuilderConfig
-
-    BUILDER_CONFIGS = [
-        QAAdjBuilderConfig(
-            name="default", version=VERSION, description="This provides the QAAdj dataset - train, dev and test"#, redistribute_dev=(0,1,0)
-        ),
-        QAAdjBuilderConfig(
-            name="full", version=VERSION, full_dataset=True, 
-            description="""This provides the QAAdj dataset including gold reference 
-            (300 expert-annotated instances) and propbank comparison instances"""
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = (
-        "default"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-    )
-    
-    def _info(self):
-        features = datasets.Features(
-            {
-                "sentence": datasets.Value("string"),
-                "sent_id": datasets.Value("string"),
-                "predicate_idx": datasets.Value("int32"),
-                "predicate_idx_end": datasets.Value("int32"),
-                "predicate": datasets.Value("string"),
-                "object_question": datasets.Value("string"),
-                "object_answer": datasets.Sequence(datasets.Value("string")),
-                "domain_question": datasets.Value("string"),
-                "domain_answer": datasets.Sequence(datasets.Value("string")),
-                "reference_question": datasets.Value("string"),
-                "reference_answer": datasets.Sequence(datasets.Value("string")),
-                "extent_question": datasets.Value("string"),
-                "extent_answer": datasets.Sequence(datasets.Value("string")),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            # homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            # citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager: datasets.utils.download_manager.DownloadManager):
-        """Returns SplitGenerators."""  
-        
-        # Handle domain selection
-        domains: Set[str] = [] 
-        if self.config.domains == "all":
-            domains = SUPPOERTED_DOMAINS
-        elif isinstance(self.config.domains, str):
-            if self.config.domains in SUPPOERTED_DOMAINS:
-                domains = {self.config.domains}
-            else:
-                raise ValueError(f"Unrecognized domain '{self.config.domains}'; only {SUPPOERTED_DOMAINS} are supported")
-        else:
-            domains = set(self.config.domains) & SUPPOERTED_DOMAINS
-            if len(domains) == 0:
-                raise ValueError(f"Unrecognized domains '{self.config.domains}'; only {SUPPOERTED_DOMAINS} are supported")
-        self.config.domains = domains
-    
-        self.corpus_base_path = Path(dl_manager.download_and_extract(URL))
-
-        splits = [
-                datasets.SplitGenerator(
-                    name=datasets.Split.TRAIN,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "csv_fn": self.corpus_base_path / "train.csv",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.VALIDATION,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "csv_fn": self.corpus_base_path / "dev.csv",
-                    },
-                ),
-                datasets.SplitGenerator(
-                    name=datasets.Split.TEST,
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "csv_fn": self.corpus_base_path / "test.csv",
-                    },
-                ),
-            ]
-        if self.config.full_dataset:
-            splits = splits + [
-                # ##TODO change "reference_data.csv" to be in same format and add it to zip file
-                # datasets.SplitGenerator(
-                #     name="gold_reference",
-                #     # These kwargs will be passed to _generate_examples
-                #     gen_kwargs={
-                #         "csv_fn": self.corpus_base_path / "reference_data.csv",
-                #     },
-                # ),
-                datasets.SplitGenerator(
-                    name="propbank",
-                    # These kwargs will be passed to _generate_examples
-                    gen_kwargs={
-                        "csv_fn": self.corpus_base_path / "propbank_comparison_data.csv",
-                    },
-                ),
-            ]
-            
-        return splits
-        
-    def _generate_examples(self, csv_fn):
-        df = pd.read_csv(csv_fn)
-        for counter, row in df.iterrows():
-            yield counter, {
-                "sentence": row['Input.sentence'],
-                "sent_id": row['Input.qasrl_id'],
-                "predicate_idx": row['Input.adj_index_start'],
-                "predicate_idx_end": row['Input.adj_index_end'],
-                "predicate": row['Input.target'],
-                "object_question": self._get_optional_question(row.object_q),
-                "object_answer": self._get_optional_answer(row["Answer.answer1"]),
-                "domain_question": self._get_optional_question(row.domain_q),
-                "domain_answer": self._get_optional_answer(row["Answer.answer3"]),
-                "reference_question": self._get_optional_question(row.comparison_q),
-                "reference_answer": self._get_optional_answer(row["Answer.answer2"]),
-                "extent_question": self._get_optional_question(row.degree_q),
-                "extent_answer":   self._get_optional_answer(row["Answer.answer4"]),
-            }
-
-    def _get_optional_answer(self, val):
-        if pd.isnull(val):   # no answer
-            return []
-        else:
-            return val.split("+")
-    def _get_optional_question(self, val):
-        if pd.isnull(val):   # no question
-            return ""
-        else:
-            return val
-