qa_adj.py

qa_adj.py

@@ -0,0 +1,213 @@
+# 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
+