Create phrase_sense_disambiguation.py

phrase_sense_disambiguation.py

@@ -0,0 +1,141 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search."""
+
+
+import json
+import os.path
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+
+"""
+
+_DESCRIPTION = """\
+
+"""
+
+_HOMEPAGE = ""
+
+_LICENSE = "CC-BY-4.0"
+
+_URL = "https://auburn.edu/~tmp0038/PiC/"
+_SPLITS = {
+    "train": "train-v1.0.json",
+    "dev": "dev-v1.0.json",
+    "test": "test-v1.0.json",
+}
+
+_PSD = "PSD"
+
+
+class PSDConfig(datasets.BuilderConfig):
+    """BuilderConfig for Phrase Sense Disambiguation in PiC."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Phrase Sense Disambiguation in PiC.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PSDConfig, self).__init__(**kwargs)
+
+
+class PhraseSenseDisambiguation(datasets.GeneratorBasedBuilder):
+    """Phrase Sense Disambiguation in PiC dataset. Version 1.0."""
+
+    BUILDER_CONFIGS = [
+        PSDConfig(
+            name=_PSD,
+            version=datasets.Version("1.0.0"),
+            description="The PiC Dataset for Phrase Sense Disambiguation at short passage level (~22 sentences)"
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.Sequence(
+                        {
+                            "text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question and context as input).
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        urls_to_download = {
+            "train": os.path.join(_URL, self.config.name, _SPLITS["train"]),
+            "dev": os.path.join(_URL, self.config.name, _SPLITS["dev"]),
+            "test": os.path.join(_URL, self.config.name, _SPLITS["test"])
+        }
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        logger.info("generating examples from = %s", filepath)
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            pic_pr = json.load(f)
+            for example in pic_pr["data"]:
+                title = example.get("title", "")
+
+                answer_starts = [answer["answer_start"] for answer in example["answers"]]
+                answers = [answer["text"] for answer in example["answers"]]
+
+                # Features currently used are "context", "question", and "answers".
+                # Others are extracted here for the ease of future expansions.
+                yield key, {
+                    "title": title,
+                    "context": example["context"],
+                    "question": example["question"],
+                    "id": example["id"],
+                    "answers": {
+                        "answer_start": answer_starts,
+                        "text": answers,
+                    },
+                }
+                key += 1