add script

feedbackQA.py

@@ -0,0 +1,128 @@
+# 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
+"""FeedbackQA: An Retrieval-based Question Answering Dataset with User Feedback"""
+
+
+import json
+
+import datasets
+import os
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+"""
+
+_DESCRIPTION = """\
+FeedbackQA is a retrieval-based QA dataset \
+that contains interactive feedback from users. \
+It has two parts: the first part contains a conventional RQA dataset, \
+whilst this repo contains the second part, which contains feedback(ratings and natural language explanations) for QA pairs.
+"""
+
+_URL = "https://drive.google.com/drive/folders/1mIcxZZ643k6SVJnZw1FmEOhndaFx4_PG?usp=sharing"
+#_URLS = {
+#    "train": _URL + "train-v1.1.json",
+#    "dev": _URL + "dev-v1.1.json",
+#}
+
+
+class FeedbackConfig(datasets.BuilderConfig):
+    """BuilderConfig for FeedbackQA."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for FeedbackQA.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FeedbackConfig, self).__init__(**kwargs)
+
+
+class FeedbackQA(datasets.GeneratorBasedBuilder):
+    """FeedbackQA: retrieval-based QA dataset that contains interactive feedback from users."""
+
+    BUILDER_CONFIGS = [
+        FeedbackConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    #"id": datasets.Value("string"),
+                    #"title": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": datasets.Value("string"),
+                    "feedback": datasets.features.Sequence(
+                        {
+                            "rating": datasets.Value("string"),
+                            "explanation": datasets.Value("string"),
+                        }
+                    ),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://mcgill-nlp.github.io/feedbackQA_data/",
+            citation=_CITATION
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files_path = dl_manager.download_and_extract(_URL)
+        train_file = os.path.join(downloaded_files_path, 'feedback_train.json')
+        valid_file = os.path.join(downloaded_files_path, 'feedback_valid.json')
+        test_file = os.path.join(downloaded_files_path, 'feedback_test.json')
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_file}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_file}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test_file}),
+        ]
+
+    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:
+            fbqa = json.load(f)
+            for dict_item in fbqa:
+                question = dict_item['question']
+                passage_text = ''
+                if dict_item['passage']['reference']['page_title']:
+                    passage_text += dict_item['passage']['reference']['page_title'] + '\n'
+                if dict_item['passage']['reference']['section_headers']:
+                    passage_text += '\n'.join(dict_item['passage']['reference']['section_headers']) + '\n'
+                if dict_item['passage']['reference']['section_content']:
+                    passage_text += dict_item['passage']['reference']['section_content']
+
+                yield key, {
+                            "question": question,
+                            "answer": passage_text,
+                            "feedback": {
+                                "rating": dict_item['rating'],
+                                "explanation": dict_item['feedback'],
+                            },
+                        }
+                key += 1