init

README.md

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+---
+annotations_creators: []
+language:
+- en
+language_creators: []
+license: []
+multilinguality:
+- monolingual
+pretty_name: PoliciesQA
+size_categories: []
+source_datasets: []
+tags: []
+task_categories:
+- question-answering
+task_ids:
+- extractive-qa
+dataset_info:
+  features:
+  - name: context
+    dtype: string
+  - name: question
+    dtype: string
+  - name: answers
+    sequence:
+    - name: text
+      dtype: string
+    - name: answer_start
+      dtype: int32
+  config_name: plain_text
+  splits:
+  - name: train
+    num_bytes: 1305279
+    num_examples: 13480
+  download_size: 0
+  dataset_size: 1305279
+---
+
+# Dataset Card for [Dataset Name]
+
+## Table of Contents
+- [Dataset Card for \[Dataset Name\]](#dataset-card-for-dataset-name)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
+      - [Who are the source language producers?](#who-are-the-source-language-producers)
+    - [Annotations](#annotations)
+      - [Annotation process](#annotation-process)
+      - [Who are the annotators?](#who-are-the-annotators)
+    - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Dataset Curators](#dataset-curators)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:**
+- **Repository:**
+- **Paper:**
+- **Leaderboard:**
+- **Point of Contact:**
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]

dataset_info.json

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+{"description": "Manually generated dataset for policies qa\n", "citation": "", "homepage": "ineract.com", "license": "", "features": {"context": {"dtype": "string", "_type": "Value"}, "question": {"dtype": "string", "_type": "Value"}, "answers": {"feature": {"text": {"dtype": "string", "_type": "Value"}, "answer_start": {"dtype": "int32", "_type": "Value"}}, "_type": "Sequence"}}, "task_templates": [{"task": "question-answering-extractive"}], "builder_name": "policies-qa", "config_name": "plain_text", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1305279, "num_examples": 13480, "dataset_name": "policies-qa"}}, "download_checksums": {}, "download_size": 0, "dataset_size": 1305279, "size_in_bytes": 1305279}

policies.py

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+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_DESCRIPTION = """\
+Manually generated dataset for policies qa
+"""
+
+_URLS = {
+    "train": "policies-qa-training.json"
+}
+
+
+class PoliciesQAConfig(datasets.BuilderConfig):
+    """BuilderConfig for Ineract Policies."""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for Ineract Policies.    
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(PoliciesQAConfig, self).__init__(**kwargs)
+
+
+class PoliciesQA(datasets.GeneratorBasedBuilder):
+    """Ineract Policies: The Policy Question Answering Dataset. Version 0.1"""
+
+    BUILDER_CONFIGS = [
+        PoliciesQAConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "plain_text"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "context": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answers": datasets.features.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="ineract.com",
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                                    "filepath": downloaded_files["train"]})
+        ]
+
+    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:
+            policies = json.load(f)
+            for policy in policies["data"]:
+                context = policy["context"]
+                question = policy["question"]
+                answer_starts = [answer_start
+                                 for answer_start in policy["answers"]["answer_start"]]
+                answers = [
+                    answer_text for answer_text in policy["answers"]["text"]]
+                # Features currently used are "context", "question", and "answers".
+                # Others are extracted here for the ease of future expansions.
+                yield key, {
+                    "context": context,
+                    "question": question,
+                    "answers": {
+                        "answer_start": answer_starts,
+                        "text": answers,
+                    },
+                }
+                key += 1