init

.gitattributes

@@ -52,3 +52,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+data/train.json filter=lfs diff=lfs merge=lfs -text
+data/test.json filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,2 @@
+cache_policies
+.DS_Store

README.md

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+---
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - 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: 3245009
+    num_examples: 7632
+  - name: test
+    num_bytes: 359230
+    num_examples: 849
+  download_size: 5007313
+  dataset_size: 3604239
+---

data/test.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b7d46b01cac93093bef2bfdb82be726abe0a636842823f5a3fa8dab50380f40
+size 499720

data/train.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:001aa8ee8b183dc4b333dcdd8a8113e36aa45a0c53b4b061d5361f03269b3362
+size 4507593

data_preparation.py

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+import json
+from sklearn.model_selection import train_test_split
+
+
+def split_data(file_name: str, data_type: str):
+    if data_type == "json":
+        with open(file_name, 'r') as json_file:
+            data = json.load(json_file)["data"]
+            json_file.close()
+
+    train, test = train_test_split(data, train_size=0.9, random_state=42)
+    return(train, test)
+
+
+def save_json(data: dict, file_name: str):
+    """
+    Method to save the json file.
+    Parameters:
+    ----------
+    data: dict,
+        data to be saved in file.
+    file_name: str,
+        name of the file.
+    Returns:
+    --------
+    None
+    """
+
+    # save the split
+    with open(file_name, "w") as data_file:
+        json.dump(data, data_file, indent=2)
+        data_file.close()
+
+
+if __name__ == "__main__":
+    # split the train data
+    train, test = split_data("train.json", "json")
+
+    # save the train split
+    save_json({"data": train}, "data/train.json")
+
+    # save the test split
+    save_json({"data": test}, "data/test.json")

policies.py

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+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+
+
+_CITATION = """"""
+
+_DESCRIPTION = """\
+Manually generated dataset for policies qa
+"""
+
+_URLS = {
+    "train": "./data/train.json",
+    "test": "./data/test.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(
+                {
+                    "id": datasets.Value("string"),
+                    "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"
+                )
+            ],
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
+                                    "filepath": downloaded_files["train"], "split": "train"}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={
+                                    "filepath": downloaded_files["test"], "split": "test"})
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """This function returns the examples in the raw (text) form."""
+        key = 0
+        with open(filepath, encoding="utf-8") as f:
+            policies = json.load(f)
+            for policy in policies["data"]:
+                id = policy["id"]
+                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"]]
+                yield key, {
+                    "id": id,
+                    "context": context,
+                    "question": question,
+                    "answers": {
+                        "answer_start": answer_starts,
+                        "text": answers,
+                    },
+                }
+                key += 1