Update parquet files

.gitignore

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

README.md

@@ -1,26 +0,0 @@
----
-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: 1846196
-    num_examples: 4500
-  - name: test
-    num_bytes: 201754
-    num_examples: 501
-  download_size: 2875518
-  dataset_size: 2047950
----

data_preparation.py

@@ -1,43 +0,0 @@
-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")

data/test.json → plain_text/policies-test.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6fd3c19c7a55f1ca28f20dec5eb3c9435df4e6bc96182b9fbaca9c8608446dfa
-size 284488
+oid sha256:0ba36886ba51f82d8865ec4316114e59bfb05f27965140d44616450869c3222e
+size 132260

data/train.json → plain_text/policies-train.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f1c86f7ad020320f143cd944214b72a59e8b88dacb73525e1c5c974fbdf89094
-size 2591030
+oid sha256:13af37b7cf33e17f7f179628dd705d3e88c81bb17ff0f3f51ffb8c459c5b6027
+size 1177092

policies.py

@@ -1,103 +0,0 @@
-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