regenerated readme

.gitignore

@@ -0,0 +1 @@
+cache_policies

README.md

@@ -29,9 +29,12 @@ dataset_info:
   config_name: plain_text
   splits:
   - name: train
-    num_bytes: 1305279
-    num_examples: 13480
-  download_size: 0
+    num_bytes: 1174320
+    num_examples: 12132
+  - name: test
+    num_bytes: 130959
+    num_examples: 1348
+  download_size: 3325946
   dataset_size: 1305279
 ---
 

create_policies_data.py

@@ -0,0 +1,28 @@
+from datasets import load_dataset, DatasetDict
+dataset_config = {
+    "LOADING_SCRIPT_FILES": "policies.py",
+    "CONFIG_NAME": "plain_text",
+    "DATA_DIR": "data",
+    "CACHE_DIR": "cache_policies",
+}
+
+ds = load_dataset(
+    dataset_config["LOADING_SCRIPT_FILES"],
+    dataset_config["CONFIG_NAME"],
+    data_dir=dataset_config["DATA_DIR"],
+    cache_dir=dataset_config["CACHE_DIR"]
+)
+
+# 90% train, 10% test + validation
+# Split the 10% test + valid in half test, half valid
+train_testvalid = ds["train"].train_test_split(shuffle=True, test_size=0.1)
+test_valid = train_testvalid["test"].train_test_split(
+    test_size=0.5)  # gather everything into a single DatasetDict
+ds = DatasetDict({
+    "train": train_testvalid["train"],
+    "test": test_valid["test"],
+    "val": test_valid["train"],
+}
+)
+
+print(ds)

data_preparation.py

@@ -37,7 +37,7 @@ if __name__ == "__main__":
     train, test = split_data("policies-qa-training.json", "json")
 
     # save the train split
-    save_json(train, "train.json")
+    save_json({"data": train}, "train.json")
 
     # save the test split
-    save_json(test, "test.json")
+    save_json({"data": test}, "test.json")

dataset_info.json

@@ -1 +0,0 @@
-{"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

@@ -1,4 +1,5 @@
 import json
+import os
 
 import datasets
 from datasets.tasks import QuestionAnsweringExtractive
@@ -13,9 +14,9 @@ Manually generated dataset for policies qa
 """
 
 _URLS = {
-                "train": "./data/train.json",
-                "test": "./data/test.json"
-        }
+    "train": "./data/train.json",
+    "test": "./data/test.json"
+}
 
 
 class PoliciesQAConfig(datasets.BuilderConfig):
@@ -74,10 +75,13 @@ class PoliciesQA(datasets.GeneratorBasedBuilder):
 
         return [
             datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={
-                                    "filepath": downloaded_files["train"]})
+                                    "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):
+    def _generate_examples(self, filepath, split):
+        print(os.fspath(filepath))
         """This function returns the examples in the raw (text) form."""
         logger.info("generating examples from = %s", filepath)
         key = 0
@@ -90,8 +94,6 @@ class PoliciesQA(datasets.GeneratorBasedBuilder):
                                  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,