Added data preparation script and updated data loader script

data_preparation.py

@@ -0,0 +1,112 @@
+""" Script to prepare the SQuAD2.0 data to the GEM format
+
+    @author: AbinayaM02
+"""
+
+# Import libraries
+import json
+import pandas as pd
+from sklearn.model_selection import train_test_split
+
+# Function to generate gem id
+def add_gem_id(data: dict, split: str) -> dict:
+    """ 
+    Add gem id for each of the datapoint in the dataset.
+
+    Parameters:
+    -----------
+    data: dict,
+        data.
+    split: str,
+        split of data (train, test or validation).
+
+    Returns:
+    --------
+    dict
+        dictionary with updated id
+    """
+    gem_id = -1
+    generated_data = {"data": []}
+    for example in data:
+        temp_dict = {}
+        title = example["title"]
+        for paragraph in example["paragraphs"]:
+            context = paragraph["context"]  # do not strip leading blank spaces GH-2585
+            for qa in paragraph["qas"]:
+                question = qa["question"]
+                qa_id = qa["id"]
+                answer_starts = [answer["answer_start"] for answer in qa["answers"]]
+                answers = [answer["text"] for answer in qa["answers"]]
+                # Features currently used are "context", "question", and "answers".
+                # Others are extracted here for the ease of future expansions.
+                gem_id += 1
+                temp_dict["id"] = qa_id
+                temp_dict["gem_id"] = f"gem-squad_v2-{split}-{gem_id}"
+                temp_dict["title"] = title
+                temp_dict["context"] = context
+                temp_dict["question"] = question
+                temp_dict["answers"] = {
+                        "answer_start": answer_starts,
+                        "text": answers,
+                    }
+                generated_data["data"].append(temp_dict)
+    return generated_data
+
+
+# Function to split data
+def split_data(file_name: str, data_type: str) -> (dict, dict):
+    """
+    Method to split the data specific to SQuAD2.0
+
+    Parameters:
+    -----------
+    file_name: str,
+        name of the file.
+    data_type: str,
+        type of the data file.
+
+    Returns:
+    --------
+    (dict, dict)
+        split of data
+    """
+    
+    if data_type == "json":
+        with open(file_name, 'r') as json_file:
+            data = json.load(json_file)["data"]
+            json_file.close()
+    
+    # split the data into train and test
+    train, test = train_test_split(data, train_size=0.7, random_state = 42)
+    return(train, test)
+
+
+if __name__ == "__main__":
+    # split the train data
+    train, test = split_data("squad_data/train-v2.0.json", "json")
+    
+    # add gem id and save the files
+    train = add_gem_id(train, "train")
+    test = add_gem_id(test, "test")
+    
+    # save the train split
+    with open("train.json", "w") as train_file:
+        json.dump(train, train_file, indent = 2)
+        train_file.close()
+
+    # save the test split
+    with open("test.json", "w") as test_file:
+        json.dump(test, test_file, indent = 2)
+        test_file.close()
+    
+    # load validation data
+    with open("squad_data/dev-v2.0.json", "r") as dev_file:
+        validation = json.load(dev_file)["data"]
+        dev_file.close()
+    
+    # add gem id and save valid.json
+    validation = add_gem_id(validation, "validation")
+    with open("valid.json", "w") as val_file:
+        json.dump(validation, val_file, indent = 2)
+        val_file.close()
+

squad_v2.py

@@ -23,15 +23,15 @@ archivePrefix = {arXiv},
 """
 
 _DESCRIPTION = """\
-combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
+ SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers
  to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but
  also determine when no answer is supported by the paragraph and abstain from answering.
 """
 
-_URL = "https://rajpurkar.github.io/SQuAD-explorer/dataset/"
 _URLS = {
-    "train": _URL + "train-v2.0.json",
-    "dev": _URL + "dev-v2.0.json",
+    "train": "train.json",
+    "test": "test.json",
+    "valid": "validation.json",
 }
 
 
@@ -64,6 +64,7 @@ class SquadV2(datasets.GeneratorBasedBuilder):
             features=datasets.Features(
                 {
                     "gem_id": datasets.Value("string"),
+                    "id": datasets.Value("string"),
                     "title": datasets.Value("string"),
                     "context": datasets.Value("string"),
                     "question": datasets.Value("string"),
@@ -99,35 +100,42 @@ class SquadV2(datasets.GeneratorBasedBuilder):
         downloaded_files = dl_manager.download_and_extract(urls_to_download)
 
         return [
-                datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"], "split": "train"}),
-                datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"], "split": "validation"}),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "filepath": downloaded_files["train"],
+                        "split": "train",
+                        },
+                    ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.VALIDATION,
+                    gen_kwargs={
+                        "filepath": downloaded_files["validation"],
+                        "split": "validation",
+                        },
+                    ),
+                datasets.SplitGenerator(
+                    name=datasets.Split.TEST,
+                    gen_kwargs={
+                        "filepath": downloaded_files["test"],
+                        "split": "test",
+                        },
+                    ),
         ]
 
     def _generate_examples(self, filepath, split):
         """Yields examples."""
         # TODO(squad_v2): Yields (key, example) tuples from the dataset
         with open(filepath, encoding="utf-8") as f:
-            squad = json.load(f)
-            for example in squad["data"]:
-                title = example.get("title", "")
-                for paragraph in example["paragraphs"]:
-                    context = paragraph["context"]  # do not strip leading blank spaces GH-2585
-                    for qa in paragraph["qas"]:
-                        question = qa["question"]
-                        id_ = qa["id"]
-
-                        answer_starts = [answer["answer_start"] for answer in qa["answers"]]
-                        answers = [answer["text"] for answer in qa["answers"]]
-
-                        # Features currently used are "context", "question", and "answers".
-                        # Others are extracted here for the ease of future expansions.
-                        yield id_, {
-                            "title": title,
-                            "context": context,
-                            "question": question,
-                            "gem_id": f"gem-{squad_v2}-{split}-{id_}",
-                            "answers": {
-                                "answer_start": answer_starts,
-                                "text": answers,
-                            },
-                        }
+            for id_, row  in enumerate(f):
+                data = json.loads(row)
+                # Features currently used are "context", "question", and "answers".
+                # Others are extracted here for the ease of future expansions.
+                yield id_, {
+                    "id": data["id"], 
+                    "gem_id": data["gem_id"],
+                    "title": data["title"],
+                    "context": data["context"],
+                    "question": data["question"],
+                    "answers": data["answers"],
+                }