Create coqa_expanded.py

coqa_expanded.py

@@ -0,0 +1,98 @@
+"""TODO(coqa): Add a description here."""
+
+
+import json
+
+import datasets
+
+
+# TODO(coqa): BibTeX citation
+_CITATION = """\
+@InProceedings{SivaAndAl:Coca,
+       author = {Siva, Reddy and Danqi, Chen and  Christopher D., Manning},
+        title = {WikiQA: A Challenge Dataset for Open-Domain Question Answering},
+      journal = { arXiv},
+         year = {2018},
+
+}
+"""
+
+# TODO(coqa):
+_DESCRIPTION = """\
+CoQA: A Conversational Question Answering Challenge
+"""
+
+_TRAIN_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json"
+_DEV_DATA_URL = "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json"
+
+
+class Coqa(datasets.GeneratorBasedBuilder):
+    """TODO(coqa): Short description of my dataset."""
+
+    # TODO(coqa): Set up version.
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        # TODO(coqa): Specifies the datasets.DatasetInfo object
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # datasets.features.FeatureConnectors
+            features=datasets.Features(
+                {
+                    "source": datasets.Value("string"),
+                    "story": datasets.Value("string"),
+                    "question": datasets.Value("string"),
+                    "answer": 
+                        {
+                            "input_text": datasets.Value("string"),
+                            "answer_start": datasets.Value("int32"),
+                            "answer_end": datasets.Value("int32"),
+                        }
+                    ,
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://stanfordnlp.github.io/coqa/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        # TODO(coqa): Downloads the data and defines the splits
+        # dl_manager is a datasets.download.DownloadManager that can be used to
+        # download and extract URLs
+        urls_to_download = {"train": _TRAIN_DATA_URL, "dev": _DEV_DATA_URL}
+        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"}
+            ),
+        ]
+
+    def _generate_examples(self, filepath, split):
+        """Yields examples."""
+        # TODO(coqa): Yields (key, example) tuples from the dataset
+        _id = 0
+        with open(filepath, encoding="utf-8") as f:
+            data = json.load(f)
+            for row in data["data"]:
+                story = row["story"]
+                source = row["source"]
+                for i,answer in enumerate(row['answers']):
+                  question = row["questions"][i]["input_text"]
+                  yield _id, {
+                      "source": source,
+                      "story": story,
+                      "question": question ,
+                      "answer": {"input_text": answer["input_text"], "answer_start": answer["span_start"], "answer_end": answer["span_end"]},
+                  }
+                  _id += 1