Convert dataset to Parquet

README.md

@@ -1,15 +1,14 @@
 ---
 annotations_creators:
 - crowdsourced
-language:
-- en
 language_creators:
 - found
+language:
+- en
 license:
 - other
 multilinguality:
 - monolingual
-pretty_name: 'CoQA: Conversational Question Answering Challenge'
 size_categories:
 - 1K<n<10K
 source_datasets:
@@ -22,6 +21,7 @@ task_categories:
 task_ids:
 - extractive-qa
 paperswithcode_id: coqa
+pretty_name: 'CoQA: Conversational Question Answering Challenge'
 tags:
 - conversational-qa
 dataset_info:
@@ -42,13 +42,20 @@ dataset_info:
       dtype: int32
   splits:
   - name: train
-    num_bytes: 17981459
+    num_bytes: 17953365
     num_examples: 7199
   - name: validation
-    num_bytes: 1225518
+    num_bytes: 1223427
     num_examples: 500
-  download_size: 58092681
-  dataset_size: 19206977
+  download_size: 12187487
+  dataset_size: 19176792
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+  - split: validation
+    path: data/validation-*
 ---
 
 # Dataset Card for "coqa"

coqa.py

@@ -1,91 +0,0 @@
-"""CoQA dataset."""
-
-
-import json
-
-import datasets
-
-
-_HOMEPAGE = "https://stanfordnlp.github.io/coqa/"
-
-_CITATION = """\
-@article{reddy-etal-2019-coqa,
-    title = "{C}o{QA}: A Conversational Question Answering Challenge",
-    author = "Reddy, Siva  and
-      Chen, Danqi  and
-      Manning, Christopher D.",
-    journal = "Transactions of the Association for Computational Linguistics",
-    volume = "7",
-    year = "2019",
-    address = "Cambridge, MA",
-    publisher = "MIT Press",
-    url = "https://aclanthology.org/Q19-1016",
-    doi = "10.1162/tacl_a_00266",
-    pages = "249--266",
-}
-"""
-
-_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):
-
-    VERSION = datasets.Version("1.0.0")
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "source": datasets.Value("string"),
-                    "story": datasets.Value("string"),
-                    "questions": datasets.features.Sequence(datasets.Value("string")),
-                    "answers": datasets.features.Sequence(
-                        {
-                            "input_text": datasets.Value("string"),
-                            "answer_start": datasets.Value("int32"),
-                            "answer_end": datasets.Value("int32"),
-                        }
-                    ),
-                }
-            ),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        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."""
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for row in data["data"]:
-                questions = [question["input_text"] for question in row["questions"]]
-                story = row["story"]
-                source = row["source"]
-                answers_start = [answer["span_start"] for answer in row["answers"]]
-                answers_end = [answer["span_end"] for answer in row["answers"]]
-                answers = [answer["input_text"] for answer in row["answers"]]
-                yield row["id"], {
-                    "source": source,
-                    "story": story,
-                    "questions": questions,
-                    "answers": {"input_text": answers, "answer_start": answers_start, "answer_end": answers_end},
-                }

data/train-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4272ee344ef27112c50237a1ae3b1c90e65e11a101d6874b2f57e4c08d147135
+size 11394343

data/validation-00000-of-00001.parquet

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d66f4811dd137818923a3db7a629ce3cf9e73977b5e2541471311c1566bf349f
+size 793144

dataset_infos.json

@@ -1 +0,0 @@
-{"default": {"description": "CoQA: A Conversational Question Answering Challenge\n", "citation": "@article{reddy-etal-2019-coqa,\n    title = \"{C}o{QA}: A Conversational Question Answering Challenge\",\n    author = \"Reddy, Siva  and\n      Chen, Danqi  and\n      Manning, Christopher D.\",\n    journal = \"Transactions of the Association for Computational Linguistics\",\n    volume = \"7\",\n    year = \"2019\",\n    address = \"Cambridge, MA\",\n    publisher = \"MIT Press\",\n    url = \"https://aclanthology.org/Q19-1016\",\n    doi = \"10.1162/tacl_a_00266\",\n    pages = \"249--266\",\n}\n", "homepage": "https://stanfordnlp.github.io/coqa/", "license": "", "features": {"source": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "questions": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "answers": {"feature": {"input_text": {"dtype": "string", "id": null, "_type": "Value"}, "answer_start": {"dtype": "int32", "id": null, "_type": "Value"}, "answer_end": {"dtype": "int32", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "coqa", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 17981459, "num_examples": 7199, "dataset_name": "coqa"}, "validation": {"name": "validation", "num_bytes": 1225518, "num_examples": 500, "dataset_name": "coqa"}}, "download_checksums": {"https://nlp.stanford.edu/data/coqa/coqa-train-v1.0.json": {"num_bytes": 49001836, "checksum": "b0fdb2bc1bd38dd3ca2ce5fa2ac3e02c6288ac914f241ac409a655ffb6619fa6"}, "https://nlp.stanford.edu/data/coqa/coqa-dev-v1.0.json": {"num_bytes": 9090845, "checksum": "dfa367a9733ce53222918d0231d9b3bedc2b8ee831a2845f62dfc70701f2540a"}}, "download_size": 58092681, "post_processing_size": null, "dataset_size": 19206977, "size_in_bytes": 77299658}}