Upload NCR.py

NCR.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+
+import datasets
+import json
+
+_CITATION = """
+
+"""
+
+_DESCRIPTION = """
+"""
+
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+_URLS = {
+    "train": "https://huggingface.co/datasets/wics/NCR/resolve/main/train_2.json",
+    "validation": "https://huggingface.co/datasets/wics/NCR/resolve/main/dev_2.json",
+    "test": "https://huggingface.co/datasets/wics/NCR/resolve/main/test_2.json",
+}
+
+
+class NCR(datasets.GeneratorBasedBuilder):
+
+
+    VERSION = datasets.Version("0.0.1")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="NCR", version=VERSION, description="Chinese dataset."
+        ),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "example_id": datasets.Value("string"),
+                "article": datasets.Value("string"),
+                "answer": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "options": datasets.features.Sequence(datasets.Value("string"))
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = {
+            "train": _URLS["train"],
+            "test": _URLS["test"],
+            "validation": _URLS["validation"],
+        }
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["train"],
+                    "split": "train",
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "filepath": data_dir["validation"],
+                    "split": "validation",
+                },
+            ),
+        ]
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, train_test_or_eval, files):
+        """Yields examples."""
+        for file_idx, (path, f) in enumerate(files):
+            if path.startswith(train_test_or_eval) and path.endswith(".txt"):
+                data = json.loads(f.read().decode("utf-8"))
+                questions = data["Questions"]
+                for i in range(len(questions)):
+                    question = questions[i]
+
+                    yield f"{file_idx}_{i}", {
+                        "example_id": data["Id"],
+                        "article": data["Content"],
+                        "question": question["Question"],
+                        "answer": question["Answer"],
+                        "options": question["Choices"],
+                    }
+