Create new file

unpredictable_support-google-com.py

@@ -0,0 +1,85 @@
+# 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.
+"""This loads the UnpredicTable-support-google-com dataset."""
+
+import json
+import os
+import pandas as pd
+
+import datasets
+
+
+_DESCRIPTION = """\
+The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
+"""
+
+_LICENSE = "Apache 2.0"
+
+_URL = "https://huggingface.co/datasets/unpredictable/unpredictable_support-google-com/resolve/main/unpredictable_support-google-com.jsonl"
+
+logger = datasets.logging.get_logger(__name__)
+
+
+class UnpredicTable(datasets.GeneratorBasedBuilder):
+    """
+    The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "task": datasets.Value("string"),
+                "input": datasets.Value("string"),
+                "output": datasets.Value("string"),
+                "options": datasets.Sequence([datasets.Value("string")]),
+                "pageTitle": datasets.Value("string"),
+                "outputColName": datasets.Value("string"),
+                "url": datasets.Value("string"),
+                "wdcFile": datasets.Value("string")
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            license=_LICENSE,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        data_dir = dl_manager.download_and_extract(_URL)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": data_dir},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """Yields examples."""
+        with open(filepath, encoding="utf-8") as f:
+            for i, row in enumerate(f):
+                data = json.loads(row)
+                key = f"{data['task']}_{i}"
+                yield key, {
+                    "task": data["task"],
+                    "input": data["input"],
+                    "output": data["output"],
+                    "options": data["options"],
+                    "pageTitle": data["pageTitle"],
+                    "outputColName": data["outputColName"],
+                    "url": data["url"],
+                    "wdcFile": data["wdcFile"],
+                }