initial commit

invoker/data.jsonl

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+{"quas":"quas", "wex": "wex", "exort": "exort", "spell": "defeaning blast"}
+{"quas":"quas quas quas", "wex": "", "exort": "", "spell": "cold snap"}
+{"quas":"quas", "wex": "wex wex", "exort": "", "spell": "tornado"}

juggernaut/data.jsonl

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+{"context":"Looks like it's just you", "answer": "and me."}
+{"context":"There's a fine line between bravery", "answer": "and stupidity."}
+{"context":"If only I could scratch", "answer": "my nose..."}

test_dataset.py

@@ -0,0 +1,98 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# 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.
+
+# Lint as: python3
+"""we're testin'"""
+
+import datasets
+import json
+
+class TestDatasetConfig(datasets.BuilderConfig):
+    """BuilderConfig for Test Dataset for testing HF parsing"""
+
+    def __init__(
+        self,
+        text_features,
+        hero = "juggernaut",
+        foo = "foo",
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+        """BuilderConfig for TestDatset.
+
+        Args:
+          text_features: `dict[string, string]`, map from the name of the feature
+            dict for each text field to the name of the column in the tsv file
+          label_column: `string`, name of the column in the tsv file corresponding
+            to the label
+          data_dir: `string`, the path to the folder containing the tsv files in the
+            downloaded zip
+          label_classes: `list[string]`, the list of classes if the label is
+            categorical. If not provided, then the label will be of type
+            `datasets.Value('float32')`.
+          process_label: `Function[string, any]`, function  taking in the raw value
+            of the label and processing it to the form required by the label feature
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TestDatasetConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
+        self.text_features = text_features
+        self.hero = hero
+        self.foo = foo
+        self.process_label = process_label
+
+
+class TestDatasetEvals(datasets.GeneratorBasedBuilder):
+    """The General Language Understanding Evaluation (GLUE) benchmark."""
+
+    BUILDER_CONFIGS = [
+        TestDatasetConfig(
+            name="name_for_test_dataset",
+            description= "this is a test dataset for our unit test intergrating HF datasets" ,
+            text_features={"context": "context", "answer": "answer"},
+            data_dir="heroes",
+        ),
+    ]
+
+    def _info(self):
+        features = {text_feature: datasets.Value("string") for text_feature in self.config.text_features.keys()}
+        features["idx"] = datasets.Value("int32")
+        return datasets.DatasetInfo(
+            description=self.config.description,
+            features=datasets.Features(features),
+        )
+
+    def _split_generators(self, dl_manager):
+        constructed_filepath = self.construct_filepath()
+        data_file = dl_manager.download(constructed_filepath)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": data_file, 
+                    # "split": "test",
+                },
+            ),
+        ]
+
+    def construct_filepath(self):
+        return self.hero + '/' + self.config.data_dir + '/data.jsonl'
+
+    def _generate_examples(self, data_file):
+        with open(data_file, encoding="utf8") as f:
+            for n, row in enumerate(f):
+                data = json.loads(row)
+                example = {feat: data[col] for feat, col in self.config.text_features.items()}
+                example["idx"] = n
+                yield example["idx"], example