Add dataloader

completeformer.py

@@ -0,0 +1,163 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the Semeru Lab and SEART research group.
+#
+# 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.
+"""TODO: Add a description here."""
+
+
+import csv
+import glob
+import os
+
+import datasets
+
+import numpy as np
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@InProceedings{huggingface:dataset,
+    title = {A great new dataset},
+    author={huggingface, Inc.
+    },
+    year={2020}
+}
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = ""
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace dataset library don't host the datasets but only point to the original files
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_DATA_URLs = {
+    "long": {
+        "train": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/long/training_long.csv",
+        "valid": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/long/validation_long.csv",
+        "test": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/long/test_long.csv",
+    },
+    "medium": {
+        "train": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/medium/training_medium.csv",
+        "valid": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/medium/validation_medium.csv",
+        "test": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/medium/test_medium.csv",
+    },
+    "short": {
+        "train": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/short/training_short.csv",
+        "valid": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/short/validation_short.csv",
+        "test": "https://huggingface.co/datasets/semeru/completeformer_java_data/resolve/main/short/test_short.csv",
+    },
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class CSNCHumanJudgementDataset(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="long",
+            version=VERSION,
+            description="",
+        ),
+        datasets.BuilderConfig(
+            name="medium",
+            version=VERSION,
+            description="",
+        ),
+        datasets.BuilderConfig(
+            name="short",
+            version=VERSION,
+            description="",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "long"
+
+    def _info(self):
+        features = datasets.Features(
+            { 
+                "idx": datasets.Value("int32"),
+                "input": datasets.Value("string"),
+                "target": datasets.Value("string"),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        my_urls = _DATA_URLs[self.config.name]
+        data_dirs = {}
+        for k, v in my_urls.items():
+            data_dirs[k] = dl_manager.download_and_extract(v)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": data_dirs["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": data_dirs["valid"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={
+                    "file_path": data_dirs["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(
+        self,
+        file_path,
+    ):
+        """Yields examples as (key, example) tuples."""
+        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is here for legacy reason (tfds) and is not important in itself.
+
+        with open(file_path, encoding="utf-8") as f:
+            csv_reader = csv.reader(f, quotechar='"', delimiter=",", quoting=csv.QUOTE_ALL, skipinitialspace=True)
+            next(csv_reader, None) # skip header
+
+            for row_id, row in enumerate(csv_reader):
+                _, idx, input, target = row
+                yield row_id, {
+                    "idx": idx,
+                    "input": input,
+                    "target": target,
+                }