dataset script

NLP4SGPapers.py

@@ -0,0 +1,150 @@
+# 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.
+# TODO: Address all TODOs and remove all explanatory comments
+"""NLP4SGPAPERS dataset: a scientific dataset with three associated tasks that can help identify NLP4SG papers"""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+_CITATION = """
+"""
+
+# TODO: Add description of the dataset here
+# You can copy an official description
+_DESCRIPTION = """\
+NLP4SGPAPERS dataset: a scientific dataset with three associated tasks that can help identify NLP4SG papers
+"""
+
+
+_HOMEPAGE = ""
+
+
+_LICENSE = ""
+
+# TODO: Add link to the official dataset URLs here
+# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
+# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
+_URLS = {
+    "train": "https://huggingface.co/datasets/feradauto/nlp4sgpapers/blob/main/train_set_final.csv",
+    "test": "https://huggingface.co/datasets/feradauto/nlp4sgpapers/blob/main/test_set_final.csv",
+    "dev": "https://huggingface.co/datasets/feradauto/nlp4sgpapers/blob/main/dev_set_final.csv",
+}
+
+
+    class NLP4SGPapers(datasets.GeneratorBasedBuilder):
+    """A scientific dataset with three associated tasks that can help identify NLP4SG papers"""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # data = datasets.load_dataset('my_dataset', 'first_domain')
+    # data = datasets.load_dataset('my_dataset', 'second_domain')
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="data", version=VERSION, description="data")
+
+    DEFAULT_CONFIG_NAME = "data"  # It's not mandatory to have a default configuration. Just use one if it make sense.
+
+    def _info(self):
+        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        features = datasets.Features(
+                {
+                    "ID": datasets.Value("string"),
+                    "url": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string")
+                    "label_nlp4sg": datasets.Value("bool")
+                    "task": datasets.Sequence(feature=datasets.Value("string"))
+                    "method": datasets.Sequence(feature=datasets.Value("string"))
+                    "goal1": datasets.Value("string")
+                    "goal2": datasets.Value("string")
+                    "goal3": datasets.Value("string")
+                    "acknowledgments": datasets.Value("string")
+                    "year": datasets.Value("int32")
+                    "test": ClassLabel(num_classes=3, names=['0','1','2'])
+                    "sdg1": datasets.Value("bool")
+                    "sdg2": datasets.Value("bool")
+                    "sdg3": datasets.Value("bool")
+                    "sdg4": datasets.Value("bool")
+                    "sdg5": datasets.Value("bool")
+                    "sdg6": datasets.Value("bool")
+                    "sdg7": datasets.Value("bool")
+                    "sdg8": datasets.Value("bool")
+                    "sdg9": datasets.Value("bool")
+                    "sdg10": datasets.Value("bool")
+                    "sdg11": datasets.Value("bool")
+                    "sdg12": datasets.Value("bool")
+                    "sdg13": datasets.Value("bool")
+                    "sdg14": datasets.Value("bool")
+                    "sdg15": datasets.Value("bool")
+                    "sdg16": datasets.Value("bool")
+                    "sdg17": datasets.Value("bool")
+                    # These are the features of your dataset like images, labels ...
+                }
+            )
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
+        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        downloaded_files = dl_manager.download_and_extract(_URLS)
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
+            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
+        ]
+        
+
+    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
+    def _generate_examples(self, filepath, split):
+        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
+        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
+        with open(filepath, encoding="utf-8") as f:
+            for key, row in enumerate(f):
+                data = json.loads(row)
+                yield key, {
+                    "ID": data["ID"],
+                    "url": data["url"],
+                    "title": data["title"],
+                    "abstract": data["abstract"]
+                }