DiFrauD

gdds.py

@@ -1,6 +1,3 @@
-
-
-
 import csv
 import json
 import os
@@ -9,59 +6,57 @@ import datasets
 
 
 # TODO: Add BibTeX citation
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
+_CITATION = """
 TODO: Add citation here
 """
 
-# 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.
+_DESCRIPTION = """
+DIFrauD -- Domain Independent Fraud Detection dataset -- is a labeled corpus containing over 95000 samples
+of deceptive and truthful texts from 7 independent domains. 
 """
 
-# 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 = ""
+_HOMEPAGE = "http://cs.uh.edu/~rmverma/ra2.html"
 
+_LICENSE = """
+    Copyright 2023 University of Houston
+    
+    Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files
+    (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge,
+    publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so,
+    subject to the following conditions:
+    
+    The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+    
+    THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES
+    OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
+    LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR 
+    IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+    """
 
 
-# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
-class GDDS(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
+class DIFrauD(datasets.GeneratorBasedBuilder):
+    """Multi-Domain Deception -- a Large English Text Corpus"""
 
     VERSION = datasets.Version("2.1.0")
-
-    # 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="fake_news", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="job_scams", version=VERSION, description="This part of my dataset covers a second domain"),
-        datasets.BuilderConfig(name="phishing", version=VERSION, description="This part of my dataset covers a second domain"),
-        datasets.BuilderConfig(name="political_statements", version=VERSION, description="This part of my dataset covers a first domain"),
-        datasets.BuilderConfig(name="product_reviews", version=VERSION, description="This part of my dataset covers a second domain"),
-        datasets.BuilderConfig(name="sms", version=VERSION, description="This part of my dataset covers a second domain"),
-        datasets.BuilderConfig(name="twitter_rumours", version=VERSION, description="This part of my dataset covers a first domain"),
+        datasets.BuilderConfig(name="fake_news", version=VERSION, description="Fake News domain"),
+        datasets.BuilderConfig(name="job_scams", version=VERSION, description="Online Job Scams"),
+        datasets.BuilderConfig(name="phishing", version=VERSION, description="Email phishing attacks"),
+        datasets.BuilderConfig(name="political_statements", version=VERSION, description="Statements by various politicians"),
+        datasets.BuilderConfig(name="product_reviews", version=VERSION, description="Amazon product reviews"),
+        datasets.BuilderConfig(name="sms", version=VERSION, description="SMS spam and phishing attacks"),
+        datasets.BuilderConfig(name="twitter_rumours", version=VERSION, 
+                               description="Collection of rumours from twitter spanning several years and topics"),
     ]
 
     def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
+        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
         features = datasets.Features(
                 {
                     "text": datasets.Value("string"),
                     "label": datasets.Value("string"),
-                    # These are the features of your dataset like images, labels ...
+                    # These are the features of your dataset ...
                 }
             )
         return datasets.DatasetInfo(
@@ -71,7 +66,7 @@ class GDDS(datasets.GeneratorBasedBuilder):
             features=features,  # Here we define them above because they are different between the two configurations
             # 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"),
+            supervised_keys=("text", "label"),
             # Homepage of the dataset for documentation
             homepage=_HOMEPAGE,
             # License for the dataset if available
@@ -105,7 +100,6 @@ class GDDS(datasets.GeneratorBasedBuilder):
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(data_dir['validation']),
                     "split": "validation",
@@ -113,7 +107,6 @@ class GDDS(datasets.GeneratorBasedBuilder):
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
-                                # These kwargs will be passed to _generate_examples
                 gen_kwargs={
                     "filepath": os.path.join(data_dir['test']),
                     "split": "test"
@@ -123,7 +116,7 @@ class GDDS(datasets.GeneratorBasedBuilder):
 
     # 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.
+        # 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):