DiFrauD

README.md

@@ -9,75 +9,83 @@ multilinguality:
 - monolingual
 task_categories:
 - text-classification
-pretty_name: GDDs (Generalized Deception Dataset)
+- zero-shot-classification
+pretty_name: DIFrauD - Domain-Independent Fraud Detection benchmark
 tags:
+- fraud-detection
 - deception-detection
 - phishing
 - fake-news
+- benchmark
 - opinion-spam
-- domain-adaptation
+- multi-domain
 configs:
-  - config_name: fake_news
-    data_files:
-      - split: train
-        path: fake_news/train.jsonl
-      - split: test
-        path: fake_news/test.jsonl
-      - split: validation
-        path: fake_news/validation.jsonl
-  - config_name: job_scams
-    data_files:
-      - split: train
-        path: job_scams/train.jsonl
-      - split: test
-        path: job_scams/test.jsonl
-      - split: validation
-        path: job_scams/validation.jsonl
-  - config_name: phishing
-    data_files:
-      - split: train
-        path: phishing/train.jsonl
-      - split: test
-        path: phishing/test.jsonl
-      - split: validation
-        path: phishing/validation.jsonl
-  - config_name: political_statements
-    data_files:
-      - split: train
-        path: political_statements/train.jsonl
-      - split: test
-        path: political_statements/test.jsonl
-      - split: validation
-        path: political_statements/validation.jsonl
-  - config_name: product_reviews
-    data_files:
-      - split: train
-        path: product_reviews/train.jsonl
-      - split: test
-        path: product_reviews/test.jsonl
-      - split: validation
-        path: product_reviews/validation.jsonl
-  - config_name: sms
-    data_files:
-      - split: train
-        path: sms/train.jsonl
-      - split: test
-        path: sms/test.jsonl
-      - split: validation
-        path: sms/validation.jsonl
-  - config_name: twitter_rumours
-    data_files:
-      - split: train
-        path: twitter_rumours/train.jsonl
-      - split: test
-        path: twitter_rumours/test.jsonl
-      - split: validation
-        path: twitter_rumours/validation.jsonl
+- config_name: fake_news
+  data_files:
+  - split: train
+    path: fake_news/train.jsonl
+  - split: test
+    path: fake_news/test.jsonl
+  - split: validation
+    path: fake_news/validation.jsonl
+- config_name: job_scams
+  data_files:
+  - split: train
+    path: job_scams/train.jsonl
+  - split: test
+    path: job_scams/test.jsonl
+  - split: validation
+    path: job_scams/validation.jsonl
+- config_name: phishing
+  data_files:
+  - split: train
+    path: phishing/train.jsonl
+  - split: test
+    path: phishing/test.jsonl
+  - split: validation
+    path: phishing/validation.jsonl
+- config_name: political_statements
+  data_files:
+  - split: train
+    path: political_statements/train.jsonl
+  - split: test
+    path: political_statements/test.jsonl
+  - split: validation
+    path: political_statements/validation.jsonl
+- config_name: product_reviews
+  data_files:
+  - split: train
+    path: product_reviews/train.jsonl
+  - split: test
+    path: product_reviews/test.jsonl
+  - split: validation
+    path: product_reviews/validation.jsonl
+- config_name: sms
+  data_files:
+  - split: train
+    path: sms/train.jsonl
+  - split: test
+    path: sms/test.jsonl
+  - split: validation
+    path: sms/validation.jsonl
+- config_name: twitter_rumours
+  data_files:
+  - split: train
+    path: twitter_rumours/train.jsonl
+  - split: test
+    path: twitter_rumours/test.jsonl
+  - split: validation
+    path: twitter_rumours/validation.jsonl
 ---
 
-# GDDs-2.0
+# DIFrauD - Domain Independent Fraud Detection Benchmark
 
-The Generalized Deception Dataset version 2.0 is a labeled corpus containing over 95000 samples of deceptive and truthful texts from a number of independent domains and tasks. 
+Domain Independent Fraud Detection Benchmark is a labeled corpus containing over 95,854 samples of deceitful 
+and truthful texts from a number of independent domains and tasks. Deception, however, can be different --
+in this corpus we made sure to gather strictly real examples of deception that are intentionally malicious 
+and cause real harm, despite them often having very little in common. Covering seven domains, this benchmark 
+is designed to serve as a representative slice of the various security challenges that remain open problems
+today.
 
 ## Authors
 
@@ -163,10 +171,10 @@ Whitespace, quotes, bulletpoints, unicode is normalized.
 
 ### Layout
 
-The directory layout of gdds is like so:
+The directory layout of `difraud` is like so:
 
 ``
-gdds
+difraud
   fake_news/
     train.jsonl
     test.jsonl
@@ -319,9 +327,6 @@ The training set contains 9997 samples, the validation and the test sets have 12
 
 ### PRODUCT REVIEWS
 
-We post-process and split Product Reviews dataset to ensure uniformity with Political Statements 2.0 and Twitter Rumours 
-as they all go into form GDDS-2.0
-
 The dataset is produced from English Amazon Reviews labeled as either real or fake, relabeled as deceptive and non-deceptive respectively. 
 The reviews cover a variety of products with no particular product dominating the dataset. Although the dataset authors filtered out 
 non-English reviews, through outlier detection we found that the dataset still contains reviews in Spanish and other languages.