cleanup

README.md

@@ -112,7 +112,7 @@ fraction of texts that are meant to deceive the person reading them one way or a
 
 Each subdirectory/config contains the domain/individual dataset split into three files:
 
-`train.jsonl`, `test.jsonl`, and `valid.jsonl` 
+`train.jsonl`, `test.jsonl`, and `validation.jsonl` 
 
 that contain train, test, and validation sets, respectively.
 
@@ -136,6 +136,15 @@ It is guaranteed to be valid unicode, less than 1 million characters, and contai
 `label` answers the question whether text is deceptive: `1` means yes, it is deceptive, `0` means no, 
 the text is not deceptive (it is truthful).
 
+### Processing and Cleaning
+
+Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, 
+entries of length less than 2 characters or exceeding 1000000 characters were all removed.
+
+Labels were manually curated and corrected in cases of clear error. 
+
+Whitespace, quotes, bulletpoints, unicode is normalized.
+
 ### Layout
 
 The directory layout of gdds is like so:
@@ -229,17 +238,8 @@ Fake News used WELFake as a basis. The WELFake dataset combines 72,134 news arti
 (Kaggle, McIntire, Reuters, and BuzzFeed Political). The dataset was cleaned of data leaks in the form of citations of 
 often reputable sources, such as "[claim] (Reuters)". It contains 35,028 real news articles and 37,106 fake news articles. 
 We found a number of out-of-domain statements that are clearly not relevant to news, such as "Cool", which is a potential
-problem for transfer learning as well as classification. After cleaning and processing, the Fake News dataset consists of
-20456 articles; 8832 are deceptive, and 11624 are not.
+problem for transfer learning as well as classification. 
 
-#### Cleaning 
-
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, 
-entries of length less than 2 characters or exceeding 1000000 characters were all removed.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
 
 #### Data
 
@@ -260,45 +260,19 @@ The original Job Labels dataset had the labels inverted when released. The probl
 
 #### Cleaning 
 
-It was cleaned by removing all HTML tags, empty descriptions, and duplicates. The dataset has been cleaned using Cleanlab.
-Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less
-than 2 characters or exceeding 1000000 characters were all removed.
-The final dataset is heavily imbalanced,  with 599 deceptive and 13696 non-deceptive samples out of the 14295 total.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
+HTML tags were removed. 
 
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 14295 samples in the dataset, contained in `job_scams.jsonl`. 
-For reproduceability, the data is also split into training, test, and validation sets in 80/10/10 ratio. 
-They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified. 
-The training set contains 11436 samples, the validation and the test sets have 1429 and 1430 samles, respectively.
+T**With just under 600 deceptive texts, this dataset is heavily imbalanced.**
 
 ### PHISHING
 
 This dataset consists of various phishing attacks as well as benign emails collected from real users.
 
-#### Cleaning 
-
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, 
-duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
-
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 15272 samples in the dataset, contained in `phishing.jsonl`.
-For reproduceability, the data is also split into training, test, and validation sets in 80/10/10 ratio. 
-They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified. 
-The training set contains 12217 samples, the validation and the test sets have 1527 and 1528 samles, respectively.
+The training set contains 12217 samples, the validation and the test sets have 1527 and 1528 samples, respectively.
 
 ### POLITICAL STATEMENTS
 
@@ -336,41 +310,19 @@ now 2 out of 6 labels map to non-deceptive and 4 map to deceptive.
 The dataset has been cleaned using cleanlab with visual inspection of problems found. Partial sentences, such as "On Iran nuclear deal", 
 "On inflation", were removed. Text with large number of errors induced by a parser were also removed.
 Statements in language other than English (namely, Spanish) were also removed. 
-Sequences with unicode errors, containing less than one characters or over 1 million characters were removed. 
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
 
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 12497 samples in the dataset, contained in `political_statements.jsonl`.
-For reproduceability, the data is also split into training, test, and validation sets in 80/10/10 ratio. 
-They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified. 
-The training set contains 9997 samples, the validation and the test sets have 1250 samles each in them.
+The training set contains 9997 samples, the validation and the test sets have 1250 samples each in them.
 
 ### 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
-
-#### Cleaning 
-
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
+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
 
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 20971 samples in the dataset, contained in `product_reviews.jsonl`.
-For reproduceability, the data is also split into training, test, and validation sets in 80/10/10 ratio. 
-They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. The sampling process was stratified.
-The training set contains 16776 samples, the validation and the test sets have 2097 and 2098 samles, respectively.
+The training set contains 16776 samples, the validation and the test sets have 2097 and 2098 samples, respectively.
 
 ### SMS
 
@@ -379,22 +331,9 @@ which contained 5,574 and 5,971 real English SMS messages, respectively. As thes
 the final dataset is made up of 6574 texts released by a private UK-based wireless operator; 1274 of them are deceptive, 
 and the remaining 5300 are not.
 
-#### Cleaning 
-
-Each dataset has been cleaned using Cleanlab. Non-english entries, erroneous (parser error) entries, empty entries, 
-duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were all removed.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
-
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 6574 samples in the dataset, contained in `sms.jsonl`. For reproduceability, the data is also split into training, 
-test, and validation sets in 80/10/10 ratio. They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`. 
-The sampling process was stratified. The training set contains 5259 samples, the validation and the test sets have 657 and 658 samles, 
+The training set contains 5259 samples, the validation and the test sets have 657 and 658 samples, 
 respectively.
 
 ### TWITTER RUMOURS
@@ -406,22 +345,9 @@ https://figshare.com/articles/dataset/PHEME_dataset_of_rumours_and_non-rumours/4
 was used in creation of this dataset. We took source tweets only, and ignored replies to them. 
 We used source tweet's label as being a rumour or non-rumour to label it as deceptive or non-deceptive.
 
-#### Cleaning
-
-The dataset has been cleaned using cleanlab with visual inspection of problems found. No issues were identified. 
-Duplicate entries, entries of length less than 2 characters or exceeding 1000000 characters were removed.
-
-#### Preprocessing
-
-Whitespace, quotes, bulletpoints, unicode is normalized.
-
 #### Data
 
-The dataset consists of "text" (string) and "is_deceptive" (1,0). 1 means the text is deceptive, 0 indicates otherwise.
-
-There are 5789 samples in the dataset, contained in `tweeter_rumours.jsonl`. For reproduceability, the data is also split into training,
-test, and validation sets in 80/10/10 ratio. They are named `train.jsonl`, `test.jsonl`, `valid.jsonl`.
-The sampling process was stratified. The training set contains 4631 samples, the validation and the test sets have 579 samles each.
+The training set contains 4631 samples, the validation and the test sets have 579 samples each.