Update README.md

README.md

@@ -12,7 +12,7 @@ size_categories:
 
 # Overview
 
-This dataset contains 18,219 rows of code-docstring-ast data along with additional metadata. Data was gathered from various Python libraries and frameworks and their
+This dataset contains 25,000+ rows of code-docstring-ast data along with additional metadata. Data was gathered from various Python libraries and frameworks and their
 publicly available GitHub repos. This dataset was created for the purpose of training the [CodeT5+](https://arxiv.org/abs/2305.07922) transformer on AST-enhanced code-to-doc tasks.
 
 # Sources
@@ -57,11 +57,10 @@ An instance of the dataset is as follows:
 {
   <library> : <The library from which the source code came from>,
   <name> : <The name of the function/class/method>,
-  <source code> : <The raw source code itself>,
+  <source_code> : <The raw source code itself stripped of its docstrings and comments>,
   <docstring> : <The corresponding docstring of the code>,
   <type> : <Whether it's a function, method, or class>,
   <file_path> : <The relative path of the file containing the function>,
-  <line_number> : <The line number of the function, method, or class within the file>,
   <ast_sequence> : <The ast sequence of the raw source code. Scroll down for more info about this>
 }
 ```
@@ -114,60 +113,43 @@ FunctionDef:calculate_area
 
 # Preprocessing
 
-The following preprocessing steps were applied:
+The dataset generally follows CodeSearchNet's code2nl dataset cleaning standards which are as follows:
 
-## Text Cleaning
+- Removed comments from the code
+- Removed examples where code cannot be parsed into an AST
+- Remove examples that codes cannot be parsed into an abstract syntax tree.
+- Remove examples where the number of tokens of documents is < 3 or >256
+- Remove examples that documents contain special tokens (e.g. <img ...> or https:...)
+- Remove examples that documents are not English
 
-- Removed comments
-- Filtering unusual/control characters
-- Removed trailing whitespaces
-- Converts all whitespace into a single spaces
-- Removed tags from docstrings
-
-## AST Cleaning
-
-- Removed noise using a custom blacklist
-- Removed abnormally long nodes (>100)
-- Stripped blank AST entries
-- Ensured ASTs start with the proper root nodes (FunctionDef or ClassDef)
-
-## Language Filtering
-
-- Removed non-English documentations
-- Keeps an item if detection fails
-
-## Similarity Filtering
-
-- Removed entries where similarity exceeds threshold (0.7)
-
-# Split
-
-- Dataset was split into a 70/15/15 ratio
+Furthermore, the following cleaning steps specific to this dataset were applied:
+- Docstrings from source codes was stripped
+- Standard text cleaning procedures (normalized whitespaces and special characters)
+- Removed abnormally long AST sequences (>100)
+- Structural pruning of the AST sequence (removing irrelevant or shallow identifiers/nodes)
 
 # Final Statistics
 
-The final statistics of the dataset before and after preprocessing are as follows:
+### Final Count : 25,489
+
+### Average Docstring Length : 51
 
-**Original Count**: 25,480
+### Average Source Code Length : 591
 
-**After Preprocessing**: 18,219
+### Average AST Sequence Length : 72
 
-**Retention Rate**: 72%
+## Type Distribution
 
-**Average Docstring Length**: 272
+**Methods: 13,149(52.1%)**
 
-**Average Source Code Length**: 1219
+**Functions: 8,671(34.3%)**
 
-**Average AST Sequence Length**: 91
+**Classes: 3,444(13.6%)**
 
-**Type Distribution**:
-- Methods: 9,135 (50.1%)
-- Functions: 6,322 (34.7%)
-- Classes: 2, 762 (15.2%)
+## Major Contributors
 
-**Top Contributors**:
-- pytorch: 4,330 (23.8%)
-- tensorflow: 3,972 (21.8%)
-- django: 1,778 (9.8%)
-- matplotlib: 1,454 (8%)
-- pandas: 903 (5%)
+**TensorFlow (5,455)**
+**PyTorch (5,448)**
+**Matplotlib (2,190)**
+**Django (2,138)**
+**Scipy (1,715)**