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README.md

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 # Dataset Card for Python-Text2Code
 
+This dataset supports the following EACL paper [Text-to-Code Generation with Modality-relative Pre-training](https://aclanthology.org/2024.eacl-long.72)
+
 - **Repository:** https://github.com/huawei-noah/noah-research/tree/master/NLP/text2code_mrpt
-- **Paper [optional]:** https://aclanthology.org/2024.eacl-long.72.pdf
 - **Point of Contact:** [Fenia Christopoulou](mailto:[email protected])
 
 ## Dataset Description
 
-The data were crawled from existing, public repositories from GitHub before May 2021.
-Duplicate files based on the rowKey of each file’s MD5 were removed and files that met the following criteria were kept: 
+The data were crawled from existing, public repositories from GitHub before May 2021 and were meant to be used for 
+additional model training for the task of Code Synthesis (i.e. Text-to-Code generation) in Python.
+
+### Details
+Files that met the following criteria were kept: 
 (a) the file size is under 1MB; 
 (b) the code is Python3 compatible, using Abstract Syntactic Tree (AST) parsing; 
 (c) there are fewer than 100 characters per line on average; 
 (d) and there are fewer than 1,000 characters in any single line. 
 
-We then applied AST parsing (via [Tree-sitter](https://tree-sitter.github.io/tree-sitter/)) on the remaining Python files to extract valid functions and 
+We applied AST parsing (via [Tree-sitter](https://tree-sitter.github.io/tree-sitter/)) on the remaining Python files to extract valid functions and 
 their corresponding docstrings. 
 Docstrings were used a "problem descriptions" and were separated from the code. Functions without a docstring were discarded.
-We then replaced new lines, indentation and dedentation with `<NEW_LINE>`, `<INDENT>` and `<DEDENT>`, respectively, to normalise spaces, which effectively reduced the length
+We replaced new lines, indentation and dedentation with `<NEW_LINE>`, `<INDENT>` and `<DEDENT>`, respectively, to normalise spaces, which effectively reduced the length
 of the sequences.
-Finally, kept instances with a maximum length of 1024 tokens (docstring+code). 
+Finally, only instances with a maximum length of 1024 tokens (docstring+code) were kept. 
 
 The final dataset contains 23,526,586 text-to-code pairs in Python.
 
+Check the paper for additional details!
+
 
 ## Data Fields
 
 Each instance contains 3 fields:
 - `id`: Unique ID of each pair
 - `code`: The python code
-- `docstring`: The docstring/problem description
+- `docstring`: The docstring/problem description associated with this code
 
 
 ## Data Splits
@@ -46,8 +52,6 @@ There is a single data split in the dataset. We randomly sampled 0.1% of the dat
 
 ## Citation
 
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
 **BibTeX:**
 
 ```html
@@ -67,7 +71,3 @@ There is a single data split in the dataset. We randomly sampled 0.1% of the dat
     pages = "1194--1208"
 }
 
-
-## Dataset Card Authors [optional]
-
-Fenia Christopoulou