Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-generation
+size_categories:
+- 10M<n<100M
+---
+
+# Dataset Card for Python-Text2Code
+
+- **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: 
+(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 
+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
+of the sequences.
+Finally, kept instances with a maximum length of 1024 tokens (docstring+code). 
+
+The final dataset contains 23,526,586 text-to-code pairs in Python.
+
+
+## Data Fields
+
+Each instance contains 3 fields:
+- `id`: Unique ID of each pair
+- `code`: The python code
+- `docstring`: The docstring/problem description
+
+
+## Data Splits
+
+There is a single data split in the dataset. We randomly sampled 0.1% of the dataset to serve as validation set.
+
+
+## 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
+@inproceedings{christopoulou-etal-2024-text,
+    title = "Text-to-Code Generation with Modality-relative Pre-training",
+    author = "Christopoulou, Fenia  and
+      Zhang, Guchun  and
+      Lampouras, Gerasimos",
+    editor = "Graham, Yvette  and
+      Purver, Matthew",
+    booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2024",
+    address = "St. Julian{'}s, Malta",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.eacl-long.72",
+    pages = "1194--1208"
+}
+
+
+## Dataset Card Authors [optional]
+
+Fenia Christopoulou