Update README.md

README.md

@@ -1,3 +1,81 @@
 ---
-license: apache-2.0
+annotations_creators: []
+language_creators:
+- crowdsourced
+- expert-generated
+language:
+- code
+license:
+- other
+multilinguality:
+- multilingual
+pretty_name: xlcost-text-to-code
+size_categories:
+- unknown
+source_datasets: []
+task_categories:
+- sequence-modeling
+task_ids:
+- language-modeling
 ---
+
+# XLCost for text-to-code synthesis
+
+## Dataset Description
+This is a subset of [XLCoST benchmark](https://github.com/reddy-lab-code-research/XLCoST), for text-to-code generation at snippet level and program level for **7** programming languages: `Python, C, C#, C++, Java, Javascript and PHP`. 
+
+## Languages
+
+The dataset contains text in English and its corresponding code translation. Each program is divided into several code snippets, so the snipppet-level subsets contains these code snippets with their corresponding comments, for program-level subsets, the comments were concatenated in one long description. Moreover, programs in all the languages are aligned at the snippet level and the comment for a particular snippet is the same across all the languages. 
+
+## Dataset Structure
+To load the dataset you need to specify a subset among the **14 exiting instances**: `LANGUAGE-snippet-level/LANGUAGE-program-level` for `LANGUAGE` in `[Python, C, Csharp, C++, Java, Javascript and PHP]`. By default `Python-snippet-level` loaded. 
+
+```python
+from datasets import load_dataset
+load_dataset("codeparrot/xlcost-text-to-code", "Python-program-level")
+
+DatasetDict({
+    train: Dataset({
+        features: ['text', 'code'],
+        num_rows: 9263
+    })
+    test: Dataset({
+        features: ['text', 'code'],
+        num_rows: 887
+    })
+    validation: Dataset({
+        features: ['text', 'code'],
+        num_rows: 472
+    })
+})
+```
+
+```python
+next(iter(data["train"]))
+{'text': 'Maximum Prefix Sum possible by merging two given arrays | Python3 implementation of the above approach ; Stores the maximum prefix sum of the array A [ ] ; Traverse the array A [ ] ; Stores the maximum prefix sum of the array B [ ] ; Traverse the array B [ ] ; Driver code',
+ 'code': 'def maxPresum ( a , b ) : NEW_LINE INDENT X = max ( a [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( a ) ) : NEW_LINE INDENT a [ i ] += a [ i - 1 ] NEW_LINE X = max ( X , a [ i ] ) NEW_LINE DEDENT Y = max ( b [ 0 ] , 0 ) NEW_LINE for i in range ( 1 , len ( b ) ) : NEW_LINE INDENT b [ i ] += b [ i - 1 ] NEW_LINE Y = max ( Y , b [ i ] ) NEW_LINE DEDENT return X + Y NEW_LINE DEDENT A = [ 2 , - 1 , 4 , - 5 ] NEW_LINE B = [ 4 , - 3 , 12 , 4 , - 3 ] NEW_LINE print ( maxPresum ( A , B ) ) NEW_LINE'}
+```
+Note that the data undergo some tokenization hence the additional whitespaces and the use of NEW_LINE instead of `\n` and INDENT instead of `\t`, DEDENT to cancel indentation...
+
+## Data Fields
+
+* text: natural language description/comment
+* code: code at snippet/program level
+
+## Data Splits
+
+Each subset has three splits: train, test and validation.
+
+## Citation Information
+
+```
+@misc{zhu2022xlcost,
+     title = {XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence},
+     url = {https://arxiv.org/abs/2206.08474},
+     author = {Zhu, Ming and Jain, Aneesh and Suresh, Karthik and Ravindran, Roshan and Tipirneni, Sindhu and Reddy, Chandan K.},
+     year = {2022},
+     eprint={2206.08474},
+     archivePrefix={arXiv}
+}
+```