update apr

README.md

@@ -1,57 +1,42 @@
 ---
 annotations_creators:
-- expert-generated
+ - expert-generated
 language:
-- code
-- en
+ - code
+ - en
 language_creators:
-- found
-- expert-generated
+ - found
+ - expert-generated
 license:
-- cc-by-nc-4.0
+ - cc-by-nc-4.0
 multilinguality:
-- multilingual
+ - multilingual
 pretty_name: xCodeEval
 size_categories:
-- 1M<n<10M
-- 10M<n<100M
+ - 1M<n<10M
+ - 10M<n<100M
 source_datasets:
-- original
+ - original
 tags:
-- programming-language
-- code
-- program-synthesis
-- automatic-code-repair
-- code-retrieval
-- code-translation
-- code-classification
+ - programming-language
+ - code
+ - program-synthesis
+ - automatic-code-repair
+ - code-retrieval
+ - code-translation
+ - code-classification
 task_categories:
-- translation
-- token-classification
-- text2text-generation
-- text-retrieval
-- text-generation
-- text-classification
-- feature-extraction
-- question-answering
-task_ids: []
-configs:
-
-- tag_classification
-- code_compilation
-- program_synthesis
-- code_translation
-- apr
-- retrieval_code_code
-- retrieval_nl_code
-- retrieval_corpus
-- problem_descriptions
-- unittest_db
+ - translation
+ - token-classification
+ - text2text-generation
+ - text-retrieval
+ - text-generation
+ - text-classification
+ - feature-extraction
+ - question-answering
 ---
 
 
-**NOTE**: Please ignore the Dataset Preview.
-
 [github](https://github.com/ntunlp/xCodeEval)
 
 # xCodeEval
@@ -63,11 +48,27 @@ This repository contains the sample code and data link for xCodeEval [paper](htt
 
 # Data Download
 
-Data is uploaded as a git LFS repo in huggingface. 
+Currently this repository supports huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api. Follow the following example to load dataset for individual examples. 
+
+```
+import datasets
+
+prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis")
+code_translation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_translation")
+tag_classification_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "tag_classification")
+apr_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "apr")
+pcode_compilation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_compilation")
+retrieval_code_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_code_code")
+retrieval_nl_code_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_nl_code")
+retrieval_corpus_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "retrieval_corpus")
+
+```
+
+Data can be also downloaded as a git LFS repo from huggingface. 
 
-![xCodeEval_hf](https://github.com/ntunlp/xCodeEval/blob/main/xcodeeval.png?raw=true)
+![xCodeEval_hf](https://github.com/ntunlp/xCodeEval/blob/main/xcodeeval-hf.png?raw=true)
 
-You can download the full data using the following command. To Download the full dataset, 
+You can download the full data using the following command.
 
 ```
 GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval
@@ -83,8 +84,6 @@ cd xCodeEval
 git lfs pull --include "apr/test/*"
 ```
 
-**NOTE**: Currently we don't support huggingface `load_dataset()` module. At this moment use `git lfs` to download the data.
-
 
 We propose 7 Tasks.
 
@@ -96,8 +95,11 @@ We propose 7 Tasks.
 6. [Code-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
 7. [NL-Code Retrieval](https://github.com/ntunlp/xCodeEval/blob/main/retrieval.md)
 
+
 # Common Data for different tasks
 
+If you are not using huggingface [`load_dataset()`](https://huggingface.co/docs/datasets/v1.11.0/package_reference/loading_methods.html#datasets.load_dataset) api, you may need to link some data with different tasks.
+
 ![xCodeEval_fig_1](https://github.com/ntunlp/xCodeEval/blob/main/xcodeeval_fig_1.png?raw=true)
 
 We have two data files that are required for multiple tasks.

loader.py

@@ -1,6 +1,51 @@
-
 import datasets
 
+SHORT_LANG_MAP = {
+    "GNU C++": "C++",
+    "GNU C++17": "C++",
+    "MS C++ 2017": "C++",
+    "MS C++": "C++",
+    "Java 8": "Java",
+    "Java 6": "Java",
+    "GNU C++11": "C++",
+    "Java 11": "Java",
+    "GNU C++14": "C++",
+    "Mono C#": "C#",
+    "GNU C": "C",
+    "Python 3": "Python",
+    "PyPy 3": "Python",
+    "GNU C11": "C",
+    "Go": "Go",
+    "Rust": "Rust",
+    "PyPy 2": "Python",
+    "Python 2": "Python",
+    "MS C#": "C#",
+    "Kotlin": "Kotlin",
+    "GNU C++0x": "C++",
+    "Java 7": "Java",
+    "Node.js": "Javascript",
+    ".NET Core C#": "C#",
+    "PHP": "PHP",
+    "GNU C++17 Diagnostics": "C++",
+    "Clang++17 Diagnostics": "C++",
+    "JavaScript": "Javascript",
+    "Ruby": "Ruby",
+    "C# 10": "C#",
+    "C# 8": "C#",
+    "Clang++20 Diagnostics": "C++",
+    "GNU C++17 (64)": "C++",
+    "GNU C++20 (64)": "C++",
+    "Java 17": "Java",
+    "Kotlin 1.4": "Kotlin",
+    "Kotlin 1.5": "Kotlin",
+    "Kotlin 1.6": "Kotlin",
+    "Kotlin 1.7": "Kotlin",
+    "PyPy 3-64": "Python",
+    "Python 3 + libs": "Python",
+    "Ruby 3": "Ruby",
+    "Rust 2021": "Rust",
+}
+
 prog_synthesis_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "program_synthesis")
 code_translation_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "code_translation")
 tag_classification_dataset = datasets.load_dataset("NTU-NLP-sg/xCodeEval", "tag_classification")

xCodeEval.py

@@ -287,6 +287,7 @@ _TEXT_FEATURES = {
     "code_compilation": {
         "file_name",
         "lang",
+        "lang_cluster",
         "source_code",
         "compilation_error",
         "code_uid",