Merge branch 'main' of https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval into main

README.md

@@ -57,7 +57,7 @@ configs:
 # xCodeEval
 [xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://arxiv.org/abs/2303.03004)
 
-We introduce **xCodeEval**, the largest executable multilingual multitask benchmark to date consisting of $25$M document-level coding examples from about $7.5$K unique problems covering up to $17$ programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval, and it employs an execution-based evaluation. We develop a test-case based multilingual code execution engine, [**ExecEval**](https://github.com/ntunlp/ExecEval) that supports all the programming languages in **xCodeEval**. We also propose a novel data splitting and a data selection schema for balancing data distributions over multiple attributes based on geometric mean and graph-theoretic principle. 
+We introduce **xCodeEval**, the largest executable multilingual multitask benchmark to date consisting of 25 M document-level coding examples from about 7.5 K unique problems covering up to 17 programming languages with execution-level parallelism. It features a total of seven tasks involving code understanding, generation, translation and retrieval, and it employs an execution-based evaluation. We develop a test-case based multilingual code execution engine, [**ExecEval**](https://github.com/ntunlp/ExecEval) that supports all the programming languages in **xCodeEval**. We also propose a novel data splitting and a data selection schema for balancing data distributions over multiple attributes based on geometric mean and graph-theoretic principle. 
 
 This repository contains the sample code and data link for xCodeEval [paper](https://arxiv.org/abs/2303.03004).
 
@@ -88,13 +88,13 @@ git lfs pull --include "apr/test/*"
 
 We propose 7 Tasks.
 
-1. [Tag Classification](./tag_classification.md)
-2. [Code Compilation](./code_compilation.md)
-3. [Program Synthesis](./program_synthesis.md)
-4. [Code Translation](./code_translation.md)
-5. [Automatic Program Repair](./apr.md)
-6. [Code-Code Retrieval](./retrieval.md)
-7. [NL-Code Retrieval](./retrieval.md)
+1. [Tag Classification](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
+2. [Code Compilation](https://github.com/ntunlp/xCodeEval/blob/main/code_compilation.md)
+3. [Program Synthesis](https://github.com/ntunlp/xCodeEval/blob/main/program_synthesis.md)
+4. [Code Translation](https://github.com/ntunlp/xCodeEval/blob/main/code_translation.md)
+5. [Automatic Program Repair](https://github.com/ntunlp/xCodeEval/blob/main/apr.md)
+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
 
@@ -105,7 +105,7 @@ We have two data files that are required for multiple tasks.
 1. `problem_descriptions.jsonl`
 2. `unittest_db.json`
 
-You can find these two files in the root directory of the [main](https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/tree/main) branch of huggingface dataset repository. To avoid data redundency we didn't include these data with relevant task, rather we add a unique id `src_uid` to retrieve these data. 
+You can find these two files in the root directory of the [main](https://huggingface.co/datasets/NTU-NLP-sg/xCodeEval/tree/main) branch of huggingface dataset repository. To avoid data redundancy we didn't include these data with the relevant tasks, rather we add a unique id `src_uid` to retrieve these data. 
 
 ## Structure of `problem_descriptions.jsonl`
 
@@ -140,19 +140,19 @@ A sample,
 ### Key Definitions
 
 1. `description`: Problem description in textual format, math operations are written in latex.
-2. `input_from`: How the program should take unit test.
+2. `input_from`: How the program should take the unit test.
 3. `output_to`: Where the program should output the result of the unit test.
 4. `time_limit`: Time limit to solve the problem. 
 5. `memory_limit`: Memory limit to solve the problem.
-6. `input_spec`: How and what order the input will be given to the program. It also include the data range, types and sizes.
-7. `output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with *exact string match* or *floating point comparison* with a precision boundary. 
+6. `input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes.
+7. `output_spec`: How the outputs should be printed. Most of the time the unit test results are matched with an *exact string match* or *floating point comparison* with a precision boundary. 
 8. `sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
 9. `sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
 10. `notes`: Explanation of `sample_inputs` & `sample_outputs`.
 11. `tags`: The problem categories.
-12. `src_uid`: The unique id of the problem. This ID is referred in the task data samples instead of putting all these information.
-13. `difficulty`: How difficult is it to solve the problem for a human (annotated by an expert human). 
-14. `created_at`: The unix timestamp at when the problem was released. Use `datetime` lib in python to parse it to a human readable format.  
+12. `src_uid`: The unique id of the problem. This ID is referred to in the task data samples instead of putting all this information.
+13. `difficulty`: How difficult is it to solve the problem for a human (annotated by an expert human)? 
+14. `created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format.  
 
 ## Structure of `unittest_db.json`
 
@@ -182,8 +182,8 @@ unittest_db = {
 ### Key Definitions
 
 1. `unittest_db.json` dict keys i.e., `db884d679d9cfb1dc4bc511f83beedda` are the `src_uid` from `problem_descriptions.jsonl`.
-2. `input` : Input of the unit test.
-3. `output` : List of expected outputs for the unit test. 
+2. `input`: Input of the unit test.
+3. `output`: List of expected outputs for the unit test. 
 
 # Citation