update data loader

xCodeEval.py

@@ -148,8 +148,7 @@ _DESCRIPTIONS = {
 			18. `prob_desc_created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format. 
 			19. `file_name`: Name of the source jsonl file from where data is loaded.
 			20. `hidden_unit_tests`: a list of unit tests returned as string. use `json.loads(hidden_unit_tests)` to load the data.
-            
-		Objective: Given a source code in lang, generate a code in target lang."""
+		Objective: Given a source code (`source_code`) in `lang_cluster`, generate a code in target programming language."""
     ),
     "program_synthesis": textwrap.dedent(
         """### Key Definitions
@@ -161,7 +160,20 @@ _DESCRIPTIONS = {
 			6. `code_uid`: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `code_uid`.
 			7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.  
 			8. `exec_outcome`: Execution outcome status. Follow [Section 4.1](https://arxiv.org/pdf/2303.03004.pdf) to know the potential list of outcomes. The `exec_outcome` flags in the training data comes from a pre-run environmeent. However, training data doesn't  includes unit-test to avoid potential hacks. We provide unit test for only dev and test data.
-		Objective: Given a src_uid read problem description from `problem_descriptions.jsonl` and generate a solution for problem description."""
+            9. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
+			10. `prob_desc_input_from`: How the program should take the unit test.
+			11. `prob_desc_output_to`: Where the program should output the result of the unit test.
+			12. `prob_desc_time_limit`: Time limit to solve the problem. 
+			13. `prob_desc_memory_limit`: Memory limit to solve the problem.
+			14. `prob_desc_input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes.
+			15. `prob_desc_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. 
+			16. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
+			17. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
+			18. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`.
+			19. `prob_desc_created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format. 
+			20. `file_name`: Name of the source jsonl file from where data is loaded.
+			21. `hidden_unit_tests`: a list of unit tests returned as string. use `json.loads(hidden_unit_tests)` to load the data.
+		Objective: Given a `src_uid` read problem description from `problem_descriptions.jsonl` and generate a solution for problem description."""
     ),
     "retrieval_code_code": textwrap.dedent(
         """### Key Definitions
@@ -169,6 +181,7 @@ _DESCRIPTIONS = {
 			2. `negative_code` : list of negative codes for `nl`
 			3. `src_uid` : A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl)
 			4. `source_code` : A source code given as input query.
+            5. `file_name`: Name of the source jsonl file from where data is loaded.
    		Objective: Given a source_code retrieve similar source code from `retrieval_corpus`."""
     ),
     "retrieval_nl_code": textwrap.dedent(
@@ -177,12 +190,14 @@ _DESCRIPTIONS = {
 			2. `positive_code` : list of positive codes for `nl`
 			3. `negative_code` : list of negative codes for `nl`
 			4. `src_uid` : A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl)
+            5. `file_name`: Name of the source jsonl file from where data is loaded.
    		Objective: Given a nl (problem description) retrieve similar source code from `retrieval_corpus`."""
     ),
     "retrieval_corpus": textwrap.dedent(
         """### Key Definitions
 	 		1. `idx` : unique index for each sample on a specific langauge (read language from filename).
 			4. `source_code` : A source code given as retrieval document.
+            3. `file_name`: Name of the source jsonl file from where data is loaded.
    		Objective: Use the retrival_corpus to perform query for retrieval_nl_code and retrieval_code_code ."""
     ),
     "code_compilation": textwrap.dedent(
@@ -194,6 +209,7 @@ _DESCRIPTIONS = {
 			5. `code_uid`: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `code_uid`.
 			6. `src_uid`: A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl)
 			7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.
+            8. `file_name`: Name of the source jsonl file from where data is loaded.
    		Objective: Given a `source_code` the objective is to classify if the code compiles or not (label:compilation_error) ."""
     ),
     "tag_classification": textwrap.dedent(
@@ -205,7 +221,19 @@ _DESCRIPTIONS = {
 			5. `code_uid`: A unique ID for the sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `code_uid`.
 			6. `src_uid`: A specific identifier that shows which problem the code is associated with. This identifier is **important** for the training of the model. The problem referred to by the `src_uid` provides a natural description of the problem that the code successfully solved. Refer to [Structure of `problem_descriptions.jsonl`](./README.md#structure-of-problem_descriptionsjsonl)
 			7. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.  
-   		Objective: Given a `source_code` the objective is to classify the code into multi-label tags (label:tags) ."""
+            8. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
+			9. `prob_desc_input_from`: How the program should take the unit test.
+			10. `prob_desc_output_to`: Where the program should output the result of the unit test.
+			11. `prob_desc_time_limit`: Time limit to solve the problem. 
+			12. `prob_desc_memory_limit`: Memory limit to solve the problem.
+			13. `prob_desc_input_spec`: How and in what order the input will be given to the program? It also includes the date range, types, and sizes.
+			14. `prob_desc_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. 
+			15. `prob_desc_sample_inputs`: A sample input for the code that is expected to solve the problem described in `description`.
+			16. `prob_desc_sample_outputs`: The expected output for the `sample_input` that is expected to solve the problem described in `description`.
+			17. `prob_desc_notes`: Explanation of `sample_inputs` & `sample_outputs`.
+			18. `prob_desc_created_at`: The Unix timestamp when the problem was released. Use `datetime` lib in Python to parse it to a human-readable format. 
+            19. `file_name`: Name of the source jsonl file from where data is loaded.
+   		Objective: Given a `source_code` the objective is to classify the code into multi-label tags (label:tags)."""
     ),
 }