update split naming

xCodeEval.py

@@ -107,8 +107,8 @@ _DESCRIPTIONS = {
 			13. `apr_id`: A unique ID for the apr sample. It is not important for model training. If you find any issue with the sample, you can report it to us mentioning the `apr_id`.
 			14. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.  
 			15. `tags`: List of potential algorithmic techniques required to write the program.
-			16. `bug_exec_outcome`: A pre-run execution outcome of `bug_source_code`. 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.   
-			17. `fix_exec_outcome`: A pre-run execution outcome of `fix_source_code`. 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.   
+			16. `bug_exec_outcome`: A pre-run execution outcome of `bug_source_code`. 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 compact and titan test data.   
+			17. `fix_exec_outcome`: A pre-run execution outcome of `fix_source_code`. 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 compact and titan test data.   
 			18. `potential_dominant_fix_op`: A potential fix op recommended by difflib.
 			19. `lang_cluster`: A generic programming language name the value of `lang` belongs to.
 			20. `prob_desc_description`: Problem description in textual format, math operations are written in latex.
@@ -133,7 +133,7 @@ _DESCRIPTIONS = {
 			3. `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`.
 			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. `difficulty`: Difficulty rating of the problem indicated by `src_uid`. The higher the harder.  
-			6. `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.
+			6. `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 compact and titan test data.
             7. `lang_cluster`: A generic programming language name the value of `lang` belongs to.
             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.
@@ -159,7 +159,7 @@ _DESCRIPTIONS = {
 			5. `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)
 			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.
+			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 compact and titan test data.
             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.
@@ -2437,7 +2437,7 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
                 },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
+                name='compact',
                 gen_kwargs={
                     "filepaths": validation_downloaded_files,
                     "problem_description_file": prob_desc_file,
@@ -2445,7 +2445,7 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
                 },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TEST,
+                name='titan',
                 gen_kwargs={
                     "filepaths": test_downloaded_files,
                     "problem_description_file": prob_desc_file,
@@ -2456,7 +2456,7 @@ class xCodeEval(datasets.GeneratorBasedBuilder):
         if task_name == "code_translation":
             split_info.append(
                 datasets.SplitGenerator(
-                    name="validation_small",
+                    name="compact_small",
                     gen_kwargs={
                         "filepaths": validation_small_downloaded_files,
                         "problem_description_file": prob_desc_file,