update code translation

xCodeEval.py

@@ -134,6 +134,21 @@ _DESCRIPTIONS = {
 			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.
+            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.
+			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.
+			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."""
     ),
     "program_synthesis": textwrap.dedent(
@@ -248,6 +263,7 @@ _TEXT_FEATURES = {
     "code_translation": {
         "file_name",
         "lang",
+        "lang_cluster",
         "source_code",
         "code_uid",
         "src_uid",