Update README.md

README.md

@@ -20,6 +20,8 @@ size_categories:
 
 ### How To Use This Dataset
 
+First follow the install instructions for [BabelCode](https://github.com/google-research/babelcode).
+
 To quickly evaluate BC-MBPP predictions, save the `qid` and `language` keys along with the postprocessed prediction code in a JSON lines file. Then follow the install instructions for [BabelCode](https://github.com/google-research/babelcode), and you can evaluate your predictions. 
 
 ### Dataset Summary
@@ -54,19 +56,19 @@ BC-MBPP supports:
 >>> load_dataset("gabeorlanski/bc-mbpp")
 DatasetDict({
     train: Dataset({
-        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution'],
+        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
         num_rows: 5308
     })
     test: Dataset({
-        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution'],
+        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
         num_rows: 6989
     })
     validation: Dataset({
-        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution'],
+        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
         num_rows: 1216
     })
     prompt: Dataset({
-        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution'],
+        features: ['qid', 'title', 'language', 'text', 'signature_with_docstring', 'signature', 'arguments', 'entry_fn_name', 'entry_cls_name', 'test_code', 'solution', 'test_list', 'test_case_ids'],
         num_rows: 160
     })
 })
@@ -83,9 +85,11 @@ DatasetDict({
 - `arguments`: The arguments of the problem.
 - `entry_fn_name`: The function's name to use an entry point.
 - `entry_cls_name`: The class name to use an entry point.
-- `test_code`: The raw testing script used in the language. If you want to use this, replace `PLACEHOLDER_FN_NAME` (and `PLACEHOLDER_CLS_NAME` if needed) with the corresponding entry points. Next, replace `PLACEHOLDER_CODE_BODY` with the postprocessed prediction.  
 - `solution`: The solution in Python.
-
+- `test_code`: The raw testing script used in the language. If you want to use this, replace `PLACEHOLDER_FN_NAME` (and `PLACEHOLDER_CLS_NAME` if needed) with the corresponding entry points. Next, replace `PLACEHOLDER_CODE_BODY` with the postprocessed prediction.  
+- `test_code`: The Testing Script created by the BabelCode framework
+- `test_case_ids`: The list of test case ids for the problem. These are used to determine if a prediction passes or not.
+- 
 ## Dataset Creation
 See section 2 of the [BabelCode Paper](https://arxiv.org/abs/2302.01973) to learn more about how the datasets are translated.