fix - issue

README.md

@@ -38,26 +38,26 @@ code prediction.
 
 The dataset has 4 subsets:
 
-- `python-cff`: python dataset with `cff` setting.
-- `python-cfr`: python dataset with `cfr` setting.
-- `java-cff`: java dataset with `cff` setting.
-- `java-cfr`: java dataset with `cfr` setting.
+- `python_cff`: python dataset with `cff` setting.
+- `python_cfr`: python dataset with `cfr` setting.
+- `java_cff`: java dataset with `cff` setting.
+- `java_cfr`: java dataset with `cfr` setting.
 
 Each subset has 4 splits:
 
-- `train-easy`: training set with easy difficulty, where the number of code snippets in the context $$k$$ satisfies $$ 5 \leq k < 10 $$.
-- `train-hard`: training set with hard difficulty, where the number of code snippets in the context $$k$$ satisfies $$ k \geq 10 $$.
-- `test-easy`: testing set with easy difficulty.
-- `test-hard`: testing set with hard difficulty.
+- `train_easy`: training set with easy difficulty, where the number of code snippets in the context $$k$$ satisfies $$ 5 \leq k < 10 $$.
+- `train_hard`: training set with hard difficulty, where the number of code snippets in the context $$k$$ satisfies $$ k \geq 10 $$.
+- `test_easy`: testing set with easy difficulty.
+- `test_hard`: testing set with hard difficulty.
 
 ## Loading Data
 
-For example, if you want to load the `test` `cross-file-first` `python` dataset with `easy` difficulty, you can use the following code:
+For example, if you want to load the `test` `cross_file_first` `python` dataset with `easy` difficulty, you can use the following code:
 
 ```python
 from datasets import load_dataset
 
-dataset = load_dataset("tianyang/repobench-r", "python-cff", "test-easy")
+dataset = load_dataset("tianyang/repobench-r", "python_cff", "test_easy")
 ```
 
 ## Dataset Structure

repobench-r.py

@@ -43,10 +43,10 @@ _HOMEPAGE = "https://github.com/Leolty/repobench"
 _LICENSE = "Apache License 2.0"
 
 _URLs = {
-    "java-cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cff.gz",
-    "java-cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cfr.gz",
-    "python-cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cff.gz",
-    "python-cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cfr.gz"
+    "java_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cff.gz",
+    "java_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/java_cfr.gz",
+    "python_cff": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cff.gz",
+    "python_cfr": "https://huggingface.co/datasets/tianyang/repobench-r/resolve/main/data/python_cfr.gz"
 }
 
 class RepoBenchR(datasets.GeneratorBasedBuilder):
@@ -56,7 +56,7 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(
-            name="python-cff",
+            name="python_cff",
             description=textwrap.dedent(
                 """
                 cff: cross_file_first -> mask the the line that a cross-file module is first used
@@ -64,7 +64,7 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
             )
         ),
         datasets.BuilderConfig(
-            name="python-cfr",
+            name="python_cfr",
             description=textwrap.dedent(
                 """
                 cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
@@ -72,7 +72,7 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
             )
         ),
         datasets.BuilderConfig(
-            name="java-cff",
+            name="java_cff",
             description=textwrap.dedent(
                 """
                 cff: cross_file_first -> mask the the line that a cross-file module is first used
@@ -80,7 +80,7 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
             )
         ),
         datasets.BuilderConfig(
-            name="java-cfr",
+            name="java_cfr",
             description=textwrap.dedent(
                 """
                 cfr: cross_file_random -> mask a random line that a cross-file module is used (not the first time)
@@ -117,20 +117,20 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
 
         return [
             datasets.SplitGenerator(
-                name=datasets.Split("train-easy"),
-                gen_kwargs={"data_dir": data_dir, "split": "train-easy"},
+                name=datasets.Split("train_easy"),
+                gen_kwargs={"data_dir": data_dir, "split": "train_easy"},
             ),
             datasets.SplitGenerator(
                 name=datasets.Split("train_hard"),
-                gen_kwargs={"data_dir": data_dir, "split": "train-hard"},
+                gen_kwargs={"data_dir": data_dir, "split": "train_hard"},
             ),
             datasets.SplitGenerator(
-                name=datasets.Split("test-easy"),
-                gen_kwargs={"data_dir": data_dir, "split": "test-easy"},
+                name=datasets.Split("test_easy"),
+                gen_kwargs={"data_dir": data_dir, "split": "test_easy"},
             ),
             datasets.SplitGenerator(
-                name=datasets.Split("test-hard"),
-                gen_kwargs={"data_dir": data_dir, "split": "test-hard"},
+                name=datasets.Split("test_hard"),
+                gen_kwargs={"data_dir": data_dir, "split": "test_hard"},
             )
         ]
 
@@ -139,7 +139,7 @@ class RepoBenchR(datasets.GeneratorBasedBuilder):
         with gzip.open(data_dir, "rb") as f:
             data = pickle.load(f)
         
-        subset, level = split.split("-")
+        subset, level = split.split("_")
 
         for i, example in enumerate(data[subset][level]):
             yield i, {