Add missing

.gitattributes

@@ -531,3 +531,7 @@ javascript/tasky_commits_javascript_559453_592362.jsonl filter=lfs diff=lfs merg
 python/tasky_commits_python_427817_460726.jsonl filter=lfs diff=lfs merge=lfs -text
 python/tasky_commits_python_4311079_4343988.jsonl filter=lfs diff=lfs merge=lfs -text
 python/tasky_commits_python_5397076_5429985.jsonl filter=lfs diff=lfs merge=lfs -text
+javascript_add/tasky_commits_javascript_65818_98727.jsonl filter=lfs diff=lfs merge=lfs -text
+javascript_add/tasky_commits_javascript_0_32909.jsonl filter=lfs diff=lfs merge=lfs -text
+javascript_add/tasky_commits_javascript_329090_361999.jsonl filter=lfs diff=lfs merge=lfs -text
+javascript_add/tasky_commits_javascript_460726_493635.jsonl filter=lfs diff=lfs merge=lfs -text

inference_java.py

@@ -28,7 +28,7 @@ def parse_args():
         required=True,
         help="Ending file number to download. Valid values: 0 - 1023",
     )
-    parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
+    parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
     parser.add_argument(
         "--model_name",
         type=str,
@@ -103,16 +103,22 @@ if __name__ == "__main__":
         print("Exists:", tasky_commits_path)
         exit()
 
+    path = "java_add_messages.jsonl"
+    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    if args.start > len(ds): exit()
+    ds = ds[range(args.start, min(args.end, len(ds)))]
+    df = pd.DataFrame(ds, index=None)
+
     tokenizer = AutoTokenizer.from_pretrained(args.model_name)
     model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model.to(device)
     model.eval()
 
-    path = "java_add_messages.jsonl"
-    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
-    ds = ds[range(args.start, min(args.end, len(ds)))]
-    df = pd.DataFrame(ds, index=None)
+    #path = "java_add_messages.jsonl"
+    #ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    #ds = ds[range(args.start, min(args.end, len(ds)))]
+    #df = pd.DataFrame(ds, index=None)
     #tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
     #if os.path.exists(f"javascript/{tasky_commits_path}"):
     #    print("Exists:", tasky_commits_path)
@@ -127,7 +133,7 @@ if __name__ == "__main__":
     # Write two jsonl files:
     # 1) Probas for all of C4
     # 2) Probas + texts for samples predicted as tasky
-    tasky_commits_path = f"tasky_commits_java_{args.start}_{args.end}.jsonl"
+    tasky_commits_path = f"java_add/tasky_commits_java_{args.start}_{args.end}.jsonl"
 
     with open(tasky_commits_path, "w") as f:
         for i in range(len(preds)):

inference_javascript.py

@@ -28,7 +28,7 @@ def parse_args():
         required=True,
         help="Ending file number to download. Valid values: 0 - 1023",
     )
-    parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
+    parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
     parser.add_argument(
         "--model_name",
         type=str,
@@ -103,16 +103,22 @@ if __name__ == "__main__":
         print("Exists:", tasky_commits_path)
         exit()
 
+    path = "javascript_add_messages.jsonl"
+    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    if args.start > len(ds): exit()
+    ds = ds[range(args.start, min(args.end, len(ds)))]
+    df = pd.DataFrame(ds, index=None)
+
     tokenizer = AutoTokenizer.from_pretrained(args.model_name)
     model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model.to(device)
     model.eval()
 
-    path = "javascript_add_messages.jsonl"
-    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
-    ds = ds[range(args.start, min(args.end, len(ds)))]
-    df = pd.DataFrame(ds, index=None)
+    #path = "javascript_add_messages.jsonl"
+    #ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    #ds = ds[range(args.start, min(args.end, len(ds)))]
+    #df = pd.DataFrame(ds, index=None)
     #tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
     #if os.path.exists(f"javascript/{tasky_commits_path}"):
     #    print("Exists:", tasky_commits_path)
@@ -127,7 +133,7 @@ if __name__ == "__main__":
     # Write two jsonl files:
     # 1) Probas for all of C4
     # 2) Probas + texts for samples predicted as tasky
-    tasky_commits_path = f"tasky_commits_javascript_{args.start}_{args.end}.jsonl"
+    tasky_commits_path = f"javascript_add/tasky_commits_javascript_{args.start}_{args.end}.jsonl"
 
     with open(tasky_commits_path, "w") as f:
         for i in range(len(preds)):

inference_python.py

@@ -28,7 +28,7 @@ def parse_args():
         required=True,
         help="Ending file number to download. Valid values: 0 - 1023",
     )
-    parser.add_argument("--batch_size", type=int, default=32, help="Batch size")
+    parser.add_argument("--batch_size", type=int, default=8, help="Batch size")
     parser.add_argument(
         "--model_name",
         type=str,
@@ -103,16 +103,22 @@ if __name__ == "__main__":
         print("Exists:", tasky_commits_path)
         exit()
 
+    path = "python_add_messages.jsonl"
+    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    if args.start > len(ds): exit()
+    ds = ds[range(args.start, min(args.end, len(ds)))]
+    df = pd.DataFrame(ds, index=None)
+
     tokenizer = AutoTokenizer.from_pretrained(args.model_name)
     model = AutoModelForSequenceClassification.from_pretrained(args.model_name)
     device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
     model.to(device)
     model.eval()
 
-    path = "python_add_messages.jsonl"
-    ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
-    ds = ds[range(args.start, min(args.end, len(ds)))]
-    df = pd.DataFrame(ds, index=None)
+    #path = "python_add_messages.jsonl"
+    #ds = datasets.load_dataset("json", data_files=[path], ignore_verifications=True)["train"]
+    #ds = ds[range(args.start, min(args.end, len(ds)))]
+    #df = pd.DataFrame(ds, index=None)
 
     texts = df["message"].to_list()
     commits = df["commit"].to_list()
@@ -123,7 +129,7 @@ if __name__ == "__main__":
     # Write two jsonl files:
     # 1) Probas for all of C4
     # 2) Probas + texts for samples predicted as tasky
-    tasky_commits_path = f"tasky_commits_python_{args.start}_{args.end}.jsonl"
+    tasky_commits_path = f"python_add/tasky_commits_python_{args.start}_{args.end}.jsonl"
 
     with open(tasky_commits_path, "w") as f:
         for i in range(len(preds)):

javascript_add/tasky_commits_javascript_0_32909.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecd7febfb0ed024386ca341d95c9ba58b380add8e7a325b7788d8bee30d5482b
+size 4892266

javascript_add/tasky_commits_javascript_329090_361999.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f14fa8e4462a7ff38d002940ce29754f5a693632b40d2732bd60587ac50a28e8
+size 4929381

javascript_add/tasky_commits_javascript_460726_493635.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:261ca373d5221f247225d77f7136fa6430d5cdffa67f9cb3ad014198e5a6c8ea
+size 4995585

javascript_add/tasky_commits_javascript_65818_98727.jsonl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:533ba4d512fc98be2f51fab4d9f9b67ac7f8cda71bed6ced0f78959a8b10e189
+size 4946988