Update README.md

README.md

@@ -44,22 +44,23 @@ During the code generation step, we use early stopping on the `}\n}` sequence to
 The early stopping method, post-processing steps, and evaluation code are available in the example below.
 
 ```python
-import torch
-import jsonlines
+import json
 import re
-from tqdm import tqdm 
+
+from datasets import load_dataset
+import jsonlines
+import torch
 from transformers import (
     AutoTokenizer,
     AutoModelForCausalLM,
     StoppingCriteria,
     StoppingCriteriaList,
 )
-from mxeval.data import get_data
+from tqdm import tqdm 
 from mxeval.evaluation import evaluate_functional_correctness
-from datasets import load_dataset
+
 
 class StoppingCriteriaSub(StoppingCriteria):
-    
     def __init__(self, stops, tokenizer):
         (StoppingCriteria.__init__(self),)
         self.stops = rf"{stops}"
@@ -75,16 +76,8 @@ class StoppingCriteriaSub(StoppingCriteria):
 
 
 def generate(problem):
-
-
-    stopping_criteria = StoppingCriteriaList(
-        [
-            StoppingCriteriaSub(
-                stops= "\n}\n", tokenizer=tokenizer
-            )
-        ]
-    )
-
+    criterion = StoppingCriteriaSub(stops="\n}\n", tokenizer=tokenizer)
+    stopping_criteria = StoppingCriteriaList([criterion])
     
     problem = tokenizer.encode(problem, return_tensors="pt").to('cuda')
     sample = model.generate(
@@ -99,22 +92,20 @@ def generate(problem):
     )
     
     answer = tokenizer.decode(sample[0], skip_special_tokens=True)
-
     return answer
 
-def clean_asnwer(code):
 
+def clean_asnwer(code):
     # Clean comments
     code_without_line_comments = re.sub(r"//.*", "", code)
     code_without_all_comments = re.sub(
         r"/\*.*?\*/", "", code_without_line_comments, flags=re.DOTALL
     )
-
     #Clean signatures
     lines = code.split("\n")
     for i, line in enumerate(lines):
         if line.startswith("fun "):
-            return "\n".join(lines[i + 1 :])
+            return "\n".join(lines[i + 1:])
             
     return code
 
@@ -123,10 +114,9 @@ model_name = "JetBrains/CodeLlama-7B-Kexer"
 dataset = load_dataset("jetbrains/Kotlin_HumanEval")['train']
 problem_dict = {problem['task_id']: problem for problem in dataset}
 
-model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype=torch.bfloat16).to('cuda')
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16).to('cuda')
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-
 output = []
 for key in tqdm(list(problem_dict.keys()), leave=False):
     problem = problem_dict[key]["prompt"]
@@ -134,7 +124,6 @@ for key in tqdm(list(problem_dict.keys()), leave=False):
     answer = clean_asnwer(answer)
     output.append({"task_id": key, "completion": answer, "language": "kotlin"})
 
-
 output_file = f"answers"
 with jsonlines.open(output_file, mode="w") as writer:
     for line in output:
@@ -148,6 +137,17 @@ evaluate_functional_correctness(
     problem_file=problem_dict,
 )
 
+with open(output_file + '_results.jsonl') as fp:
+    total = 0
+    correct = 0
+    for line in fp:
+        sample_res = json.loads(line)
+        print(sample_res)
+        total += 1
+        correct += sample_res['passed']
+
+print(f'Pass rate: {correct/total}')
+
 ```