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  1. .gitattributes +6 -0
  2. data/code_eval_board.csv +5 -4
  3. data/queue.csv +5 -1
  4. detect-pretrain-code-contamination/src/__pycache__/analyze.cpython-39.pyc +0 -0
  5. detect-pretrain-code-contamination/src/__pycache__/eval.cpython-39.pyc +0 -0
  6. detect-pretrain-code-contamination/src/__pycache__/options.cpython-39.pyc +0 -0
  7. detect-pretrain-code-contamination/src/__pycache__/run.cpython-39.pyc +0 -0
  8. detect-pretrain-code-contamination/src/__pycache__/utils.cpython-39.pyc +0 -0
  9. detect-pretrain-code-contamination/src/eval.py +26 -0
  10. detect-pretrain-code-contamination/src/run.py +1 -1
  11. saves/huggyllama-llama-7b/Rowan-hellaswag/inference2_pass.txt +0 -0
  12. saves/huggyllama-llama-7b/Rowan-hellaswag/neighbors_dls.txt +2 -2
  13. saves/huggyllama-llama-7b/ai2_arc/inference2_pass.txt +0 -0
  14. saves/huggyllama-llama-7b/ai2_arc/neighbors_dls.txt +0 -0
  15. saves/huggyllama-llama-7b/cais-mmlu/inference2_pass.txt +0 -0
  16. saves/huggyllama-llama-7b/cais-mmlu/neighbors_dls.txt +2 -2
  17. saves/huggyllama-llama-7b/gsm8k/inference2_pass.txt +0 -0
  18. saves/huggyllama-llama-7b/gsm8k/neighbors_dls.txt +2 -2
  19. saves/huggyllama-llama-7b/truthful_qa/inference2_pass.txt +0 -0
  20. saves/huggyllama-llama-7b/truthful_qa/neighbors_dls.txt +0 -0
  21. saves/huggyllama-llama-7b/winogrande/inference2_pass.txt +0 -0
  22. saves/huggyllama-llama-7b/winogrande/neighbors_dls.txt +0 -0
  23. saves/mistralai-Mistral-7B-v0.1/Rowan-hellaswag/inference2_pass.txt +0 -0
  24. saves/mistralai-Mistral-7B-v0.1/Rowan-hellaswag/neighbors_dls.txt +2 -2
  25. saves/mistralai-Mistral-7B-v0.1/ai2_arc/inference2_pass.txt +0 -0
  26. saves/mistralai-Mistral-7B-v0.1/ai2_arc/neighbors_dls.txt +0 -0
  27. saves/mistralai-Mistral-7B-v0.1/cais-mmlu/inference2_pass.txt +0 -0
  28. saves/mistralai-Mistral-7B-v0.1/cais-mmlu/neighbors_dls.txt +2 -2
  29. saves/mistralai-Mistral-7B-v0.1/gsm8k/inference2_pass.txt +0 -0
  30. saves/mistralai-Mistral-7B-v0.1/gsm8k/neighbors_dls.txt +2 -2
  31. saves/mistralai-Mistral-7B-v0.1/truthful_qa/inference2_pass.txt +0 -0
  32. saves/mistralai-Mistral-7B-v0.1/truthful_qa/neighbors_dls.txt +0 -0
  33. saves/mistralai-Mistral-7B-v0.1/winogrande/inference2_pass.txt +0 -0
  34. saves/mistralai-Mistral-7B-v0.1/winogrande/neighbors_dls.txt +0 -0
  35. saves/upstage-SOLAR-10.7B-v1.0/ai2_arc/inference2_pass.txt +0 -0
  36. saves/upstage-SOLAR-10.7B-v1.0/ai2_arc/neighbors_dls.txt +0 -0
  37. src/__pycache__/css_html.cpython-39.pyc +0 -0
  38. src/__pycache__/envs.cpython-39.pyc +0 -0
  39. src/__pycache__/text_content.cpython-39.pyc +0 -0
  40. src/__pycache__/utils.cpython-39.pyc +0 -0
.gitattributes CHANGED
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detect-pretrain-code-contamination/src/eval.py CHANGED
@@ -154,6 +154,7 @@ def process_gsm8k(data):
154
  return new_data
155
 
156
  def process_winogrande(data):
 
157
  new_data = []
158
  for ex in data:
159
  new_ex = {}
@@ -163,6 +164,31 @@ def process_winogrande(data):
163
  new_ex["input"] = ex["sentence"] + " " + output
164
  new_data.append(new_ex)
165
  return new_data
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
166
  # I'm not sure if that's the correct format for winogrande given how the dataset works.
167
 
168
  def process_hellaswag(data):
 
154
  return new_data
155
 
156
  def process_winogrande(data):
157
+ '''
158
  new_data = []
159
  for ex in data:
160
  new_ex = {}
 
164
  new_ex["input"] = ex["sentence"] + " " + output
165
  new_data.append(new_ex)
166
  return new_data
167
+ '''
168
+ new_data = []
169
+ for doc in data:
170
+ new_doc = {}
171
+
172
+ # Convert the answer to a numeric index
173
+ answer_to_num = {"1": 0, "2": 1}
174
+ label_idx = answer_to_num[doc["answer"]]
175
+
176
+ # Generate options and select the correct one based on label_idx
177
+ options = [doc["option1"], doc["option2"]]
178
+ output = options[label_idx]
179
+
180
+ # Build the new sentence by inserting the selected option
181
+ idx = doc["sentence"].index("_")
182
+ input_sentence = doc["sentence"][:idx] + output + doc["sentence"][idx+1:]
183
+
184
+ # Assigning the processed values to the new_doc
185
+ new_doc["output"] = output
186
+ new_doc["input"] = input_sentence
187
+
188
+ # Append the processed document to new_data
189
+ new_data.append(new_doc)
190
+
191
+ return new_data
192
  # I'm not sure if that's the correct format for winogrande given how the dataset works.
193
 
194
  def process_hellaswag(data):
detect-pretrain-code-contamination/src/run.py CHANGED
@@ -228,7 +228,7 @@ def main(target_model,ref_model,output_dir,data,length,key_name,ratio_gen):
228
  data = convert_huggingface_data_to_list_dic(dataset)
229
  data = process_hellaswag(data)
230
  elif data == "winogrande":
231
- dataset = load_dataset(data,"winogrande_debiased", split="validation")
232
  data = convert_huggingface_data_to_list_dic(dataset)
233
  data = process_winogrande(data)
234
 
 
228
  data = convert_huggingface_data_to_list_dic(dataset)
229
  data = process_hellaswag(data)
230
  elif data == "winogrande":
231
+ dataset = load_dataset(data, "winogrande_xl", split="validation")
232
  data = convert_huggingface_data_to_list_dic(dataset)
233
  data = process_winogrande(data)
234
 
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