Initial commit

2025-04-13 09:30:46 +08:00
commit 4f8972e88a
248 changed files with 34689 additions and 0 deletions
@@ -0,0 +1,67 @@
+import json
+import re
+
+json_path = "/mnt/bn/vl-research/workspace/boli01/projects/sft_data_workspace/vlfeedback_80k.jsonl"
+
+with open(json_path, "r") as f:
+    data = f.readlines()
+
+data = [json.loads(d) for d in data]
+
+
+def convert_format(original_data, dimension="Visual Faithfulness"):
+    converted_data = []
+    for item in original_data:
+        # Assuming the best response is the one with the highest helpfulness rating
+        best_completion = max(item["completions"], key=lambda x: int(x["annotations"]["Helpfulness"]["Rating"]))
+        best_response = best_completion["response"]
+        best_model = best_completion["model"]
+
+        if "†source" in best_response:
+            print(best_response)
+            # Regex pattern to match the pattern 【digit†source】
+            pattern = r"【\d+†source】"
+            # Replace the matched patterns with an empty string
+            cleaned_text = re.sub(pattern, "", best_response)
+            best_response = cleaned_text
+            print(f"*****************************************")
+            print(best_response)
+
+        # Assuming the worst response is the one with the lowest helpfulness rating
+        worst_completion = min(item["completions"], key=lambda x: int(x["annotations"]["Helpfulness"]["Rating"]))
+        worst_response = worst_completion["response"]
+
+        if "†source" in worst_response:
+            print(worst_response)
+            # Regex pattern to match the pattern ��digit†source】
+            pattern = r"【\d+†source】"
+            # Replace the matched patterns with an empty string
+            cleaned_text = re.sub(pattern, "", worst_response)
+            worst_response = cleaned_text
+            print(f"*****************************************")
+            print(worst_response)
+
+        # Extract scores
+        best_score = int(best_completion["annotations"][dimension]["Rating"])
+        worst_score = int(worst_completion["annotations"][dimension]["Rating"])
+
+        # Construct the new format
+        new_item = {
+            "id": item["id"],
+            "prompt": item["prompt"],
+            "answer": "",
+            "image": f"silkie_dpo/{item['id']}.jpg",  # Assuming the video ID is the last part of the original ID
+            "chosen": best_response,
+            "rejected": worst_response,
+            "chosen_score": best_score,
+            "rejected_score": worst_score,
+        }
+        converted_data.append(new_item)
+
+    return converted_data
+
+
+for dimension in ["Visual Faithfulness", "Helpfulness", "Ethical Considerations"]:
+    converted_data = convert_format(data, dimension=dimension)
+    with open(f"/mnt/bn/vl-research/data/llava_instruct/dpo_data/silkie_dpo_data_{dimension.replace(' ', '_').lower()}_{len(converted_data)}.json", "w") as f:
+        json.dump(converted_data, f, indent=4)