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157 | @DataConverterRegistry.register("rewardbench2")
class RewardBench2Converter(DataConverter):
"""
Unified converter for conversation data with prompt, chosen and rejected responses (version 2)
"""
def convert_to_data_sample(
self, data_dict: Dict[str, Any], source_info: Dict[str, Any]
) -> DataSample:
"""Convert conversation data to DataSample format"""
# generate unique id using id field if available, otherwise use prompt content
if "id" in data_dict:
unique_id = str(data_dict["id"])
else:
content = str(data_dict.get("prompt", ""))
unique_id = hashlib.md5(content.encode()).hexdigest()
# Create input from prompt
data_input = self._create_conversation_input(data_dict)
# Create outputs from chosen/rejected responses
data_output = self._create_conversation_output(data_dict)
try:
# Build metadata based on source type
metadata = {
"raw_data": data_dict,
"load_strategy": "RewardBench2Converter",
"subset": data_dict.get("subset"),
"num_correct": data_dict.get("num_correct"),
"num_rejected": data_dict.get("num_rejected"),
"total_completions": data_dict.get("total_completions"),
"models": data_dict.get("models"),
}
# Add source-specific metadata
if source_info.get("load_type") == "local":
metadata.update(
{
"source_file_path": source_info.get("source_file_path"),
"load_type": "local",
}
)
elif source_info.get("load_type") == "huggingface":
metadata.update(
{
"dataset_name": source_info.get("dataset_name"),
"dataset_config": source_info.get("dataset_config"),
"split": source_info.get("split", "train"),
"load_type": "huggingface",
}
)
data_sample = DataSample(
unique_id=unique_id,
input=data_input,
output=data_output,
source="rewardbench2",
task_category="conversation",
metadata=metadata,
)
return data_sample
except Exception as e:
logger.error(f"Error creating conversation DataSample: {str(e)}")
return None
def _create_conversation_input(
self, data_dict: Dict[str, Any]
) -> list[ChatMessage]:
"""Create DataInput from conversation prompt"""
prompt = data_dict.get("prompt", "")
# Since prompt is now a string, create a single user message
if isinstance(prompt, str):
return [ChatMessage(role="user", content=prompt)]
else:
# Fallback for backwards compatibility
history = []
if isinstance(prompt, list):
for turn in prompt:
if isinstance(turn, dict):
role = turn.get("role", "user")
content = turn.get("content", str(turn))
history.append(ChatMessage(role=role, content=content))
else:
history.append(ChatMessage(role="user", content=str(turn)))
else:
history.append(ChatMessage(role="user", content=str(prompt)))
return history
def _create_conversation_output(
self, data_dict: Dict[str, Any]
) -> list[DataOutput]:
"""Create DataOutput list from conversation responses"""
outputs = []
# Handle chosen responses (now a list of strings)
chosen_responses = data_dict.get("chosen", [])
if isinstance(chosen_responses, list):
for chosen_content in chosen_responses:
outputs.append(
DataOutput(
answer=Step(
role="assistant",
content=str(chosen_content),
label={"preference": "chosen"},
),
)
)
elif chosen_responses: # Single chosen response (backwards compatibility)
outputs.append(
DataOutput(
answer=Step(
role="assistant",
content=str(chosen_responses),
label={"preference": "chosen"},
),
)
)
# Handle rejected responses (now a list of strings)
rejected_responses = data_dict.get("rejected", [])
if isinstance(rejected_responses, list):
for rejected_content in rejected_responses:
outputs.append(
DataOutput(
answer=Step(
role="assistant",
content=str(rejected_content),
label={"preference": "rejected"},
),
)
)
elif rejected_responses: # Single rejected response (backwards compatibility)
outputs.append(
DataOutput(
answer=Step(
role="assistant",
content=str(rejected_responses),
label={"preference": "rejected"},
),
)
)
return outputs
|