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from datasets import load_dataset, Dataset
import pandas as pd
from pdb import set_trace as st
def process_dataset_ultrafeedback():
"""
Processes the 'train_prefs' and 'test_prefs' splits of the 'HuggingFaceH4/ultrafeedback_binarized' dataset
into a unified format for preference modeling.
Returns:
dict: A dictionary containing the unified 'train' and 'test' splits of the dataset in the KTO format.
Each split is a Hugging Face Dataset object.
"""
# Load the relevant splits of the dataset
dataset_name = "HuggingFaceH4/ultrafeedback_binarized"
train_prefs = load_dataset(dataset_name, split="train_prefs")
test_prefs = load_dataset(dataset_name, split="test_prefs")
# Function to transform a single example into the desired schema
def transform_data(example):
data_points = []
# Chosen completion
chosen_completion = example["chosen"][1]["content"]
if chosen_completion.strip(): # Check for non-empty completions
data_points.append({
"prompt": example["prompt"],
"completion": chosen_completion.strip(),
"label": True
})
# Rejected completion
rejected_completion = example["rejected"][1]["content"]
if rejected_completion.strip(): # Check for non-empty completions
data_points.append({
"prompt": example["prompt"],
"completion": rejected_completion.strip(),
"label": False
})
return data_points
# Process train and test splits
train_data = []
test_data = []
for example in train_prefs:
train_data.extend(transform_data(example))
for example in test_prefs:
test_data.extend(transform_data(example))
# Convert unified data to DataFrames
train_df = pd.DataFrame(train_data)
test_df = pd.DataFrame(test_data)
# Convert to Hugging Face Dataset
unified_train = Dataset.from_pandas(train_df)
unified_test = Dataset.from_pandas(test_df)
return {"train": unified_train, "test": unified_test}
if __name__ == "__main__":
kto_dataset = process_dataset_ultrafeedback()
st()
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