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from datasets import load_dataset
dataset_name = "2nji/makebelieve-480"
# Load the dataset
dataset = load_dataset('json', data_files='./generation/processed/main.jsonl')
# Shuffle the dataset and slice it
dataset = dataset['train'].shuffle(seed=42).select(range(480))
# Define a function to transform the data
def transform_conversation(example):
conversation_text = example['text']
segments = conversation_text.split('###')
reformatted_segments = []
# Iterate over pairs of segments
for i in range(1, len(segments) - 1, 2):
human_text = segments[i].strip().replace('Human:', '').strip()
# Check if there is a corresponding assistant segment before processing
if i + 1 < len(segments):
assistant_text = segments[i+1].strip().replace('Assistant:', '').strip()
# Apply the new template
reformatted_segments.append(f'<s>[INST] {human_text} [/INST] {assistant_text} </s>')
else:
# Handle the case where there is no corresponding assistant segment
reformatted_segments.append(f'<s>[INST] {human_text} [/INST] </s>')
return {'text': ''.join(reformatted_segments)}
# Apply the transformation
transformed_dataset = dataset.map(transform_conversation)
# Upload the dataset to the Hub
# Don't forget to replace the token with your own
transformed_dataset.push_to_hub(dataset_name, token="...")
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