| 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="...") | |