Download pre-processing.py from tatvamasi/phi-2-ft: direct link, hf CLI and curl.
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https://huggingface.co/tatvamasi/phi-2-ft/resolve/main/pre-processing.py
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hf download hf://tatvamasi/phi-2-ft/pre-processing.py
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curl -L -o pre-processing.py https://huggingface.co/tatvamasi/phi-2-ft/resolve/main/pre-processing.py
1.02 kB
| from datasets import load_dataset | |
| from datasets import load_from_disk | |
| import pandas as pd | |
| from huggingface_hub import interpreter_login | |
| interpreter_login() | |
| # dataset = load_from_disk("Path to folder") | |
| dataset = load_dataset("tatvamasi/medquad-std", split="train") | |
| print(dataset) | |
| # Convert to DataFrame | |
| df = pd.DataFrame(dataset) | |
| # Display the first few rows of the DataFrame | |
| print(df.head(2)) | |
| # Need to merge the question and answer in the instruction format | |
| def format_row(row): | |
| question = row['Question'] | |
| answer = row['Answer'] | |
| formatted_string = f"[INST] {question} [/INST] {answer} " | |
| return formatted_string | |
| # Apply the function to each row of the dataframe | |
| df['Formatted'] = df.apply(format_row, axis=1) | |
| # Display the formatted column | |
| print(df['Formatted']) | |
| new_df = df.rename(columns={'Formatted': 'Text'}) | |
| new_df = new_df[['Text']] | |
| print(new_df.head(3)) | |
| new_df.to_csv('formatted_qna_set.csv', index=False) | |
| df_from_disk = pd.read_csv("formatted_qna_set.csv") | |
| print(df_from_disk.head(2)) |