| from datasets import Dataset, DatasetDict | |
| import pandas as pd | |
| def process_ChatDoctor_data(data_path, hf_data_path=None): | |
| """ | |
| converting ChatDoctor data to hugging face Dataset | |
| :param data_path: | |
| :param hf_data_path: | |
| :return: | |
| """ | |
| buffer = [] | |
| rows = [] | |
| errors = 0 | |
| def clean_line(text): | |
| text = text.strip() | |
| text = text.split(":")[1].strip() | |
| text = text.strip(",").strip("\"") | |
| text = text.lstrip(",").lstrip("\"") | |
| return text.strip() | |
| with open(data_path, 'r') as file: | |
| for line in file: | |
| if line.strip() in ['[', '{', ']']: | |
| continue | |
| if line.strip() in ["},", "}"]: | |
| if len(buffer) == 3: | |
| rows.append(buffer) | |
| buffer = [] | |
| else: | |
| buffer.append(clean_line(line)) | |
| df_train = pd.DataFrame(rows, columns=["instruction", "input", "output"]) | |
| hf_data_train = Dataset.from_pandas(df_train) | |
| hf_data = DatasetDict({'train': hf_data_train}) | |
| if hf_data_path is not None: | |
| hf_data.push_to_hub(hf_data_path) | |
| print("processed data points: {}".format(len(df_train))) | |
| return df_train | |