| import datasets | |
| from datasets import DatasetBuilder, DatasetInfo | |
| import pandas as pd | |
| class TMDataset(datasets.GeneratorBasedBuilder): | |
| def __init__(self): | |
| self.downloads = [ | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_1.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_10.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_11.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_12.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_13.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_2.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_3.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_4.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_5.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_6.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_7.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_8.parquet", | |
| "https://huggingface.co/datasets/Locutusque/TM-DATA/resolve/main/combined_datasets_batch_9.parquet", | |
| ] | |
| VERSION = datasets.Version("1.0.0") | |
| def _info(self): | |
| features = datasets.Features({ | |
| "text": datasets.Value("string"), | |
| }) | |
| return datasets.DatasetInfo( | |
| description="Combination of text completion datasets", | |
| features=features, | |
| supervised_keys=None | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(self.downloads) | |
| return [ | |
| datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}) | |
| ] | |
| def _generate_examples(self, filepath): | |
| for file in filepath: | |
| df = pd.read_parquet(file) | |
| for idx, row in df.iterrows(): | |
| yield idx, { | |
| "text": row["text"], | |
| } |