| 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"], |
| } |
