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theblackcat102
46 lines
2.0 KiB
Python
46 lines
2.0 KiB
Python
from custom_datasets.prompt_dialogue import PromptGeneratedDataset
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from custom_datasets.qa_datasets import SODA, JokeExplaination, QADataset, WebGPT
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from custom_datasets.summarization import SummarizationDataset
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from sklearn.model_selection import train_test_split
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from torch.utils.data import Subset
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QA_DATASETS = ["squad_v2", "adversarial_qa", "trivia_qa_context", "trivia_qa_nocontext", "gsm8k"]
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SUMMARIZATION_DATASETS = ["xsum", "cnn_dailymail", "samsum", "multi_news", "scitldr", "billsum"]
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def train_val_dataset(dataset, val_split=0.2):
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train_idx, val_idx = train_test_split(
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list(range(len(dataset))), test_size=val_split, random_state=666, shuffle=True
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)
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return Subset(dataset, train_idx), Subset(dataset, val_idx)
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def get_one_dataset(conf, dataset_name):
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dataset_name = dataset_name.lower()
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if dataset_name in QA_DATASETS:
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train = QADataset(dataset_name, conf.cache_dir, "train")
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val_name = "validation" if dataset_name not in ["gsm8k"] else "test"
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eval = QADataset(dataset_name, conf.cache_dir, val_name)
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elif dataset_name in SUMMARIZATION_DATASETS:
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train = SummarizationDataset(dataset_name, conf.cache_dir, "train")
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val_name = "validation" if dataset_name not in ["billsum"] else "test"
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eval = SummarizationDataset(dataset_name, conf.cache_dir, val_name)
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elif dataset_name == "webgpt":
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dataset = WebGPT()
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train, eval = train_val_dataset(dataset, val_split=0.2)
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elif dataset_name == "prompt_dialogue":
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dataset = PromptGeneratedDataset(conf.cache_dir)
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train, eval = train_val_dataset(dataset, val_split=0.2)
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elif dataset_name == "soda":
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dataset = SODA(conf.cache_dir)
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train, eval = train_val_dataset(dataset, val_split=0.1)
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elif dataset_name == "joke":
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dataset = JokeExplaination(conf.cache_dir)
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train, eval = train_val_dataset(dataset, val_split=0.2)
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else:
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raise ValueError(f"Unknown dataset {dataset_name}")
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return train, eval
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