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theblackcat102
99 lines
3.8 KiB
Python
99 lines
3.8 KiB
Python
"""
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High level functions for model training
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"""
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from custom_datasets.prompt_dialogue import InstructionTuning, PromptGeneratedDataset
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from custom_datasets.qa_datasets import SODA, JokeExplaination, QADataset, SODADialogue, WebGPT
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from custom_datasets.summarization import SummarizationDataset
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from custom_datasets.toxic_conversation import ProsocialDialogue, ProsocialDialogueExplaination
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from custom_datasets.translation import WMT2019, DiveMT, TEDTalk
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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 = [
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"squad_v2",
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"adversarial_qa",
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"trivia_qa_context",
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"trivia_qa_nocontext",
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"gsm8k",
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"wikihow",
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"essay_instruction",
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"math_qa",
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"reddit_eli5",
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"reddit_askh",
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"reddit_asks",
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]
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SUMMARIZATION_DATASETS = [
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"xsum",
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"cnn_dailymail",
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"samsum",
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"multi_news",
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"scitldr",
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"billsum",
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"debate_sum",
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"tldr_news",
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]
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OTHER = ["prosocial_dialogue", "explain_prosocial", "instruct_tuning"]
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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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if train.no_val:
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train, eval = train_val_dataset(train, val_split=0.2)
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else:
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eval = QADataset(dataset_name, conf.cache_dir, "validation")
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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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if dataset_name == "debate_sum":
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train, eval = train_val_dataset(train, val_split=0.2)
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else:
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eval = SummarizationDataset(dataset_name, conf.cache_dir, "validation")
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elif "ted_trans" in dataset_name:
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language_pair = dataset_name.split("_")[-1]
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dataset = TEDTalk(pair=language_pair, split="train")
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train, eval = train_val_dataset(dataset, val_split=0.2)
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elif "wmt2019" in dataset_name:
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language_pair = dataset_name.split("_")[-1]
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train = WMT2019(pair=language_pair, split="train")
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eval = WMT2019(pair=language_pair, split="validation")
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elif dataset_name == "dive_mt":
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dataset = DiveMT()
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train, eval = train_val_dataset(dataset, val_split=0.2)
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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 == "prosocial_dialogue":
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train = ProsocialDialogue(cache_dir=conf.cache_dir, split="train")
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eval = ProsocialDialogue(cache_dir=conf.cache_dir, split="validation")
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elif dataset_name == "explain_prosocial":
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train = ProsocialDialogueExplaination(cache_dir=conf.cache_dir, split="train")
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eval = ProsocialDialogueExplaination(cache_dir=conf.cache_dir, split="validation")
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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 == "soda_dialogue":
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dataset = SODADialogue(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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elif dataset_name == "instruct_tuning":
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dataset = InstructionTuning(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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