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},
"chemblpre": {"dataset": "chemblpre",
"task": {"default": DefaultGPTask,
"default_text": DefaultTextGPTask,
"QA": GQATask},
"evaluation": {"default": ("multiauc", {"metric_name": "multiauc", "num_labels": 1048}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy", "mode": "re",
"regular_patterns": r"\b(Yes|yes|No|no)\b"})},
},
"molproperties": {"dataset": "molproperties",
"task": {"QA": GQATask},
"evaluation": {"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"products": {"dataset": "products",
"task": {"default": DefaultNPTask,
"subgraph": SubgraphNPTask,
"default_text": DefaultTextNPTask,
"subgraph_text": SubgraphTextNPTask,
"QA": NQATask},
"evaluation": {"default": ("accuracy", {"metric_name": "accuracy", "num_classes": 44}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"ml1m": {"dataset": "ml1m",
"task": {"default": DefaultLPTask,
"subgraph": SubgraphLPTask,
"default_text": DefaultTextLPTask,
"subgraph_text": SubgraphTextLPTask,
"QA": LQATask},
"evaluation": {"default": ("rmse", {"metric_name": "rmse"}),
"QA": ("text_rmse", {"metric_name": "text_rmse"})},
},
"ml1m_cls": {"dataset": "ml1m_cls",
"task": {"default": DefaultLPTask,
"subgraph": SubgraphLPTask,
"default_text": DefaultTextLPTask,
"subgraph_text": SubgraphTextLPTask,
"QA": LQATask},
"evaluation": {"default": ("accuracy", {"metric_name": "accuracy", "num_classes": 2}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy", "mode": "re",
"regular_patterns": r"\b(Yes|yes|No|no)\b"})},
},
"mag240m": {"dataset": "mag240m",
"task": {"default": DefaultNPTask,
"subgraph": SubgraphNPTask,
"default_text": DefaultTextNPTask,
"subgraph_text": SubgraphTextNPTask,
"QA": NQATask},
"evaluation": {"default": ("accuracy", {"metric_name": "accuracy", "num_classes": 153}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"expla_graph": {"dataset": "expla_graph",
"task": {"default_text": DefaultTextGPTask,
"QA": GQATask},
"evaluation": {"default": ("accuracy", {"metric_name": "accuracy", "num_classes": 2}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy", "mode": "re",
"regular_patterns": r"\b(Support|support|Counter|counter)\b"})},
},
"scene_graph": {"dataset": "scene_graph",
"task": {"QA": GQATask},
"evaluation": {"QA": ("text_accuracy", {"metric_name": "text_accuracy", "mode": "search"})},
},
"wiki_graph": {"dataset": "wiki_graph",
"task": {"QA": GQATask},
"evaluation": {"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"ultrachat200k": {"dataset": "ultrachat200k",
"task": {"QA": GQATask},
"evaluation": {"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"wikikg90m": {"dataset": "wikikg90m",
"task": {"default": DefaultLPTask,
"subgraph": SubgraphLPTask,
"default_text": DefaultTextLPTask,
"subgraph_text": SubgraphTextLPTask,
"QA": LQATask},
"evaluation": {"default": ("accuracy", {"metric_name": "accuracy", "num_classes": 1387}),
"QA": ("text_accuracy", {"metric_name": "text_accuracy"})},
},
"webqsp": {"dataset": "webqsp",
"task": {"QA": GQATask},
"evaluation": {"QA": ("text_accuracy", {"metric_name": "text_accuracy", "mode": "search"})},
},
}
def get_dataset(
name: str,
root: Optional[str] = None,
transform: Optional[Callable] = None,
pre_transform: Optional[Callable] = None,
pre_filter: Optional[Callable] = None,
**kwargs) -> TAGDataset:
return DATASET_TO_CLASS_DICT[DATASET_INFOR_DICT[name]["dataset"]](root=root, transform=transform,
pre_transform=pre_transform,
pre_filter=pre_filter, **kwargs)
def get_datasets(names: Union[str, list[str]],
root: Optional[str] = None,
transform: Optional[Callable] = None,