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%20is%20to%20derive%20structured%20representations%20from%20unstructured%20or%20semi-structured%20documents.%20However%2C%20developing%20IE%20models%20is%20complex%20due%20to%20the%20need%20of%20integrating%20several%20subtasks.%20Additionally%2C%20representation%20of%20data%20among%20varied%20tasks%20and%20transforming%20datasets%20into%20task-specific%20model%20inputs%20presents%20further%20challenges.%20To%20streamline%20this%20undertaking%20for%20researchers%2C%20we%20introduce%20PyTorch-IE%2C%20a%20deep-learning-based%20framework%20uniquely%20designed%20to%20enable%20swift%2C%20reproducible%2C%20and%20reusable%20implementations%20of%20IE%20models.%20PyTorch-IE%20offers%20a%20flexible%20data%20model%20capable%20of%20creating%20complex%20data%20structures%20by%20integrating%20interdependent%20layers%20of%20annotations%20derived%20from%20various%20data%20types%2C%20like%20plain%20text%20or%20semi-structured%20text%2C%20and%20even%20images.%20We%20propose%20task%20modules%20to%20decouple%20the%20concerns%20of%20data%20representation%20and%20model-specific%20representations%2C%20thereby%20fostering%20greater%20flexibility%20and%20reusability%20of%20code.%20PyTorch-IE%20also%20extends%20support%20for%20widely%20used%20libraries%20such%20as%20PyTorch-Lightning%20for%20training%2C%20HuggingFace%20datasets%20for%20dataset%20reading%2C%20and%20Hydra%20for%20experiment%20configuration.%20Supplementary%20libraries%20and%20GitHub%20templates%20for%20the%20easy%20setup%20of%20new%20projects%20are%20also%20provided.%20By%20ensuring%20functionality%20and%20versatility%2C%20PyTorch-IE%20provides%20vital%20support%20to%20the%20research%20community%20engaged%20in%20Information%20Extraction.)
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