Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
| # Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. | |
| # SPDX-License-Identifier: CC-BY-NC-4.0 | |
| from typing import List, Dict | |
| import uuid | |
| from loguru import logger | |
| from models import DocumentAsset, SplicedDocument, SourceDocument, GroundTruthPage | |
| from services.shuffle_strategies.base_strategy import BaseStrategy | |
| class MonoRand(BaseStrategy): | |
| """DocSplit-Mono-Rand: Single category document pages randomization. | |
| Similar to Mono-Seq but shuffles pages from concatenated documents. | |
| Tests models' robustness to page-level disruptions common in manual | |
| document assembly, requiring boundary detection and page sequence reconstruction. | |
| """ | |
| def generate( | |
| self, | |
| documents_by_type: Dict[str, List[DocumentAsset]], | |
| doc_names_for_split: Dict[str, List[str]], | |
| num_spliced_docs: int | |
| ) -> List[SplicedDocument]: | |
| available_docs = self._get_available_docs(documents_by_type, doc_names_for_split) | |
| # Filter out language category for large datasets (min_pages >= 20) | |
| if self.min_pages >= 20 and "language" in available_docs: | |
| del available_docs["language"] | |
| spliced_documents = [] | |
| for i in range(num_spliced_docs): | |
| # Pick a random document type | |
| doc_type = self.rng.choice(list(available_docs.keys())) | |
| # Set target page count | |
| target_pages = self.rng.randint(self.min_pages, self.max_pages) | |
| # Keep adding entire documents until reaching target | |
| source_documents = [] | |
| ground_truth = [] | |
| current_page = 1 | |
| used_docs = set() | |
| while current_page - 1 < target_pages: | |
| available = [d for d in available_docs[doc_type] | |
| if d.doc_name not in used_docs and d.page_count <= self.max_pages] | |
| if not available: | |
| break | |
| doc = self.rng.choice(available) | |
| used_docs.add(doc.doc_name) | |
| if current_page - 1 + doc.page_count > self.max_pages: | |
| continue | |
| # Add all pages but shuffle their order | |
| pages = list(range(1, doc.page_count + 1)) | |
| self.rng.shuffle(pages) | |
| source_documents.append(SourceDocument( | |
| doc_type=doc.doc_type, | |
| doc_name=doc.doc_name, | |
| pages=pages | |
| )) | |
| for source_page in pages: | |
| ground_truth.append(GroundTruthPage( | |
| page_num=current_page, | |
| doc_type=doc.doc_type, | |
| source_doc=doc.doc_name, | |
| source_page=source_page | |
| )) | |
| current_page += 1 | |
| if current_page - 1 >= self.min_pages: | |
| spliced_doc = SplicedDocument( | |
| spliced_doc_id=str(uuid.uuid4()), | |
| source_documents=source_documents, | |
| ground_truth=ground_truth, | |
| total_pages=current_page - 1 | |
| ) | |
| spliced_documents.append(spliced_doc) | |
| logger.info(f"Generated {len(spliced_documents)} DocSplit-Mono-Rand documents") | |
| return spliced_documents | |