Datasets:
Size:
1M<n<10M
ArXiv:
Tags:
Document_Understanding
Document_Packet_Splitting
Document_Comprehension
Document_Classification
Document_Recognition
Document_Segmentation
DOI:
License:
File size: 3,490 Bytes
165da3c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # 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
|