We lifted 17 functions out of this paper's own repositories and ran 14 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| DS4SD/MarkushGrapher | pwc_unofficial | 11 of 14 |
| copy not recorded | — | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| custom_huber | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/losses.py code served (permissive licence) · get_code("f587be8f27266175") |
| custom_huber2 | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/losses.py code served (permissive licence) · get_code("8ad08bf63e399fe5") |
| default | Ran | this paper's copy was not recorded; identical code first harvested from ashen-sensored/sd_webui_SAG pointer only · get_code("424012cb37b31172") |
| estimate_word_width | Ran | DS4SD/MarkushGrapher/markushgrapher/core/common/data_preprocessing.py code served (permissive licence) · get_code("68402efebc760365") |
| exists | Ran | this paper's copy was not recorded; identical code first harvested from ThomasMrY/VCT pointer only · get_code("aa5486a3650902d8") |
| get_last_checkpoint | Ran | DS4SD/MarkushGrapher/markushgrapher/core/common/utils.py code served (permissive licence) · get_code("9f1130d32c6430f8") |
| get_linear_with_fact_schedule_with_warmup | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/optimization.py code served (permissive licence) · get_code("a3b662bbc3a23327") |
| get_scheduler | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/optimization.py code served (permissive licence) · get_code("e78d0e6d79459413") |
| huber_loss | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/losses.py code served (permissive licence) · get_code("a83a110a9551382d") |
| pad_sequence_native | Ran | DS4SD/MarkushGrapher/markushgrapher/core/trainers/data_collator.py code served (permissive licence) · get_code("4d78a796062fd89b") |
| setup_logging | Ran | DS4SD/MarkushGrapher/markushgrapher/core/common/begin.py code served (permissive licence) · get_code("fbd45e87bae891a7") |
| split_bounding_box_for_words | Ran | DS4SD/MarkushGrapher/markushgrapher/core/common/data_preprocessing.py code served (permissive licence) · get_code("084ee3628fc5d124") |
| split_sentence_into_words | Ran | DS4SD/MarkushGrapher/markushgrapher/core/common/data_preprocessing.py code served (permissive licence) · get_code("32b4122ad5350b54") |
| uniq | Ran | this paper's copy was not recorded; identical code first harvested from JingWu321/MUNBa pointer only · get_code("9a299fe5ae09e407") |
| calculate_iou | Not yet run | DS4SD/MarkushGrapher/markushgrapher/core/common/utils.py code served (permissive licence) · get_code("ea1bb4ac7f48a7b5") |
| clamp | Not yet run | DS4SD/MarkushGrapher/markushgrapher/core/common/utils.py code served (permissive licence) · get_code("59eacee354fa7772") |
| last_checkpoint | Not yet run | DS4SD/MarkushGrapher/markushgrapher/core/common/begin.py code served (permissive licence) · get_code("5b942743f6a5251f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose Universal Document Processing (UDOP), a foundation Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation. With a novel Vision-Text-Layout Transformer, UDOP unifies pretraining and multi-domain downstream tasks into a prompt-based sequence generation scheme. UDOP is pretrained on both large-scale unlabeled document corpora using innovative self-supervised objectives and diverse labeled data. UDOP also learns to generate document images from text and layout modalities via masked image reconstruction. To the best of our knowledge, this is the first time in the field of document AI that one model simultaneously achieves high-quality neural document editing and content customization. Our method sets the state-of-the-art on 8 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites. UDOP ranks first on the leaderboard of the Document Understanding Benchmark.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2212.02623")
get_code_for_paper("2212.02623")
have("2212.02623")
Connect an agent — have() is free.