We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| nunenuh/layoutlm.pytorch | reimplementation | 2 of 2 |
| copy not recorded | — | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| collate_fn | Ran | nunenuh/layoutlm.pytorch/unilm/layoutlm/examples/seq_labeling/run_seq_labeling.py code served (permissive licence) · get_code("f2858c5b1e4ffe3a") |
| get_labels | Ran | nunenuh/layoutlm.pytorch/unilm/layoutlm/examples/seq_labeling/run_seq_labeling.py code served (permissive licence) · get_code("292d7b1eea96d516") |
| simple_accuracy | Not yet run | this paper's copy was not recorded; identical code first harvested from ink-usc/expl-refinement pointer only · get_code("3c241ecfe3749a6d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Pre-training techniques have been verified successfully in a variety of NLP tasks in recent years. Despite the widespread use of pre-training models for NLP applications, they almost exclusively focus on text-level manipulation, while neglecting layout and style information that is vital for document image understanding. In this paper, we propose the \textbf{LayoutLM} to jointly model interactions between text and layout information across scanned document images, which is beneficial for a great number of real-world document image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words' visual information into LayoutLM. To the best of our knowledge, this is the first time that text and layout are jointly learned in a single framework for document-level pre-training. It achieves new state-of-the-art results in several downstream tasks, including form understanding (from 70.72 to 79.27), receipt understanding (from 94.02 to 95.24) and document image classification (from 93.07 to 94.42). The code and pre-trained LayoutLM models are publicly available at \url{https://aka.ms/layoutlm}.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1912.13318")
get_code_for_paper("1912.13318")
have("1912.13318")
Connect an agent — have() is free.