Awesome

Multi-scale Cell-based Layout Representation for Document Understanding

Model

We use models from transformers. https://huggingface.co/models

• LayoutLM
  • Base: microsoft/layoutlm-base-uncased
  • Large: microsoft/layoutlm-base-uncased
• LayoutLMv2
  • Base: microsoft/layoutlmv2-base-uncased
  • Large: microsoft/layoutlmv2-large-uncased
• LayoutLMv3
  • Base: microsoft/layoutlmv3-base
  • Large: microsoft/layoutlmv3-large

Dataset

• FUNSD
  • https://guillaumejaume.github.io/FUNSD/
• CORD
  • https://github.com/clovaai/cord
• RVL-CDIP
  • https://adamharley.com/rvl-cdip/

Enviroment

Container1

will be released soon.

Anaconda env

• v3
  • For using layoutlmv3 Model in named-entity recognition
  • For document classification task
• layoutlmft
  • For using layoutlmv2 and layoutlmv2 in named-entity recognition

Results

Commands

Named-entity Recognition (NER)

LayoutLMv1

• CODE DIR: layoutlmft/
• CONTAINER: Container1
• ENV: layoutlmft
• DATASET: FUNSD

python -m torch.distributed.launch --nproc_per_node=1 
--master_port 44398 
examples/run_funsd.py         
--model_name_or_path microsoft/layoutlm-base-uncased        
--output_dir output/         
--do_train         
--do_predict         
--max_steps 5000         
--warmup_ratio 0.1         
--fp16   
--per_device_train_batch_size 4

LayoutLMv2

• CODE DIR: layoutlmft/
• CONTAINER: Container1
• ENV: layoutlmft
• DATASET: FUNSD

python -m torch.distributed.launch --nproc_per_node=1 
--master_port 24398 
examples/run_funsd.py         
--model_name_or_path microsoft/layoutlmv2-large-uncased         
--output_dir output/    
--do_train         
--do_predict         
--max_steps 2000         
--warmup_ratio 0.1         
--fp16   
--overwrite_output_dir   
--per_device_train_batch_size 4

LayoutLMv3

• CODE DIR: layoutlmv3/
• CONTAINER: Container1
• ENV: v3
• DATASET: FUNSD, CORD

python -m torch.distributed.launch   
--nproc_per_node=1 
--master_port 4398 
examples/run_funsd_cord.py   
--dataset_name [funsd or cord]  
--do_train 
--do_eval   
--model_name_or_path microsoft/layoutlmv3-base   
--output_dir output/
--segment_level_layout 1 
--visual_embed 1 
--input_size 224  
--max_steps 1000 
--save_steps -1 
--evaluation_strategy steps 
--eval_steps 1000   
--learning_rate 1e-5 
--per_device_train_batch_size 8 
--gradient_accumulation_steps 1   
--dataloader_num_workers 1   
--overwrite_output_dir 

Document Classification

LayoutLMv1

• CODE DIR: layoutlm/deprecated/examples/classification
• CONTAINER: Container1
• ENV: v3
• DATASET: RVL-CDIP

python run_classification.py  
--data_dir  [datasetPath]   
--model_type layoutlm                               
--model_name_or_path ~/dev/Models/LayoutLM/layoutlm-base-uncased   
--output_dir output/
--do_lower_case 
--max_seq_length 512  
--do_train 
--do_eval 
--num_train_epochs 40.0 
--logging_steps 5000 
--save_steps 5000 
--per_gpu_train_batch_size 16 
--per_gpu_eval_batch_size 16 
--evaluate_during_training 
--fp16 --data_level 1

LayoutLMv3

• CODE DIR: layoutlm/deprecated/examples/classification
• CONTAINER: Container1
• ENV: v3
• DATASET: RVL-CDIP

python run_classification.py  
--data_dir  [datasetPath]                              
--model_type v3                               
--model_name_or_path microsoft/layoutlmv3-base                                                            
--do_lower_case                               
--max_seq_length 512                               
--do_train                                                            
--num_train_epochs 40.0                               
--logging_steps 5000                               
--save_steps 5000                               
--per_gpu_train_batch_size 2                               
--per_gpu_eval_batch_size 2                               
--evaluate_during_training

Reference

We use some codes from https://github.com/microsoft/unilm.