OCR-LayoutLMv3

This model is a fine-tuned version of microsoft/layoutlmv3-base on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

• Loss: 0.9788
• Precision: 0.8989
• Recall: 0.9051
• F1: 0.9020
• Accuracy: 0.8404

Model description

LayoutLMv3 is a pre-trained multimodal Transformer for Document AI with unified text and image masking. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model. For example, LayoutLMv3 can be fine-tuned for both text-centric tasks, including form understanding, receipt understanding, and document visual question answering, and image-centric tasks such as document image classification and document layout analysis.

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking Yupan Huang, Tengchao Lv, Lei Cui, Yutong Lu, Furu Wei, Preprint 2022.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 1e-05
• train_batch_size: 2
• eval_batch_size: 2
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2000

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.33	100	0.6966	0.7418	0.8063	0.7727	0.7801
No log	2.67	200	0.5767	0.8104	0.8644	0.8365	0.8117
No log	4.0	300	0.5355	0.8246	0.8852	0.8539	0.8295
No log	5.33	400	0.5240	0.8706	0.8922	0.8813	0.8427
0.5326	6.67	500	0.6337	0.8528	0.8778	0.8651	0.8260
0.5326	8.0	600	0.6870	0.8698	0.8828	0.8762	0.8240
0.5326	9.33	700	0.6584	0.8723	0.9061	0.8889	0.8342
0.5326	10.67	800	0.7186	0.8868	0.9031	0.8949	0.8335
0.5326	12.0	900	0.6822	0.9040	0.9076	0.9058	0.8526
0.1248	13.33	1000	0.7042	0.8872	0.9021	0.8946	0.8511
0.1248	14.67	1100	0.7920	0.9027	0.9036	0.9032	0.8480
0.1248	16.0	1200	0.8052	0.8964	0.9151	0.9056	0.8389
0.1248	17.33	1300	0.8932	0.8995	0.9066	0.9030	0.8329
0.1248	18.67	1400	0.8728	0.8950	0.9061	0.9005	0.8398
0.0442	20.0	1500	0.9051	0.8960	0.9116	0.9037	0.8347
0.0442	21.33	1600	0.9587	0.8947	0.9031	0.8989	0.8401
0.0442	22.67	1700	0.9822	0.9042	0.9046	0.9044	0.8389
0.0442	24.0	1800	0.9734	0.9043	0.9061	0.9052	0.8391
0.0442	25.33	1900	0.9842	0.9042	0.9091	0.9066	0.8410
0.0225	26.67	2000	0.9788	0.8989	0.9051	0.9020	0.8404

Framework versions

• Transformers 4.25.0.dev0
• Pytorch 1.12.1
• Datasets 2.6.1
• Tokenizers 0.13.1