README.md

metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: layoutxlm
    results: []

layoutxlm

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

• Loss: 1.5889
• Answer: {'precision': 0.8761904761904762, 'recall': 0.9008567931456548, 'f1': 0.8883524441762222, 'number': 817}
• Header: {'precision': 0.6666666666666666, 'recall': 0.5546218487394958, 'f1': 0.6055045871559633, 'number': 119}
• Question: {'precision': 0.8883968113374667, 'recall': 0.9312906220984215, 'f1': 0.9093381686310064, 'number': 1077}
• Overall Precision: 0.8728
• Overall Recall: 0.8967
• Overall F1: 0.8846
• Overall Accuracy: 0.8115

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• training_steps: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4322	10.53	200	0.9083	{'precision': 0.7704569606801275, 'recall': 0.8873929008567931, 'f1': 0.8248009101251422, 'number': 817}	{'precision': 0.6162790697674418, 'recall': 0.44537815126050423, 'f1': 0.5170731707317073, 'number': 119}	{'precision': 0.866852886405959, 'recall': 0.8644382544103992, 'f1': 0.8656438865643886, 'number': 1077}	0.8134	0.8490	0.8308	0.7863
0.0467	21.05	400	1.2942	{'precision': 0.8496583143507973, 'recall': 0.9130966952264382, 'f1': 0.88023598820059, 'number': 817}	{'precision': 0.6585365853658537, 'recall': 0.453781512605042, 'f1': 0.5373134328358209, 'number': 119}	{'precision': 0.8859964093357271, 'recall': 0.9164345403899722, 'f1': 0.9009584664536742, 'number': 1077}	0.8616	0.8877	0.8745	0.7966
0.015	31.58	600	1.2662	{'precision': 0.8574739281575898, 'recall': 0.9057527539779682, 'f1': 0.880952380952381, 'number': 817}	{'precision': 0.5304347826086957, 'recall': 0.5126050420168067, 'f1': 0.5213675213675214, 'number': 119}	{'precision': 0.8793879387938794, 'recall': 0.9071494893221913, 'f1': 0.8930530164533822, 'number': 1077}	0.8511	0.8833	0.8669	0.8114
0.0081	42.11	800	1.5223	{'precision': 0.8710462287104623, 'recall': 0.8763769889840881, 'f1': 0.8737034777303235, 'number': 817}	{'precision': 0.5882352941176471, 'recall': 0.5882352941176471, 'f1': 0.5882352941176471, 'number': 119}	{'precision': 0.8885844748858448, 'recall': 0.903435468895079, 'f1': 0.8959484346224678, 'number': 1077}	0.8639	0.8738	0.8689	0.8041
0.0033	52.63	1000	1.4361	{'precision': 0.8502304147465438, 'recall': 0.9033047735618115, 'f1': 0.8759643916913946, 'number': 817}	{'precision': 0.6144578313253012, 'recall': 0.42857142857142855, 'f1': 0.504950495049505, 'number': 119}	{'precision': 0.8767605633802817, 'recall': 0.924791086350975, 'f1': 0.9001355625847266, 'number': 1077}	0.8553	0.8867	0.8707	0.8156
0.0026	63.16	1200	1.4994	{'precision': 0.8615560640732265, 'recall': 0.9216646266829865, 'f1': 0.8905972797161442, 'number': 817}	{'precision': 0.5981308411214953, 'recall': 0.5378151260504201, 'f1': 0.5663716814159291, 'number': 119}	{'precision': 0.8945454545454545, 'recall': 0.9136490250696379, 'f1': 0.9039963252181902, 'number': 1077}	0.8654	0.8947	0.8798	0.8208
0.0016	73.68	1400	1.6091	{'precision': 0.858139534883721, 'recall': 0.9033047735618115, 'f1': 0.8801431127012522, 'number': 817}	{'precision': 0.5980392156862745, 'recall': 0.5126050420168067, 'f1': 0.5520361990950226, 'number': 119}	{'precision': 0.8947849954254345, 'recall': 0.9080779944289693, 'f1': 0.9013824884792625, 'number': 1077}	0.8647	0.8828	0.8736	0.8167
0.0009	84.21	1600	1.6010	{'precision': 0.859122401847575, 'recall': 0.9106487148102815, 'f1': 0.8841354723707664, 'number': 817}	{'precision': 0.6741573033707865, 'recall': 0.5042016806722689, 'f1': 0.576923076923077, 'number': 119}	{'precision': 0.8882931188561215, 'recall': 0.9229340761374187, 'f1': 0.9052823315118397, 'number': 1077}	0.8669	0.8932	0.8799	0.8049
0.0006	94.74	1800	1.5889	{'precision': 0.8761904761904762, 'recall': 0.9008567931456548, 'f1': 0.8883524441762222, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.5546218487394958, 'f1': 0.6055045871559633, 'number': 119}	{'precision': 0.8883968113374667, 'recall': 0.9312906220984215, 'f1': 0.9093381686310064, 'number': 1077}	0.8728	0.8967	0.8846	0.8115
0.0004	105.26	2000	1.6126	{'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817}	{'precision': 0.6538461538461539, 'recall': 0.5714285714285714, 'f1': 0.6098654708520179, 'number': 119}	{'precision': 0.894404332129964, 'recall': 0.9201485608170845, 'f1': 0.9070938215102976, 'number': 1077}	0.8695	0.8937	0.8814	0.8127
0.0004	115.79	2200	1.6606	{'precision': 0.8403648802736602, 'recall': 0.9020807833537332, 'f1': 0.8701298701298701, 'number': 817}	{'precision': 0.6509433962264151, 'recall': 0.5798319327731093, 'f1': 0.6133333333333333, 'number': 119}	{'precision': 0.8884826325411335, 'recall': 0.9025069637883009, 'f1': 0.8954398894518655, 'number': 1077}	0.8560	0.8833	0.8694	0.7906
0.0002	126.32	2400	1.6619	{'precision': 0.8378684807256236, 'recall': 0.9045287637698899, 'f1': 0.8699234844025897, 'number': 817}	{'precision': 0.6836734693877551, 'recall': 0.5630252100840336, 'f1': 0.6175115207373272, 'number': 119}	{'precision': 0.881981981981982, 'recall': 0.9090064995357474, 'f1': 0.8952903520804755, 'number': 1077}	0.8541	0.8867	0.8701	0.7929

Framework versions

• Transformers 4.30.2
• Pytorch 2.1.0.dev20230523+cu117
• Datasets 2.13.0
• Tokenizers 0.13.3