metadata

license: mit
base_model: SCUT-DLVCLab/lilt-roberta-en-base
tags:
  - generated_from_trainer
datasets:
  - funsd-layoutlmv3
model-index:
  - name: lilt-en-funsd
    results: []

lilt-en-funsd

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: 1.7487
Answer: {'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}
Header: {'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}
Question: {'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}
Overall Precision: 0.8731
Overall Recall: 0.8952
Overall F1: 0.8840
Overall Accuracy: 0.7977

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
0.4352	10.53	200	0.9574	{'precision': 0.8385167464114832, 'recall': 0.8580171358629131, 'f1': 0.8481548699334543, 'number': 817}	{'precision': 0.5673076923076923, 'recall': 0.4957983193277311, 'f1': 0.5291479820627802, 'number': 119}	{'precision': 0.8394534585824082, 'recall': 0.9127205199628597, 'f1': 0.8745551601423487, 'number': 1077}	0.8257	0.8659	0.8453	0.7896
0.0467	21.05	400	1.3446	{'precision': 0.8343057176196033, 'recall': 0.8751529987760098, 'f1': 0.8542413381123058, 'number': 817}	{'precision': 0.5789473684210527, 'recall': 0.46218487394957986, 'f1': 0.514018691588785, 'number': 119}	{'precision': 0.8543859649122807, 'recall': 0.904363974001857, 'f1': 0.8786648624267027, 'number': 1077}	0.8337	0.8664	0.8497	0.7969
0.0125	31.58	600	1.4274	{'precision': 0.8556461001164144, 'recall': 0.8996328029375765, 'f1': 0.8770883054892601, 'number': 817}	{'precision': 0.568, 'recall': 0.5966386554621849, 'f1': 0.5819672131147541, 'number': 119}	{'precision': 0.8916211293260473, 'recall': 0.9090064995357474, 'f1': 0.9002298850574713, 'number': 1077}	0.8573	0.8867	0.8718	0.8010
0.0071	42.11	800	1.4147	{'precision': 0.865265760197775, 'recall': 0.8567931456548348, 'f1': 0.8610086100861009, 'number': 817}	{'precision': 0.6888888888888889, 'recall': 0.5210084033613446, 'f1': 0.5933014354066986, 'number': 119}	{'precision': 0.8798206278026905, 'recall': 0.9108635097493036, 'f1': 0.8950729927007299, 'number': 1077}	0.8654	0.8659	0.8657	0.8055
0.0067	52.63	1000	1.5877	{'precision': 0.8747016706443914, 'recall': 0.8971848225214198, 'f1': 0.8858006042296073, 'number': 817}	{'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}	{'precision': 0.8936363636363637, 'recall': 0.9127205199628597, 'f1': 0.9030776297657327, 'number': 1077}	0.8709	0.8847	0.8778	0.8080
0.003	63.16	1200	1.5406	{'precision': 0.875, 'recall': 0.8996328029375765, 'f1': 0.8871454435727218, 'number': 817}	{'precision': 0.584070796460177, 'recall': 0.5546218487394958, 'f1': 0.5689655172413793, 'number': 119}	{'precision': 0.8858695652173914, 'recall': 0.9080779944289693, 'f1': 0.8968363136176066, 'number': 1077}	0.8649	0.8838	0.8742	0.8183
0.0011	73.68	1400	1.6131	{'precision': 0.8686987104337632, 'recall': 0.9069767441860465, 'f1': 0.8874251497005988, 'number': 817}	{'precision': 0.7011494252873564, 'recall': 0.5126050420168067, 'f1': 0.5922330097087377, 'number': 119}	{'precision': 0.8821966341895483, 'recall': 0.924791086350975, 'f1': 0.9029918404351769, 'number': 1077}	0.8690	0.8932	0.8809	0.8111
0.0008	84.21	1600	1.7487	{'precision': 0.8851674641148325, 'recall': 0.9057527539779682, 'f1': 0.8953418027828192, 'number': 817}	{'precision': 0.65625, 'recall': 0.5294117647058824, 'f1': 0.586046511627907, 'number': 119}	{'precision': 0.8825088339222615, 'recall': 0.9275766016713092, 'f1': 0.9044816659121775, 'number': 1077}	0.8731	0.8952	0.8840	0.7977
0.0007	94.74	1800	1.8317	{'precision': 0.8605990783410138, 'recall': 0.9143206854345165, 'f1': 0.886646884272997, 'number': 817}	{'precision': 0.6095238095238096, 'recall': 0.5378151260504201, 'f1': 0.5714285714285715, 'number': 119}	{'precision': 0.9026876737720111, 'recall': 0.904363974001857, 'f1': 0.9035250463821892, 'number': 1077}	0.8699	0.8867	0.8782	0.7934
0.0005	105.26	2000	1.8600	{'precision': 0.8669778296382731, 'recall': 0.9094247246022031, 'f1': 0.8876941457586618, 'number': 817}	{'precision': 0.6428571428571429, 'recall': 0.5294117647058824, 'f1': 0.5806451612903226, 'number': 119}	{'precision': 0.8928247048138056, 'recall': 0.9127205199628597, 'f1': 0.9026629935720845, 'number': 1077}	0.8701	0.8887	0.8793	0.7897
0.0005	115.79	2200	1.7672	{'precision': 0.8781362007168458, 'recall': 0.8996328029375765, 'f1': 0.8887545344619106, 'number': 817}	{'precision': 0.6568627450980392, 'recall': 0.5630252100840336, 'f1': 0.6063348416289592, 'number': 119}	{'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077}	0.8756	0.8882	0.8819	0.8074
0.0002	126.32	2400	1.8044	{'precision': 0.8604382929642446, 'recall': 0.9130966952264382, 'f1': 0.8859857482185273, 'number': 817}	{'precision': 0.6391752577319587, 'recall': 0.5210084033613446, 'f1': 0.5740740740740741, 'number': 119}	{'precision': 0.9022140221402214, 'recall': 0.9080779944289693, 'f1': 0.9051365108745951, 'number': 1077}	0.8721	0.8872	0.8796	0.8000

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3