metadata

license: mit
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: 1.8731
Answer: {'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817}
Header: {'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}
Question: {'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077}
Overall Precision: 0.8792
Overall Recall: 0.8857
Overall F1: 0.8825
Overall Accuracy: 0.7976

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.4323	10.53	200	1.0423	{'precision': 0.8369195922989807, 'recall': 0.9045287637698899, 'f1': 0.8694117647058823, 'number': 817}	{'precision': 0.5405405405405406, 'recall': 0.5042016806722689, 'f1': 0.5217391304347826, 'number': 119}	{'precision': 0.8869323447636701, 'recall': 0.8885793871866295, 'f1': 0.8877551020408162, 'number': 1077}	0.8471	0.8723	0.8595	0.7981
0.045	21.05	400	1.2757	{'precision': 0.8435374149659864, 'recall': 0.9106487148102815, 'f1': 0.8758092995879929, 'number': 817}	{'precision': 0.5795454545454546, 'recall': 0.42857142857142855, 'f1': 0.49275362318840576, 'number': 119}	{'precision': 0.8626943005181347, 'recall': 0.9275766016713092, 'f1': 0.8939597315436242, 'number': 1077}	0.8430	0.8912	0.8665	0.8026
0.0133	31.58	600	1.4887	{'precision': 0.8632075471698113, 'recall': 0.8959608323133414, 'f1': 0.8792792792792793, 'number': 817}	{'precision': 0.6020408163265306, 'recall': 0.4957983193277311, 'f1': 0.543778801843318, 'number': 119}	{'precision': 0.8791887125220459, 'recall': 0.9257195914577531, 'f1': 0.9018543645409318, 'number': 1077}	0.8596	0.8882	0.8737	0.7983
0.0051	42.11	800	1.7382	{'precision': 0.8601645123384254, 'recall': 0.8959608323133414, 'f1': 0.8776978417266187, 'number': 817}	{'precision': 0.5636363636363636, 'recall': 0.5210084033613446, 'f1': 0.5414847161572053, 'number': 119}	{'precision': 0.9032558139534884, 'recall': 0.9015784586815228, 'f1': 0.9024163568773235, 'number': 1077}	0.8669	0.8768	0.8718	0.7925
0.004	52.63	1000	1.7599	{'precision': 0.8307349665924276, 'recall': 0.9130966952264382, 'f1': 0.8699708454810495, 'number': 817}	{'precision': 0.6039603960396039, 'recall': 0.5126050420168067, 'f1': 0.5545454545454545, 'number': 119}	{'precision': 0.8939256572982774, 'recall': 0.9155060352831941, 'f1': 0.9045871559633027, 'number': 1077}	0.8530	0.8907	0.8714	0.7941
0.002	63.16	1200	1.8409	{'precision': 0.8312985571587126, 'recall': 0.9167686658506732, 'f1': 0.8719441210710128, 'number': 817}	{'precision': 0.6074766355140186, 'recall': 0.5462184873949579, 'f1': 0.575221238938053, 'number': 119}	{'precision': 0.8814949863263446, 'recall': 0.8978644382544104, 'f1': 0.8896044158233671, 'number': 1077}	0.8461	0.8847	0.8650	0.7876
0.0013	73.68	1400	1.7795	{'precision': 0.81445523193096, 'recall': 0.9241126070991432, 'f1': 0.8658256880733943, 'number': 817}	{'precision': 0.6237623762376238, 'recall': 0.5294117647058824, 'f1': 0.5727272727272728, 'number': 119}	{'precision': 0.888785046728972, 'recall': 0.883008356545961, 'f1': 0.8858872845831393, 'number': 1077}	0.8432	0.8788	0.8606	0.7934
0.0011	84.21	1600	1.8386	{'precision': 0.8338833883388339, 'recall': 0.9277845777233782, 'f1': 0.8783314020857474, 'number': 817}	{'precision': 0.6597938144329897, 'recall': 0.5378151260504201, 'f1': 0.5925925925925926, 'number': 119}	{'precision': 0.8943985307621671, 'recall': 0.904363974001857, 'f1': 0.8993536472760849, 'number': 1077}	0.8573	0.8922	0.8744	0.7945
0.0048	94.74	1800	1.8664	{'precision': 0.8589595375722543, 'recall': 0.9094247246022031, 'f1': 0.8834720570749108, 'number': 817}	{'precision': 0.6504854368932039, 'recall': 0.5630252100840336, 'f1': 0.6036036036036037, 'number': 119}	{'precision': 0.9003656307129799, 'recall': 0.914577530176416, 'f1': 0.9074159373560571, 'number': 1077}	0.8705	0.8917	0.8810	0.7927
0.0004	105.26	2000	1.8672	{'precision': 0.8634772462077013, 'recall': 0.9057527539779682, 'f1': 0.8841099163679809, 'number': 817}	{'precision': 0.7093023255813954, 'recall': 0.5126050420168067, 'f1': 0.5951219512195123, 'number': 119}	{'precision': 0.8923076923076924, 'recall': 0.9155060352831941, 'f1': 0.9037580201649862, 'number': 1077}	0.8726	0.8877	0.8801	0.7953
0.0005	115.79	2200	1.8731	{'precision': 0.8688915375446961, 'recall': 0.8922888616891065, 'f1': 0.8804347826086957, 'number': 817}	{'precision': 0.638095238095238, 'recall': 0.5630252100840336, 'f1': 0.5982142857142857, 'number': 119}	{'precision': 0.9105166051660517, 'recall': 0.9164345403899722, 'f1': 0.9134659879685332, 'number': 1077}	0.8792	0.8857	0.8825	0.7976
0.0002	126.32	2400	1.9408	{'precision': 0.8408071748878924, 'recall': 0.9179926560587516, 'f1': 0.8777062609713283, 'number': 817}	{'precision': 0.6310679611650486, 'recall': 0.5462184873949579, 'f1': 0.5855855855855856, 'number': 119}	{'precision': 0.9091760299625468, 'recall': 0.9015784586815228, 'f1': 0.9053613053613054, 'number': 1077}	0.8657	0.8872	0.8763	0.7935

Framework versions

Transformers 4.24.0
Pytorch 1.12.1+cu113
Datasets 2.7.1
Tokenizers 0.13.2