lilt-en-funsd

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

• Loss: 1.6681
• Answer: {'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}
• Header: {'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}
• Question: {'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}
• Overall Precision: 0.8760
• Overall Recall: 0.8882
• Overall F1: 0.8821
• Overall Accuracy: 0.8030

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
0.431	10.5263	200	1.0598	{'precision': 0.8017718715393134, 'recall': 0.8861689106487148, 'f1': 0.841860465116279, 'number': 817}	{'precision': 0.4228187919463087, 'recall': 0.5294117647058824, 'f1': 0.47014925373134325, 'number': 119}	{'precision': 0.8755935422602089, 'recall': 0.8560817084493965, 'f1': 0.8657276995305164, 'number': 1077}	0.8119	0.8490	0.8300	0.7774
0.0542	21.0526	400	1.2173	{'precision': 0.8382352941176471, 'recall': 0.9069767441860465, 'f1': 0.8712522045855379, 'number': 817}	{'precision': 0.5350877192982456, 'recall': 0.5126050420168067, 'f1': 0.5236051502145922, 'number': 119}	{'precision': 0.8882521489971347, 'recall': 0.8635097493036211, 'f1': 0.8757062146892656, 'number': 1077}	0.8469	0.8604	0.8536	0.8016
0.014	31.5789	600	1.2955	{'precision': 0.8415051311288484, 'recall': 0.9033047735618115, 'f1': 0.8713105076741442, 'number': 817}	{'precision': 0.6210526315789474, 'recall': 0.4957983193277311, 'f1': 0.5514018691588785, 'number': 119}	{'precision': 0.8972477064220183, 'recall': 0.9080779944289693, 'f1': 0.9026303645592985, 'number': 1077}	0.8608	0.8818	0.8712	0.8160
0.0064	42.1053	800	1.2848	{'precision': 0.8696186961869619, 'recall': 0.8653610771113831, 'f1': 0.8674846625766871, 'number': 817}	{'precision': 0.5193798449612403, 'recall': 0.5630252100840336, 'f1': 0.5403225806451614, 'number': 119}	{'precision': 0.858274647887324, 'recall': 0.9052924791086351, 'f1': 0.8811568007230005, 'number': 1077}	0.8417	0.8689	0.8550	0.8222
0.0037	52.6316	1000	1.5983	{'precision': 0.8530751708428246, 'recall': 0.9167686658506732, 'f1': 0.8837758112094395, 'number': 817}	{'precision': 0.5658914728682171, 'recall': 0.6134453781512605, 'f1': 0.5887096774193549, 'number': 119}	{'precision': 0.8946360153256705, 'recall': 0.8672237697307336, 'f1': 0.8807166430928807, 'number': 1077}	0.8562	0.8723	0.8642	0.7916
0.0034	63.1579	1200	1.5936	{'precision': 0.85, 'recall': 0.9155446756425949, 'f1': 0.881555686505598, 'number': 817}	{'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119}	{'precision': 0.8912442396313364, 'recall': 0.8978644382544104, 'f1': 0.8945420906567992, 'number': 1077}	0.8570	0.8813	0.8690	0.8102
0.0021	73.6842	1400	1.4765	{'precision': 0.8558139534883721, 'recall': 0.9008567931456548, 'f1': 0.877757901013715, 'number': 817}	{'precision': 0.5619047619047619, 'recall': 0.4957983193277311, 'f1': 0.5267857142857143, 'number': 119}	{'precision': 0.885036496350365, 'recall': 0.9006499535747446, 'f1': 0.892774965485504, 'number': 1077}	0.8564	0.8768	0.8665	0.8010
0.0009	84.2105	1600	1.6681	{'precision': 0.8778173190984578, 'recall': 0.9057527539779682, 'f1': 0.891566265060241, 'number': 817}	{'precision': 0.6036036036036037, 'recall': 0.5630252100840336, 'f1': 0.582608695652174, 'number': 119}	{'precision': 0.9024839006439742, 'recall': 0.9108635097493036, 'f1': 0.9066543438077633, 'number': 1077}	0.8760	0.8882	0.8821	0.8030
0.0003	94.7368	1800	1.6379	{'precision': 0.8595238095238096, 'recall': 0.8837209302325582, 'f1': 0.8714544357272178, 'number': 817}	{'precision': 0.5929203539823009, 'recall': 0.5630252100840336, 'f1': 0.5775862068965517, 'number': 119}	{'precision': 0.896709323583181, 'recall': 0.9108635097493036, 'f1': 0.9037309995393827, 'number': 1077}	0.8647	0.8793	0.8719	0.7986
0.0002	105.2632	2000	1.7186	{'precision': 0.8644859813084113, 'recall': 0.9057527539779682, 'f1': 0.8846383741781233, 'number': 817}	{'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119}	{'precision': 0.8921658986175115, 'recall': 0.8987929433611885, 'f1': 0.8954671600370029, 'number': 1077}	0.8631	0.8798	0.8713	0.7978
0.0003	115.7895	2200	1.6765	{'precision': 0.8690476190476191, 'recall': 0.8935128518971848, 'f1': 0.8811104405552203, 'number': 817}	{'precision': 0.5726495726495726, 'recall': 0.5630252100840336, 'f1': 0.5677966101694915, 'number': 119}	{'precision': 0.8934802571166207, 'recall': 0.903435468895079, 'f1': 0.8984302862419206, 'number': 1077}	0.8651	0.8793	0.8721	0.8000
0.0003	126.3158	2400	1.7309	{'precision': 0.8817852834740652, 'recall': 0.8947368421052632, 'f1': 0.888213851761847, 'number': 817}	{'precision': 0.5675675675675675, 'recall': 0.5294117647058824, 'f1': 0.5478260869565218, 'number': 119}	{'precision': 0.8914233576642335, 'recall': 0.9071494893221913, 'f1': 0.8992176714219972, 'number': 1077}	0.8698	0.8798	0.8748	0.7959

Framework versions

• Transformers 4.41.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1