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: 1.7110
• Answer: {'precision': 0.8460661345496009, 'recall': 0.9082007343941249, 'f1': 0.8760330578512396, 'number': 817}
• Header: {'precision': 0.6470588235294118, 'recall': 0.5546218487394958, 'f1': 0.5972850678733032, 'number': 119}
• Question: {'precision': 0.9019248395967002, 'recall': 0.9136490250696379, 'f1': 0.9077490774907748, 'number': 1077}
• Overall Precision: 0.8657
• Overall Recall: 0.8902
• Overall F1: 0.8778
• Overall Accuracy: 0.7988

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
0.4082	10.5263	200	1.0891	{'precision': 0.8286384976525821, 'recall': 0.8641370869033048, 'f1': 0.8460155781905333, 'number': 817}	{'precision': 0.4550898203592814, 'recall': 0.6386554621848739, 'f1': 0.5314685314685315, 'number': 119}	{'precision': 0.8792134831460674, 'recall': 0.871866295264624, 'f1': 0.8755244755244757, 'number': 1077}	0.8246	0.8549	0.8395	0.7758
0.0489	21.0526	400	1.1864	{'precision': 0.8470588235294118, 'recall': 0.8812729498164015, 'f1': 0.8638272345530894, 'number': 817}	{'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}	{'precision': 0.8630377524143986, 'recall': 0.9127205199628597, 'f1': 0.8871841155234657, 'number': 1077}	0.8441	0.8768	0.8601	0.8014
0.0135	31.5789	600	1.4159	{'precision': 0.8746928746928747, 'recall': 0.8714810281517748, 'f1': 0.8730839975475169, 'number': 817}	{'precision': 0.59375, 'recall': 0.4789915966386555, 'f1': 0.5302325581395348, 'number': 119}	{'precision': 0.8701964133219471, 'recall': 0.9461467038068709, 'f1': 0.9065836298932384, 'number': 1077}	0.8592	0.8882	0.8735	0.8040
0.007	42.1053	800	1.4263	{'precision': 0.8548199767711963, 'recall': 0.9008567931456548, 'f1': 0.8772348033373063, 'number': 817}	{'precision': 0.6138613861386139, 'recall': 0.5210084033613446, 'f1': 0.5636363636363637, 'number': 119}	{'precision': 0.8946412352406903, 'recall': 0.914577530176416, 'f1': 0.9044995408631772, 'number': 1077}	0.8643	0.8857	0.8749	0.8061
0.0039	52.6316	1000	1.6051	{'precision': 0.8764845605700713, 'recall': 0.9033047735618115, 'f1': 0.8896925858951176, 'number': 817}	{'precision': 0.5323741007194245, 'recall': 0.6218487394957983, 'f1': 0.5736434108527132, 'number': 119}	{'precision': 0.8847209515096066, 'recall': 0.8978644382544104, 'f1': 0.8912442396313364, 'number': 1077}	0.8578	0.8838	0.8706	0.7967
0.0017	63.1579	1200	1.5147	{'precision': 0.8608490566037735, 'recall': 0.8935128518971848, 'f1': 0.8768768768768769, 'number': 817}	{'precision': 0.6388888888888888, 'recall': 0.5798319327731093, 'f1': 0.6079295154185022, 'number': 119}	{'precision': 0.8934056007226739, 'recall': 0.9182915506035283, 'f1': 0.9056776556776556, 'number': 1077}	0.8667	0.8882	0.8773	0.8087
0.0014	73.6842	1400	1.8128	{'precision': 0.8349514563106796, 'recall': 0.9473684210526315, 'f1': 0.8876146788990826, 'number': 817}	{'precision': 0.6078431372549019, 'recall': 0.5210084033613446, 'f1': 0.5610859728506787, 'number': 119}	{'precision': 0.9125475285171103, 'recall': 0.8913649025069638, 'f1': 0.9018318459370597, 'number': 1077}	0.8630	0.8922	0.8774	0.7931
0.001	84.2105	1600	1.7309	{'precision': 0.8884758364312267, 'recall': 0.8776009791921665, 'f1': 0.8830049261083744, 'number': 817}	{'precision': 0.576271186440678, 'recall': 0.5714285714285714, 'f1': 0.5738396624472574, 'number': 119}	{'precision': 0.8825622775800712, 'recall': 0.9210770659238626, 'f1': 0.9014084507042255, 'number': 1077}	0.8673	0.8828	0.8749	0.7998
0.0006	94.7368	1800	1.7644	{'precision': 0.8462414578587699, 'recall': 0.9094247246022031, 'f1': 0.8766961651917403, 'number': 817}	{'precision': 0.6363636363636364, 'recall': 0.5294117647058824, 'f1': 0.5779816513761468, 'number': 119}	{'precision': 0.9010175763182239, 'recall': 0.904363974001857, 'f1': 0.9026876737720111, 'number': 1077}	0.8649	0.8843	0.8745	0.7967
0.0006	105.2632	2000	1.6953	{'precision': 0.8673835125448028, 'recall': 0.8886168910648715, 'f1': 0.8778718258766626, 'number': 817}	{'precision': 0.6666666666666666, 'recall': 0.5378151260504201, 'f1': 0.5953488372093023, 'number': 119}	{'precision': 0.8741319444444444, 'recall': 0.9350046425255338, 'f1': 0.9035441902198295, 'number': 1077}	0.8619	0.8927	0.8770	0.8027
0.0003	115.7895	2200	1.7110	{'precision': 0.8460661345496009, 'recall': 0.9082007343941249, 'f1': 0.8760330578512396, 'number': 817}	{'precision': 0.6470588235294118, 'recall': 0.5546218487394958, 'f1': 0.5972850678733032, 'number': 119}	{'precision': 0.9019248395967002, 'recall': 0.9136490250696379, 'f1': 0.9077490774907748, 'number': 1077}	0.8657	0.8902	0.8778	0.7988
0.0002	126.3158	2400	1.7082	{'precision': 0.8447488584474886, 'recall': 0.9057527539779682, 'f1': 0.874187832250443, 'number': 817}	{'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}	{'precision': 0.9002744739249772, 'recall': 0.9136490250696379, 'f1': 0.9069124423963134, 'number': 1077}	0.8638	0.8882	0.8758	0.7978

Framework versions

• Transformers 4.41.2
• Pytorch 2.3.0+cu121
• Datasets 2.19.2
• Tokenizers 0.19.1