Loss: 0.7107
Answer: {'precision': 0.696078431372549, 'recall': 0.7898640296662547, 'f1': 0.740011580775912, 'number': 809}
Header: {'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}
Question: {'precision': 0.7799827437446074, 'recall': 0.8488262910798122, 'f1': 0.8129496402877696, 'number': 1065}
Overall Precision: 0.7211
Overall Recall: 0.7938
Overall F1: 0.7557
Overall Accuracy: 0.8008

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: 3e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8202	1.0	10	1.5835	{'precision': 0.025120772946859903, 'recall': 0.032138442521631644, 'f1': 0.028199566160520606, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.20365535248041775, 'recall': 0.21971830985915494, 'f1': 0.2113821138211382, 'number': 1065}	0.1190	0.1305	0.1245	0.3882
1.4316	2.0	20	1.2683	{'precision': 0.16164383561643836, 'recall': 0.14585908529048208, 'f1': 0.1533463287849253, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4461190655614167, 'recall': 0.5558685446009389, 'f1': 0.49498327759197325, 'number': 1065}	0.3452	0.3562	0.3506	0.5544
1.1076	3.0	30	0.9812	{'precision': 0.44790547798066593, 'recall': 0.515451174289246, 'f1': 0.4793103448275862, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5667215815485996, 'recall': 0.6460093896713615, 'f1': 0.6037735849056605, 'number': 1065}	0.5087	0.5544	0.5306	0.6776
0.845	4.0	40	0.8144	{'precision': 0.5346083788706739, 'recall': 0.7255871446229913, 'f1': 0.615626638699528, 'number': 809}	{'precision': 0.029411764705882353, 'recall': 0.01680672268907563, 'f1': 0.021390374331550797, 'number': 119}	{'precision': 0.6285482562854826, 'recall': 0.7276995305164319, 'f1': 0.6744995648389904, 'number': 1065}	0.5686	0.6844	0.6211	0.7435
0.6785	5.0	50	0.7277	{'precision': 0.6069651741293532, 'recall': 0.754017305315204, 'f1': 0.6725468577728776, 'number': 809}	{'precision': 0.29850746268656714, 'recall': 0.16806722689075632, 'f1': 0.21505376344086022, 'number': 119}	{'precision': 0.6952296819787986, 'recall': 0.7389671361502348, 'f1': 0.7164314974965864, 'number': 1065}	0.6429	0.7110	0.6752	0.7748
0.5677	6.0	60	0.6958	{'precision': 0.6477987421383647, 'recall': 0.7639060568603214, 'f1': 0.7010777084515031, 'number': 809}	{'precision': 0.29347826086956524, 'recall': 0.226890756302521, 'f1': 0.2559241706161137, 'number': 119}	{'precision': 0.7031375703942075, 'recall': 0.8206572769953052, 'f1': 0.7573656845753899, 'number': 1065}	0.6636	0.7622	0.7095	0.7865
0.4873	7.0	70	0.6717	{'precision': 0.6487046632124353, 'recall': 0.7737948084054388, 'f1': 0.705749718151071, 'number': 809}	{'precision': 0.31313131313131315, 'recall': 0.2605042016806723, 'f1': 0.28440366972477066, 'number': 119}	{'precision': 0.7402707275803723, 'recall': 0.8215962441314554, 'f1': 0.778816199376947, 'number': 1065}	0.6821	0.7687	0.7228	0.7916
0.4374	8.0	80	0.6668	{'precision': 0.6781115879828327, 'recall': 0.7812113720642769, 'f1': 0.7260195290063183, 'number': 809}	{'precision': 0.3173076923076923, 'recall': 0.2773109243697479, 'f1': 0.29596412556053814, 'number': 119}	{'precision': 0.743142144638404, 'recall': 0.8394366197183099, 'f1': 0.7883597883597885, 'number': 1065}	0.6963	0.7822	0.7368	0.7951
0.3894	9.0	90	0.6763	{'precision': 0.6646341463414634, 'recall': 0.8084054388133498, 'f1': 0.7295036252091467, 'number': 809}	{'precision': 0.3465346534653465, 'recall': 0.29411764705882354, 'f1': 0.3181818181818182, 'number': 119}	{'precision': 0.7589833479404031, 'recall': 0.8131455399061033, 'f1': 0.7851314596554851, 'number': 1065}	0.6986	0.7802	0.7371	0.7977
0.3492	10.0	100	0.6727	{'precision': 0.6944140197152245, 'recall': 0.7836835599505563, 'f1': 0.7363530778164925, 'number': 809}	{'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}	{'precision': 0.762350936967632, 'recall': 0.8403755868544601, 'f1': 0.7994640464493078, 'number': 1065}	0.7101	0.7868	0.7465	0.7993
0.3166	11.0	110	0.6756	{'precision': 0.6956055734190782, 'recall': 0.8022249690976514, 'f1': 0.7451205510907002, 'number': 809}	{'precision': 0.30833333333333335, 'recall': 0.31092436974789917, 'f1': 0.3096234309623431, 'number': 119}	{'precision': 0.7672047578589635, 'recall': 0.847887323943662, 'f1': 0.8055307760927745, 'number': 1065}	0.7126	0.7973	0.7525	0.8022
0.2962	12.0	120	0.7018	{'precision': 0.6983783783783784, 'recall': 0.7985166872682324, 'f1': 0.7450980392156863, 'number': 809}	{'precision': 0.32231404958677684, 'recall': 0.3277310924369748, 'f1': 0.32499999999999996, 'number': 119}	{'precision': 0.7716468590831919, 'recall': 0.8535211267605634, 'f1': 0.8105216228265715, 'number': 1065}	0.7167	0.7998	0.7560	0.8007
0.2861	13.0	130	0.7136	{'precision': 0.6987041036717062, 'recall': 0.799752781211372, 'f1': 0.745821325648415, 'number': 809}	{'precision': 0.3305084745762712, 'recall': 0.3277310924369748, 'f1': 0.32911392405063294, 'number': 119}	{'precision': 0.7859007832898173, 'recall': 0.847887323943662, 'f1': 0.8157181571815718, 'number': 1065}	0.7246	0.7973	0.7592	0.8021
0.2685	14.0	140	0.7143	{'precision': 0.6964091403699674, 'recall': 0.7911001236093943, 'f1': 0.7407407407407407, 'number': 809}	{'precision': 0.325, 'recall': 0.3277310924369748, 'f1': 0.3263598326359833, 'number': 119}	{'precision': 0.7802768166089965, 'recall': 0.8469483568075117, 'f1': 0.8122467357046375, 'number': 1065}	0.7203	0.7933	0.7550	0.8003
0.2625	15.0	150	0.7107	{'precision': 0.696078431372549, 'recall': 0.7898640296662547, 'f1': 0.740011580775912, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.3277310924369748, 'f1': 0.3305084745762712, 'number': 119}	{'precision': 0.7799827437446074, 'recall': 0.8488262910798122, 'f1': 0.8129496402877696, 'number': 1065}	0.7211	0.7938	0.7557	0.8008

Framework versions

Transformers 4.34.1
Pytorch 2.1.0+cu118
Datasets 2.14.6
Tokenizers 0.14.1

nishthaahuja25/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nishthaahuja25/
layoutlm-funsd