Loss: 0.6739
Answer: {'precision': 0.7077087794432548, 'recall': 0.8170580964153276, 'f1': 0.7584624211130234, 'number': 809}
Header: {'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119}
Question: {'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}
Overall Precision: 0.7215
Overall Recall: 0.7928
Overall F1: 0.7554
Overall Accuracy: 0.8075

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7578	1.0	10	1.5659	{'precision': 0.020053475935828877, 'recall': 0.018541409147095178, 'f1': 0.01926782273603083, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.311886586695747, 'recall': 0.26854460093896715, 'f1': 0.2885973763874874, 'number': 1065}	0.1808	0.1510	0.1646	0.3760
1.409	2.0	20	1.2205	{'precision': 0.220795892169448, 'recall': 0.2126081582200247, 'f1': 0.21662468513853905, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.43257184966838613, 'recall': 0.5511737089201878, 'f1': 0.4847233691164327, 'number': 1065}	0.3553	0.3808	0.3676	0.5932
1.0728	3.0	30	0.9396	{'precision': 0.5072765072765073, 'recall': 0.6032138442521632, 'f1': 0.5511010728402033, 'number': 809}	{'precision': 0.02702702702702703, 'recall': 0.008403361344537815, 'f1': 0.01282051282051282, 'number': 119}	{'precision': 0.5947242206235012, 'recall': 0.6985915492957746, 'f1': 0.6424870466321244, 'number': 1065}	0.548	0.6187	0.5812	0.7236
0.8188	4.0	40	0.7725	{'precision': 0.6076845298281092, 'recall': 0.7428924598269468, 'f1': 0.6685205784204672, 'number': 809}	{'precision': 0.18, 'recall': 0.07563025210084033, 'f1': 0.10650887573964496, 'number': 119}	{'precision': 0.6797608881298036, 'recall': 0.7474178403755869, 'f1': 0.7119856887298748, 'number': 1065}	0.6362	0.7055	0.6690	0.7680
0.6647	5.0	50	0.7205	{'precision': 0.6301806588735388, 'recall': 0.7330037082818294, 'f1': 0.6777142857142857, 'number': 809}	{'precision': 0.22093023255813954, 'recall': 0.15966386554621848, 'f1': 0.18536585365853656, 'number': 119}	{'precision': 0.6648731744811683, 'recall': 0.812206572769953, 'f1': 0.7311918850380389, 'number': 1065}	0.6345	0.7411	0.6836	0.7775
0.5719	6.0	60	0.6793	{'precision': 0.6366336633663366, 'recall': 0.7948084054388134, 'f1': 0.7069818581638262, 'number': 809}	{'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119}	{'precision': 0.7342342342342343, 'recall': 0.7652582159624414, 'f1': 0.749425287356322, 'number': 1065}	0.6714	0.7421	0.7050	0.7826
0.5011	7.0	70	0.6617	{'precision': 0.6697819314641744, 'recall': 0.7972805933250927, 'f1': 0.7279909706546276, 'number': 809}	{'precision': 0.24347826086956523, 'recall': 0.23529411764705882, 'f1': 0.23931623931623933, 'number': 119}	{'precision': 0.7497773820124666, 'recall': 0.7906103286384977, 'f1': 0.7696526508226691, 'number': 1065}	0.6883	0.7602	0.7225	0.7929
0.4478	8.0	80	0.6529	{'precision': 0.6725755995828988, 'recall': 0.7972805933250927, 'f1': 0.7296380090497737, 'number': 809}	{'precision': 0.23577235772357724, 'recall': 0.24369747899159663, 'f1': 0.23966942148760334, 'number': 119}	{'precision': 0.7578397212543554, 'recall': 0.8169014084507042, 'f1': 0.7862629914143697, 'number': 1065}	0.6924	0.7747	0.7312	0.8001
0.3901	9.0	90	0.6513	{'precision': 0.6936353829557713, 'recall': 0.7948084054388134, 'f1': 0.7407834101382489, 'number': 809}	{'precision': 0.27906976744186046, 'recall': 0.3025210084033613, 'f1': 0.29032258064516125, 'number': 119}	{'precision': 0.7517123287671232, 'recall': 0.8244131455399061, 'f1': 0.7863860277653381, 'number': 1065}	0.7001	0.7812	0.7384	0.8034
0.3881	10.0	100	0.6564	{'precision': 0.685890834191555, 'recall': 0.823238566131026, 'f1': 0.7483146067415729, 'number': 809}	{'precision': 0.3063063063063063, 'recall': 0.2857142857142857, 'f1': 0.2956521739130435, 'number': 119}	{'precision': 0.7702582368655387, 'recall': 0.812206572769953, 'f1': 0.7906764168190127, 'number': 1065}	0.7098	0.7852	0.7456	0.8075
0.3249	11.0	110	0.6580	{'precision': 0.7036247334754797, 'recall': 0.8158220024721878, 'f1': 0.755580995993131, 'number': 809}	{'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}	{'precision': 0.7693646649260226, 'recall': 0.8300469483568075, 'f1': 0.7985546522131888, 'number': 1065}	0.7148	0.7948	0.7527	0.8088
0.3099	12.0	120	0.6646	{'precision': 0.7090909090909091, 'recall': 0.8195302843016069, 'f1': 0.7603211009174312, 'number': 809}	{'precision': 0.29411764705882354, 'recall': 0.33613445378151263, 'f1': 0.3137254901960785, 'number': 119}	{'precision': 0.7797672336615935, 'recall': 0.8178403755868544, 'f1': 0.7983501374885427, 'number': 1065}	0.7194	0.7898	0.7529	0.8098
0.2907	13.0	130	0.6653	{'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}	{'precision': 0.3125, 'recall': 0.33613445378151263, 'f1': 0.3238866396761134, 'number': 119}	{'precision': 0.7902790279027903, 'recall': 0.8244131455399061, 'f1': 0.806985294117647, 'number': 1065}	0.7295	0.7928	0.7598	0.8104
0.2715	14.0	140	0.6720	{'precision': 0.71259418729817, 'recall': 0.8182941903584673, 'f1': 0.761795166858458, 'number': 809}	{'precision': 0.31343283582089554, 'recall': 0.35294117647058826, 'f1': 0.3320158102766798, 'number': 119}	{'precision': 0.7867383512544803, 'recall': 0.8244131455399061, 'f1': 0.8051352590554791, 'number': 1065}	0.7260	0.7938	0.7584	0.8078
0.2743	15.0	150	0.6739	{'precision': 0.7077087794432548, 'recall': 0.8170580964153276, 'f1': 0.7584624211130234, 'number': 809}	{'precision': 0.30656934306569344, 'recall': 0.35294117647058826, 'f1': 0.32812500000000006, 'number': 119}	{'precision': 0.7837354781054513, 'recall': 0.8234741784037559, 'f1': 0.8031135531135531, 'number': 1065}	0.7215	0.7928	0.7554	0.8075

Framework versions

Transformers 4.40.0
Pytorch 2.2.1+cu121
Datasets 2.19.0
Tokenizers 0.19.1

Mocha2471/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Mocha2471/
layoutlm-funsd