Loss: 0.6898
Answer: {'precision': 0.6987951807228916, 'recall': 0.788627935723115, 'f1': 0.7409988385598142, 'number': 809}
Header: {'precision': 0.3157894736842105, 'recall': 0.35294117647058826, 'f1': 0.33333333333333337, 'number': 119}
Question: {'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}
Overall Precision: 0.7168
Overall Recall: 0.7863
Overall F1: 0.7499
Overall Accuracy: 0.8057

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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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
1.8366	1.0	10	1.6180	{'precision': 0.003418803418803419, 'recall': 0.002472187886279357, 'f1': 0.0028694404591104736, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.1913214990138067, 'recall': 0.09107981220657277, 'f1': 0.12340966921119592, 'number': 1065}	0.0907	0.0497	0.0642	0.3481
1.4796	2.0	20	1.2677	{'precision': 0.2348860257680872, 'recall': 0.29295426452410384, 'f1': 0.2607260726072607, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.448690728945506, 'recall': 0.5953051643192488, 'f1': 0.5117029862792575, 'number': 1065}	0.3596	0.4370	0.3946	0.6123
1.112	3.0	30	0.9348	{'precision': 0.46387832699619774, 'recall': 0.6032138442521632, 'f1': 0.5244492208490059, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5677842565597667, 'recall': 0.7314553990610329, 'f1': 0.6393106278210915, 'number': 1065}	0.5220	0.6357	0.5733	0.7072
0.8556	4.0	40	0.7811	{'precision': 0.5760765550239234, 'recall': 0.7441285537700866, 'f1': 0.6494066882416398, 'number': 809}	{'precision': 0.043478260869565216, 'recall': 0.01680672268907563, 'f1': 0.024242424242424242, 'number': 119}	{'precision': 0.6784810126582278, 'recall': 0.7549295774647887, 'f1': 0.7146666666666667, 'number': 1065}	0.6186	0.7065	0.6596	0.7636
0.679	5.0	50	0.7063	{'precision': 0.6343519494204426, 'recall': 0.7441285537700866, 'f1': 0.6848691695108078, 'number': 809}	{'precision': 0.15294117647058825, 'recall': 0.1092436974789916, 'f1': 0.12745098039215685, 'number': 119}	{'precision': 0.6796812749003984, 'recall': 0.8009389671361502, 'f1': 0.7353448275862068, 'number': 1065}	0.6413	0.7366	0.6857	0.7854
0.5692	6.0	60	0.6788	{'precision': 0.6491769547325102, 'recall': 0.7799752781211372, 'f1': 0.708590679393599, 'number': 809}	{'precision': 0.26436781609195403, 'recall': 0.19327731092436976, 'f1': 0.22330097087378642, 'number': 119}	{'precision': 0.7327510917030567, 'recall': 0.787793427230047, 'f1': 0.7592760180995476, 'number': 1065}	0.6774	0.7491	0.7115	0.7889
0.4895	7.0	70	0.6565	{'precision': 0.6697722567287785, 'recall': 0.799752781211372, 'f1': 0.7290140845070422, 'number': 809}	{'precision': 0.29473684210526313, 'recall': 0.23529411764705882, 'f1': 0.2616822429906542, 'number': 119}	{'precision': 0.7526315789473684, 'recall': 0.8056338028169014, 'f1': 0.7782312925170067, 'number': 1065}	0.6965	0.7692	0.7310	0.7999
0.441	8.0	80	0.6647	{'precision': 0.6814345991561181, 'recall': 0.7985166872682324, 'f1': 0.7353443369379624, 'number': 809}	{'precision': 0.25196850393700787, 'recall': 0.2689075630252101, 'f1': 0.2601626016260163, 'number': 119}	{'precision': 0.7489177489177489, 'recall': 0.812206572769953, 'f1': 0.7792792792792793, 'number': 1065}	0.6919	0.7742	0.7308	0.8008
0.3834	9.0	90	0.6705	{'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}	{'precision': 0.31666666666666665, 'recall': 0.31932773109243695, 'f1': 0.3179916317991632, 'number': 119}	{'precision': 0.7519116397621071, 'recall': 0.8309859154929577, 'f1': 0.7894736842105263, 'number': 1065}	0.7079	0.7807	0.7425	0.8010
0.3793	10.0	100	0.6591	{'precision': 0.6965811965811965, 'recall': 0.8059332509270705, 'f1': 0.7472779369627507, 'number': 809}	{'precision': 0.3211009174311927, 'recall': 0.29411764705882354, 'f1': 0.30701754385964913, 'number': 119}	{'precision': 0.7831431079894644, 'recall': 0.8375586854460094, 'f1': 0.809437386569873, 'number': 1065}	0.7230	0.7923	0.7560	0.8096
0.3189	11.0	110	0.6794	{'precision': 0.6991247264770241, 'recall': 0.7898640296662547, 'f1': 0.7417295414973882, 'number': 809}	{'precision': 0.3111111111111111, 'recall': 0.35294117647058826, 'f1': 0.33070866141732286, 'number': 119}	{'precision': 0.779646017699115, 'recall': 0.8272300469483568, 'f1': 0.8027334851936219, 'number': 1065}	0.7168	0.7837	0.7488	0.8043
0.3037	12.0	120	0.6780	{'precision': 0.7, 'recall': 0.7873918417799752, 'f1': 0.7411285631180919, 'number': 809}	{'precision': 0.32558139534883723, 'recall': 0.35294117647058826, 'f1': 0.33870967741935487, 'number': 119}	{'precision': 0.7782646801051709, 'recall': 0.8338028169014085, 'f1': 0.8050770625566637, 'number': 1065}	0.7188	0.7863	0.7510	0.8046
0.2878	13.0	130	0.6864	{'precision': 0.7065934065934066, 'recall': 0.7948084054388134, 'f1': 0.748109365910413, 'number': 809}	{'precision': 0.33070866141732286, 'recall': 0.35294117647058826, 'f1': 0.34146341463414637, 'number': 119}	{'precision': 0.7889087656529516, 'recall': 0.828169014084507, 'f1': 0.8080622995877232, 'number': 1065}	0.7271	0.7863	0.7555	0.8057
0.2626	14.0	140	0.6874	{'precision': 0.7023153252480706, 'recall': 0.7873918417799752, 'f1': 0.7424242424242424, 'number': 809}	{'precision': 0.3181818181818182, 'recall': 0.35294117647058826, 'f1': 0.3346613545816733, 'number': 119}	{'precision': 0.7798245614035088, 'recall': 0.8347417840375587, 'f1': 0.8063492063492064, 'number': 1065}	0.7196	0.7868	0.7517	0.8056
0.2683	15.0	150	0.6898	{'precision': 0.6987951807228916, 'recall': 0.788627935723115, 'f1': 0.7409988385598142, 'number': 809}	{'precision': 0.3157894736842105, 'recall': 0.35294117647058826, 'f1': 0.33333333333333337, 'number': 119}	{'precision': 0.7780701754385965, 'recall': 0.8328638497652582, 'f1': 0.8045351473922903, 'number': 1065}	0.7168	0.7863	0.7499	0.8057

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.6.0
Tokenizers 0.21.1

rushda7/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

rushda7/
layoutlm-funsd