Loss: 0.7403
Answer: {'precision': 0.73, 'recall': 0.8121137206427689, 'f1': 0.7688706846108836, 'number': 809}
Header: {'precision': 0.3611111111111111, 'recall': 0.4369747899159664, 'f1': 0.3954372623574144, 'number': 119}
Question: {'precision': 0.7853962600178095, 'recall': 0.828169014084507, 'f1': 0.8062157221206582, 'number': 1065}
Overall Precision: 0.7342
Overall Recall: 0.7983
Overall F1: 0.7649
Overall Accuracy: 0.8101

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: 16

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.3197	1.0	10	1.0997	{'precision': 0.34190231362467866, 'recall': 0.3288009888751545, 'f1': 0.3352236925015753, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5646958011996572, 'recall': 0.6187793427230047, 'f1': 0.5905017921146953, 'number': 1065}	0.4756	0.4641	0.4698	0.6432
0.9556	2.0	20	0.8488	{'precision': 0.5481481481481482, 'recall': 0.6402966625463535, 'f1': 0.5906499429874572, 'number': 809}	{'precision': 0.038461538461538464, 'recall': 0.008403361344537815, 'f1': 0.013793103448275862, 'number': 119}	{'precision': 0.6639566395663956, 'recall': 0.6901408450704225, 'f1': 0.6767955801104972, 'number': 1065}	0.6035	0.6292	0.6161	0.7343
0.7263	3.0	30	0.7385	{'precision': 0.645397489539749, 'recall': 0.7626699629171817, 'f1': 0.6991501416430596, 'number': 809}	{'precision': 0.11320754716981132, 'recall': 0.05042016806722689, 'f1': 0.06976744186046512, 'number': 119}	{'precision': 0.7092013888888888, 'recall': 0.7671361502347418, 'f1': 0.7370320252593595, 'number': 1065}	0.6664	0.7225	0.6933	0.7743
0.5842	4.0	40	0.6892	{'precision': 0.6642487046632124, 'recall': 0.792336217552534, 'f1': 0.7226606538895153, 'number': 809}	{'precision': 0.21686746987951808, 'recall': 0.15126050420168066, 'f1': 0.1782178217821782, 'number': 119}	{'precision': 0.7226027397260274, 'recall': 0.7924882629107981, 'f1': 0.7559337214509628, 'number': 1065}	0.6782	0.7541	0.7142	0.7964
0.4945	5.0	50	0.6673	{'precision': 0.6974416017797553, 'recall': 0.7750309023485785, 'f1': 0.734192037470726, 'number': 809}	{'precision': 0.30337078651685395, 'recall': 0.226890756302521, 'f1': 0.2596153846153846, 'number': 119}	{'precision': 0.7408637873754153, 'recall': 0.8375586854460094, 'f1': 0.7862494490965183, 'number': 1065}	0.7053	0.7757	0.7388	0.8033
0.4343	6.0	60	0.6592	{'precision': 0.6962962962962963, 'recall': 0.8133498145859085, 'f1': 0.750285062713797, 'number': 809}	{'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119}	{'precision': 0.7504173622704507, 'recall': 0.844131455399061, 'f1': 0.7945205479452054, 'number': 1065}	0.7069	0.7963	0.7489	0.8077
0.3681	7.0	70	0.6624	{'precision': 0.7049891540130152, 'recall': 0.8034610630407911, 'f1': 0.7510109763142693, 'number': 809}	{'precision': 0.30158730158730157, 'recall': 0.31932773109243695, 'f1': 0.310204081632653, 'number': 119}	{'precision': 0.7659758203799655, 'recall': 0.8328638497652582, 'f1': 0.7980206927575348, 'number': 1065}	0.7140	0.7903	0.7502	0.8090
0.3312	8.0	80	0.6825	{'precision': 0.7097826086956521, 'recall': 0.8071693448702101, 'f1': 0.7553499132446501, 'number': 809}	{'precision': 0.32142857142857145, 'recall': 0.37815126050420167, 'f1': 0.3474903474903475, 'number': 119}	{'precision': 0.7703056768558952, 'recall': 0.828169014084507, 'f1': 0.7981900452488688, 'number': 1065}	0.7166	0.7928	0.7527	0.8078
0.2955	9.0	90	0.7009	{'precision': 0.7141316073354909, 'recall': 0.8182941903584673, 'f1': 0.7626728110599078, 'number': 809}	{'precision': 0.3493150684931507, 'recall': 0.42857142857142855, 'f1': 0.38490566037735846, 'number': 119}	{'precision': 0.7753108348134992, 'recall': 0.819718309859155, 'f1': 0.7968963943404839, 'number': 1065}	0.7212	0.7958	0.7567	0.8034
0.2888	10.0	100	0.6894	{'precision': 0.7125813449023861, 'recall': 0.8121137206427689, 'f1': 0.7590987868284228, 'number': 809}	{'precision': 0.37272727272727274, 'recall': 0.3445378151260504, 'f1': 0.35807860262008734, 'number': 119}	{'precision': 0.7917783735478106, 'recall': 0.831924882629108, 'f1': 0.8113553113553114, 'number': 1065}	0.7364	0.7948	0.7645	0.8140
0.2482	11.0	110	0.7131	{'precision': 0.7191854233654876, 'recall': 0.8294190358467244, 'f1': 0.7703788748564868, 'number': 809}	{'precision': 0.3, 'recall': 0.40336134453781514, 'f1': 0.34408602150537637, 'number': 119}	{'precision': 0.7843833185448092, 'recall': 0.8300469483568075, 'f1': 0.8065693430656934, 'number': 1065}	0.7221	0.8043	0.7610	0.8084
0.2297	12.0	120	0.7189	{'precision': 0.7373167981961668, 'recall': 0.8084054388133498, 'f1': 0.7712264150943396, 'number': 809}	{'precision': 0.3484848484848485, 'recall': 0.3865546218487395, 'f1': 0.3665338645418326, 'number': 119}	{'precision': 0.7730434782608696, 'recall': 0.8347417840375587, 'f1': 0.8027088036117382, 'number': 1065}	0.7326	0.7973	0.7636	0.8125
0.2168	13.0	130	0.7283	{'precision': 0.723986856516977, 'recall': 0.8170580964153276, 'f1': 0.7677119628339142, 'number': 809}	{'precision': 0.33793103448275863, 'recall': 0.4117647058823529, 'f1': 0.37121212121212116, 'number': 119}	{'precision': 0.7878245299910475, 'recall': 0.8262910798122066, 'f1': 0.8065994500458296, 'number': 1065}	0.7310	0.7978	0.7630	0.8099
0.2011	14.0	140	0.7318	{'precision': 0.7338530066815144, 'recall': 0.8145859085290482, 'f1': 0.7721148213239603, 'number': 809}	{'precision': 0.3493150684931507, 'recall': 0.42857142857142855, 'f1': 0.38490566037735846, 'number': 119}	{'precision': 0.7833775419982316, 'recall': 0.831924882629108, 'f1': 0.8069216757741348, 'number': 1065}	0.7338	0.8008	0.7658	0.8112
0.1948	15.0	150	0.7391	{'precision': 0.7216721672167217, 'recall': 0.8108776266996292, 'f1': 0.7636786961583235, 'number': 809}	{'precision': 0.3561643835616438, 'recall': 0.4369747899159664, 'f1': 0.39245283018867927, 'number': 119}	{'precision': 0.7848214285714286, 'recall': 0.8253521126760563, 'f1': 0.8045766590389016, 'number': 1065}	0.7297	0.7963	0.7615	0.8076
0.1955	16.0	160	0.7403	{'precision': 0.73, 'recall': 0.8121137206427689, 'f1': 0.7688706846108836, 'number': 809}	{'precision': 0.3611111111111111, 'recall': 0.4369747899159664, 'f1': 0.3954372623574144, 'number': 119}	{'precision': 0.7853962600178095, 'recall': 0.828169014084507, 'f1': 0.8062157221206582, 'number': 1065}	0.7342	0.7983	0.7649	0.8101

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

MuafiraThasni/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MuafiraThasni/
layoutlm-funsd