Loss: 0.6608
Answer: {'precision': 0.7201783723522854, 'recall': 0.7985166872682324, 'f1': 0.7573270808909731, 'number': 809}
Header: {'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}
Question: {'precision': 0.7643478260869565, 'recall': 0.8253521126760563, 'f1': 0.7936794582392775, 'number': 1065}
Overall Precision: 0.7216
Overall Recall: 0.7842
Overall F1: 0.7516
Overall Accuracy: 0.8167

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
1.8317	1.0	10	1.6104	{'precision': 0.027842227378190254, 'recall': 0.029666254635352288, 'f1': 0.02872531418312388, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2206047032474804, 'recall': 0.18497652582159624, 'f1': 0.20122574055158324, 'number': 1065}	0.1259	0.1109	0.1179	0.3482
1.4526	2.0	20	1.2629	{'precision': 0.2147165259348613, 'recall': 0.2200247218788628, 'f1': 0.21733821733821734, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.45054095826893353, 'recall': 0.5474178403755868, 'f1': 0.4942772361169987, 'number': 1065}	0.3585	0.3818	0.3698	0.5749
1.0991	3.0	30	0.9508	{'precision': 0.4650856389986825, 'recall': 0.4363411619283066, 'f1': 0.45025510204081637, 'number': 809}	{'precision': 0.05128205128205128, 'recall': 0.01680672268907563, 'f1': 0.025316455696202535, 'number': 119}	{'precision': 0.6182287188306105, 'recall': 0.6751173708920187, 'f1': 0.6454219030520646, 'number': 1065}	0.5477	0.5389	0.5432	0.7030
0.8223	4.0	40	0.7675	{'precision': 0.5823863636363636, 'recall': 0.7601977750309024, 'f1': 0.6595174262734583, 'number': 809}	{'precision': 0.1774193548387097, 'recall': 0.09243697478991597, 'f1': 0.12154696132596685, 'number': 119}	{'precision': 0.6633249791144528, 'recall': 0.7455399061032864, 'f1': 0.7020335985853228, 'number': 1065}	0.6134	0.7125	0.6592	0.7615
0.6605	5.0	50	0.6992	{'precision': 0.6135662898252826, 'recall': 0.7379480840543882, 'f1': 0.6700336700336701, 'number': 809}	{'precision': 0.273972602739726, 'recall': 0.16806722689075632, 'f1': 0.20833333333333331, 'number': 119}	{'precision': 0.7077865266841645, 'recall': 0.7596244131455399, 'f1': 0.7327898550724637, 'number': 1065}	0.6514	0.7155	0.6820	0.7834
0.5625	6.0	60	0.6647	{'precision': 0.6484784889821616, 'recall': 0.7639060568603214, 'f1': 0.7014755959137344, 'number': 809}	{'precision': 0.25, 'recall': 0.24369747899159663, 'f1': 0.24680851063829787, 'number': 119}	{'precision': 0.7197032151690025, 'recall': 0.819718309859155, 'f1': 0.7664618086040387, 'number': 1065}	0.6661	0.7627	0.7111	0.7955
0.4838	7.0	70	0.6497	{'precision': 0.6606189967982924, 'recall': 0.765142150803461, 'f1': 0.7090492554410079, 'number': 809}	{'precision': 0.29896907216494845, 'recall': 0.24369747899159663, 'f1': 0.2685185185185185, 'number': 119}	{'precision': 0.7332775919732442, 'recall': 0.8234741784037559, 'f1': 0.7757629367536488, 'number': 1065}	0.6839	0.7652	0.7222	0.8043
0.4394	8.0	80	0.6342	{'precision': 0.6813778256189451, 'recall': 0.7824474660074165, 'f1': 0.7284234752589184, 'number': 809}	{'precision': 0.30701754385964913, 'recall': 0.29411764705882354, 'f1': 0.30042918454935624, 'number': 119}	{'precision': 0.7540425531914894, 'recall': 0.831924882629108, 'f1': 0.7910714285714286, 'number': 1065}	0.7006	0.7797	0.7381	0.8090
0.3871	9.0	90	0.6447	{'precision': 0.7117516629711752, 'recall': 0.7935723114956736, 'f1': 0.750438340151958, 'number': 809}	{'precision': 0.35, 'recall': 0.29411764705882354, 'f1': 0.31963470319634707, 'number': 119}	{'precision': 0.7660510114335972, 'recall': 0.8178403755868544, 'f1': 0.7910990009082652, 'number': 1065}	0.7237	0.7767	0.7493	0.8132
0.3503	10.0	100	0.6390	{'precision': 0.7056892778993435, 'recall': 0.7972805933250927, 'f1': 0.7486941381311665, 'number': 809}	{'precision': 0.3431372549019608, 'recall': 0.29411764705882354, 'f1': 0.31674208144796384, 'number': 119}	{'precision': 0.7638888888888888, 'recall': 0.8262910798122066, 'f1': 0.7938655841226885, 'number': 1065}	0.7196	0.7827	0.7498	0.8160
0.3196	11.0	110	0.6503	{'precision': 0.7168338907469343, 'recall': 0.7948084054388134, 'f1': 0.753810082063306, 'number': 809}	{'precision': 0.29464285714285715, 'recall': 0.2773109243697479, 'f1': 0.28571428571428575, 'number': 119}	{'precision': 0.7765862377122431, 'recall': 0.815962441314554, 'f1': 0.7957875457875458, 'number': 1065}	0.7260	0.7752	0.7498	0.8155
0.3023	12.0	120	0.6432	{'precision': 0.7020810514786419, 'recall': 0.792336217552534, 'f1': 0.7444831591173056, 'number': 809}	{'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}	{'precision': 0.7600341588385995, 'recall': 0.8356807511737089, 'f1': 0.7960644007155636, 'number': 1065}	0.7138	0.7858	0.7480	0.8181
0.289	13.0	130	0.6666	{'precision': 0.7231638418079096, 'recall': 0.7911001236093943, 'f1': 0.755608028335301, 'number': 809}	{'precision': 0.29838709677419356, 'recall': 0.31092436974789917, 'f1': 0.3045267489711935, 'number': 119}	{'precision': 0.7837837837837838, 'recall': 0.8169014084507042, 'f1': 0.8, 'number': 1065}	0.7301	0.7762	0.7524	0.8184
0.27	14.0	140	0.6599	{'precision': 0.7224080267558528, 'recall': 0.8009888751545118, 'f1': 0.7596717467760844, 'number': 809}	{'precision': 0.32456140350877194, 'recall': 0.31092436974789917, 'f1': 0.31759656652360513, 'number': 119}	{'precision': 0.763840830449827, 'recall': 0.8291079812206573, 'f1': 0.7951373255290409, 'number': 1065}	0.7236	0.7868	0.7538	0.8159
0.2686	15.0	150	0.6608	{'precision': 0.7201783723522854, 'recall': 0.7985166872682324, 'f1': 0.7573270808909731, 'number': 809}	{'precision': 0.31932773109243695, 'recall': 0.31932773109243695, 'f1': 0.31932773109243695, 'number': 119}	{'precision': 0.7643478260869565, 'recall': 0.8253521126760563, 'f1': 0.7936794582392775, 'number': 1065}	0.7216	0.7842	0.7516	0.8167

Framework versions

Transformers 4.34.0
Pytorch 2.0.1+cu118
Datasets 2.14.5
Tokenizers 0.14.1

Sagar0934/layoutlm-funsd

layoutlm-funsd

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Sagar0934/
layoutlm-funsd