layoutlm-funsd

Sagar0934/layoutlm-funsd

layoutlm-funsd is a token classification model from Sagar0934. Use it when you need labels on individual words, such as names. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Oct 7, 2023
Updated: Oct 7, 2023
SHA: a93b81aa

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the funsd dataset. It achieves the following results on the evaluation set:

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

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 7, 2023
Updated: Oct 7, 2023