layoutlm-funsd

sreejith8100/layoutlm-funsd

layoutlm-funsd is a token classification model from sreejith8100. 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 10, 2023
Updated: Oct 14, 2023
SHA: 1ac99bae

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: 0.7204
Answer: {'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}
Header: {'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}
Question: {'precision': 0.7799126637554585, 'recall': 0.8384976525821596, 'f1': 0.8081447963800905, 'number': 1065}
Overall Precision: 0.7284
Overall Recall: 0.7913
Overall F1: 0.7585
Overall Accuracy: 0.7930

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
1.8258	1.0	10	1.6104	{'precision': 0.03933136676499508, 'recall': 0.049443757725587144, 'f1': 0.04381161007667032, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.2102076124567474, 'recall': 0.22816901408450704, 'f1': 0.21882035119315627, 'number': 1065}	0.1302	0.1420	0.1359	0.3742
1.4584	2.0	20	1.2730	{'precision': 0.1923509561304837, 'recall': 0.21137206427688504, 'f1': 0.20141342756183747, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4099290780141844, 'recall': 0.5427230046948357, 'f1': 0.467070707070707, 'number': 1065}	0.3258	0.3758	0.3490	0.5734
1.1076	3.0	30	0.9718	{'precision': 0.501532175689479, 'recall': 0.6069221260815822, 'f1': 0.5492170022371365, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5749211356466877, 'recall': 0.6845070422535211, 'f1': 0.6249464209172738, 'number': 1065}	0.5358	0.6121	0.5714	0.6890
0.8541	4.0	40	0.8287	{'precision': 0.5573921028466483, 'recall': 0.7503090234857849, 'f1': 0.6396206533192834, 'number': 809}	{'precision': 0.14893617021276595, 'recall': 0.058823529411764705, 'f1': 0.08433734939759036, 'number': 119}	{'precision': 0.6451342281879194, 'recall': 0.7220657276995305, 'f1': 0.6814355338945502, 'number': 1065}	0.5941	0.6939	0.6401	0.7420
0.7105	5.0	50	0.7483	{'precision': 0.6358695652173914, 'recall': 0.723114956736712, 'f1': 0.6766917293233082, 'number': 809}	{'precision': 0.2465753424657534, 'recall': 0.15126050420168066, 'f1': 0.18749999999999997, 'number': 119}	{'precision': 0.6898305084745763, 'recall': 0.7643192488262911, 'f1': 0.7251670378619154, 'number': 1065}	0.6521	0.7110	0.6803	0.7618
0.6079	6.0	60	0.7023	{'precision': 0.6306209850107066, 'recall': 0.7280593325092707, 'f1': 0.6758462421113023, 'number': 809}	{'precision': 0.2875, 'recall': 0.19327731092436976, 'f1': 0.23115577889447236, 'number': 119}	{'precision': 0.6796267496111975, 'recall': 0.8206572769953052, 'f1': 0.7435133985538068, 'number': 1065}	0.6461	0.7456	0.6923	0.7776
0.5267	7.0	70	0.6779	{'precision': 0.674892703862661, 'recall': 0.7775030902348579, 'f1': 0.7225732337736933, 'number': 809}	{'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119}	{'precision': 0.717391304347826, 'recall': 0.8056338028169014, 'f1': 0.7589562140645733, 'number': 1065}	0.6826	0.7597	0.7191	0.7853
0.4735	8.0	80	0.6688	{'precision': 0.6955093099671413, 'recall': 0.7849196538936959, 'f1': 0.7375145180023228, 'number': 809}	{'precision': 0.32608695652173914, 'recall': 0.25210084033613445, 'f1': 0.2843601895734597, 'number': 119}	{'precision': 0.7424892703862661, 'recall': 0.812206572769953, 'f1': 0.7757847533632287, 'number': 1065}	0.7051	0.7677	0.7350	0.7950
0.4196	9.0	90	0.6791	{'precision': 0.6843243243243243, 'recall': 0.7824474660074165, 'f1': 0.7301038062283737, 'number': 809}	{'precision': 0.3181818181818182, 'recall': 0.29411764705882354, 'f1': 0.3056768558951965, 'number': 119}	{'precision': 0.7561807331628303, 'recall': 0.8328638497652582, 'f1': 0.7926720285969615, 'number': 1065}	0.7043	0.7802	0.7403	0.7937
0.3756	10.0	100	0.6968	{'precision': 0.7089887640449438, 'recall': 0.7799752781211372, 'f1': 0.7427898763978811, 'number': 809}	{'precision': 0.328, 'recall': 0.3445378151260504, 'f1': 0.33606557377049184, 'number': 119}	{'precision': 0.7790393013100436, 'recall': 0.8375586854460094, 'f1': 0.807239819004525, 'number': 1065}	0.7241	0.7847	0.7532	0.7947
0.3402	11.0	110	0.6959	{'precision': 0.7024070021881839, 'recall': 0.7935723114956736, 'f1': 0.7452118398142775, 'number': 809}	{'precision': 0.3416666666666667, 'recall': 0.3445378151260504, 'f1': 0.34309623430962344, 'number': 119}	{'precision': 0.7791411042944786, 'recall': 0.8347417840375587, 'f1': 0.8059836808703537, 'number': 1065}	0.7228	0.7888	0.7543	0.7958
0.3225	12.0	120	0.6945	{'precision': 0.7106430155210643, 'recall': 0.792336217552534, 'f1': 0.7492694330800702, 'number': 809}	{'precision': 0.3644067796610169, 'recall': 0.36134453781512604, 'f1': 0.3628691983122363, 'number': 119}	{'precision': 0.7667238421955404, 'recall': 0.8394366197183099, 'f1': 0.8014343343792021, 'number': 1065}	0.7219	0.7918	0.7552	0.7961
0.3031	13.0	130	0.7204	{'precision': 0.71, 'recall': 0.7898640296662547, 'f1': 0.7478057343475717, 'number': 809}	{'precision': 0.35, 'recall': 0.35294117647058826, 'f1': 0.35146443514644354, 'number': 119}	{'precision': 0.7895204262877442, 'recall': 0.8347417840375587, 'f1': 0.8115015974440895, 'number': 1065}	0.7316	0.7878	0.7586	0.7916
0.289	14.0	140	0.7196	{'precision': 0.7095709570957096, 'recall': 0.7972805933250927, 'f1': 0.750873108265425, 'number': 809}	{'precision': 0.3826086956521739, 'recall': 0.3697478991596639, 'f1': 0.37606837606837606, 'number': 119}	{'precision': 0.7816593886462883, 'recall': 0.8403755868544601, 'f1': 0.8099547511312217, 'number': 1065}	0.7303	0.7948	0.7612	0.7949
0.2801	15.0	150	0.7204	{'precision': 0.7103218645948945, 'recall': 0.7911001236093943, 'f1': 0.7485380116959064, 'number': 809}	{'precision': 0.3697478991596639, 'recall': 0.3697478991596639, 'f1': 0.3697478991596639, 'number': 119}	{'precision': 0.7799126637554585, 'recall': 0.8384976525821596, 'f1': 0.8081447963800905, 'number': 1065}	0.7284	0.7913	0.7585	0.7930

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 10, 2023
Updated: Oct 14, 2023