layoutlm-funsd-c

cuongdz01/layoutlm-funsd-c

layoutlm-funsd-c is a token classification model from cuongdz01. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: layoutlm
Access: Public
Created: Dec 19, 2023
Updated: Dec 19, 2023
SHA: edfb1c76

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd-c

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: 0.7152
Answer: {'precision': 0.7134894091415831, 'recall': 0.7911001236093943, 'f1': 0.7502930832356389, 'number': 809}
Header: {'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}
Question: {'precision': 0.7805309734513274, 'recall': 0.828169014084507, 'f1': 0.8036446469248291, 'number': 1065}
Overall Precision: 0.7245
Overall Recall: 0.7837
Overall F1: 0.7530
Overall Accuracy: 0.8069

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
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7835	1.0	10	1.5696	{'precision': 0.02753303964757709, 'recall': 0.030902348578491966, 'f1': 0.029120559114735003, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.23644444444444446, 'recall': 0.24976525821596243, 'f1': 0.24292237442922376, 'number': 1065}	0.1431	0.1460	0.1446	0.4162
1.4134	2.0	20	1.2167	{'precision': 0.15942028985507245, 'recall': 0.13597033374536466, 'f1': 0.1467645096731154, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.42325227963525835, 'recall': 0.5230046948356808, 'f1': 0.4678706425871483, 'number': 1065}	0.3322	0.3347	0.3334	0.5768
1.0829	3.0	30	0.9351	{'precision': 0.4783599088838269, 'recall': 0.519159456118665, 'f1': 0.4979253112033195, 'number': 809}	{'precision': 0.034482758620689655, 'recall': 0.008403361344537815, 'f1': 0.013513513513513513, 'number': 119}	{'precision': 0.6103896103896104, 'recall': 0.6619718309859155, 'f1': 0.6351351351351351, 'number': 1065}	0.5461	0.5650	0.5554	0.7105
0.8077	4.0	40	0.7702	{'precision': 0.6122233930453108, 'recall': 0.7181705809641533, 'f1': 0.6609783845278726, 'number': 809}	{'precision': 0.2033898305084746, 'recall': 0.10084033613445378, 'f1': 0.13483146067415733, 'number': 119}	{'precision': 0.6381631037212985, 'recall': 0.7568075117370892, 'f1': 0.6924398625429553, 'number': 1065}	0.6160	0.7020	0.6562	0.7659
0.6407	5.0	50	0.7146	{'precision': 0.6491978609625668, 'recall': 0.7503090234857849, 'f1': 0.6961009174311926, 'number': 809}	{'precision': 0.2948717948717949, 'recall': 0.19327731092436976, 'f1': 0.233502538071066, 'number': 119}	{'precision': 0.6921221864951769, 'recall': 0.8084507042253521, 'f1': 0.7457773928107406, 'number': 1065}	0.6606	0.7481	0.7016	0.7869
0.5585	6.0	60	0.6995	{'precision': 0.673866090712743, 'recall': 0.7713226205191595, 'f1': 0.7193083573487031, 'number': 809}	{'precision': 0.3372093023255814, 'recall': 0.24369747899159663, 'f1': 0.2829268292682927, 'number': 119}	{'precision': 0.7374784110535406, 'recall': 0.8018779342723005, 'f1': 0.768331084120558, 'number': 1065}	0.6945	0.7561	0.7240	0.7948
0.4934	7.0	70	0.6852	{'precision': 0.6681222707423581, 'recall': 0.7564894932014833, 'f1': 0.7095652173913044, 'number': 809}	{'precision': 0.37777777777777777, 'recall': 0.2857142857142857, 'f1': 0.3253588516746411, 'number': 119}	{'precision': 0.7634408602150538, 'recall': 0.8, 'f1': 0.7812929848693261, 'number': 1065}	0.7059	0.7516	0.7281	0.7979
0.4384	8.0	80	0.6731	{'precision': 0.6920492721164614, 'recall': 0.7639060568603214, 'f1': 0.7262044653349001, 'number': 809}	{'precision': 0.3008130081300813, 'recall': 0.31092436974789917, 'f1': 0.3057851239669422, 'number': 119}	{'precision': 0.7508503401360545, 'recall': 0.8291079812206573, 'f1': 0.788041053101294, 'number': 1065}	0.7016	0.7717	0.7350	0.8021
0.3737	9.0	90	0.6766	{'precision': 0.6993392070484582, 'recall': 0.7849196538936959, 'f1': 0.7396622015142692, 'number': 809}	{'precision': 0.2992125984251969, 'recall': 0.31932773109243695, 'f1': 0.30894308943089427, 'number': 119}	{'precision': 0.7890974084003575, 'recall': 0.8291079812206573, 'f1': 0.8086080586080587, 'number': 1065}	0.7224	0.7807	0.7504	0.8046
0.341	10.0	100	0.6950	{'precision': 0.6888888888888889, 'recall': 0.7663782447466008, 'f1': 0.7255705090696314, 'number': 809}	{'precision': 0.3619047619047619, 'recall': 0.31932773109243695, 'f1': 0.33928571428571425, 'number': 119}	{'precision': 0.7859030837004405, 'recall': 0.8375586854460094, 'f1': 0.8109090909090909, 'number': 1065}	0.7243	0.7777	0.7501	0.8088
0.3178	11.0	110	0.6979	{'precision': 0.7157534246575342, 'recall': 0.7750309023485785, 'f1': 0.7442136498516321, 'number': 809}	{'precision': 0.375, 'recall': 0.35294117647058826, 'f1': 0.3636363636363636, 'number': 119}	{'precision': 0.7805092186128183, 'recall': 0.8347417840375587, 'f1': 0.8067150635208712, 'number': 1065}	0.7325	0.7817	0.7563	0.8059
0.2998	12.0	120	0.7019	{'precision': 0.7027624309392265, 'recall': 0.7861557478368356, 'f1': 0.7421236872812136, 'number': 809}	{'precision': 0.32061068702290074, 'recall': 0.35294117647058826, 'f1': 0.336, 'number': 119}	{'precision': 0.7885816235504014, 'recall': 0.8300469483568075, 'f1': 0.808783165599268, 'number': 1065}	0.7242	0.7837	0.7528	0.8069
0.2809	13.0	130	0.7056	{'precision': 0.7177777777777777, 'recall': 0.7985166872682324, 'f1': 0.7559976594499708, 'number': 809}	{'precision': 0.3565217391304348, 'recall': 0.3445378151260504, 'f1': 0.3504273504273504, 'number': 119}	{'precision': 0.7911504424778761, 'recall': 0.8394366197183099, 'f1': 0.8145785876993167, 'number': 1065}	0.7371	0.7933	0.7641	0.8097
0.2656	14.0	140	0.7117	{'precision': 0.718609865470852, 'recall': 0.792336217552534, 'f1': 0.7536743092298648, 'number': 809}	{'precision': 0.33884297520661155, 'recall': 0.3445378151260504, 'f1': 0.3416666666666667, 'number': 119}	{'precision': 0.7888198757763976, 'recall': 0.8347417840375587, 'f1': 0.8111313868613138, 'number': 1065}	0.7341	0.7883	0.7602	0.8098
0.2669	15.0	150	0.7152	{'precision': 0.7134894091415831, 'recall': 0.7911001236093943, 'f1': 0.7502930832356389, 'number': 809}	{'precision': 0.31007751937984496, 'recall': 0.33613445378151263, 'f1': 0.3225806451612903, 'number': 119}	{'precision': 0.7805309734513274, 'recall': 0.828169014084507, 'f1': 0.8036446469248291, 'number': 1065}	0.7245	0.7837	0.7530	0.8069

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: layoutlm
License: mit
Created: Dec 19, 2023
Updated: Dec 19, 2023