layoutlm-funsd

mialiam/layoutlm-funsd

layoutlm-funsd is a token classification model from mialiam. 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: Jun 26, 2023
Updated: Jun 27, 2023
SHA: 351f0e02

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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: 0.6993
Answer: {'precision': 0.7155172413793104, 'recall': 0.8207663782447466, 'f1': 0.7645365572826713, 'number': 809}
Header: {'precision': 0.2781954887218045, 'recall': 0.31092436974789917, 'f1': 0.2936507936507936, 'number': 119}
Question: {'precision': 0.783303730017762, 'recall': 0.828169014084507, 'f1': 0.8051118210862619, 'number': 1065}
Overall Precision: 0.7238
Overall Recall: 0.7943
Overall F1: 0.7574
Overall Accuracy: 0.8095

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
1.7894	1.0	10	1.6149	{'precision': 0.029508196721311476, 'recall': 0.03337453646477132, 'f1': 0.031322505800464036, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.18211920529801323, 'recall': 0.15492957746478872, 'f1': 0.167427701674277, 'number': 1065}	0.1053	0.0963	0.1006	0.3666
1.4628	2.0	20	1.2718	{'precision': 0.21764705882352942, 'recall': 0.22867737948084055, 'f1': 0.2230259192284509, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4429190751445087, 'recall': 0.5755868544600939, 'f1': 0.5006124948958759, 'number': 1065}	0.3572	0.4004	0.3776	0.5813
1.1079	3.0	30	0.9869	{'precision': 0.42190889370932755, 'recall': 0.48084054388133496, 'f1': 0.4494511842865395, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6041666666666666, 'recall': 0.6807511737089202, 'f1': 0.640176600441501, 'number': 1065}	0.5215	0.5590	0.5396	0.6898
0.8376	4.0	40	0.8064	{'precision': 0.6006036217303823, 'recall': 0.7379480840543882, 'f1': 0.6622296173044925, 'number': 809}	{'precision': 0.04918032786885246, 'recall': 0.025210084033613446, 'f1': 0.03333333333333334, 'number': 119}	{'precision': 0.6531302876480541, 'recall': 0.7248826291079812, 'f1': 0.6871384067645749, 'number': 1065}	0.6133	0.6884	0.6487	0.7512
0.6793	5.0	50	0.7442	{'precision': 0.6339468302658486, 'recall': 0.7663782447466008, 'f1': 0.693900391717963, 'number': 809}	{'precision': 0.15306122448979592, 'recall': 0.12605042016806722, 'f1': 0.1382488479262673, 'number': 119}	{'precision': 0.7100802854594113, 'recall': 0.7474178403755869, 'f1': 0.7282708142726441, 'number': 1065}	0.6513	0.7180	0.6831	0.7720
0.5643	6.0	60	0.6937	{'precision': 0.6551373346897253, 'recall': 0.796044499381953, 'f1': 0.7187499999999999, 'number': 809}	{'precision': 0.24175824175824176, 'recall': 0.18487394957983194, 'f1': 0.20952380952380953, 'number': 119}	{'precision': 0.71, 'recall': 0.8, 'f1': 0.752317880794702, 'number': 1065}	0.6675	0.7617	0.7115	0.7895
0.4869	7.0	70	0.6780	{'precision': 0.676130389064143, 'recall': 0.7948084054388134, 'f1': 0.7306818181818182, 'number': 809}	{'precision': 0.2072072072072072, 'recall': 0.19327731092436976, 'f1': 0.2, 'number': 119}	{'precision': 0.7147568013190437, 'recall': 0.8140845070422535, 'f1': 0.7611940298507464, 'number': 1065}	0.6738	0.7692	0.7184	0.7962
0.439	8.0	80	0.6706	{'precision': 0.696068012752391, 'recall': 0.8096415327564895, 'f1': 0.7485714285714284, 'number': 809}	{'precision': 0.2184873949579832, 'recall': 0.2184873949579832, 'f1': 0.2184873949579832, 'number': 119}	{'precision': 0.7454858125537404, 'recall': 0.8140845070422535, 'f1': 0.7782764811490125, 'number': 1065}	0.6964	0.7767	0.7343	0.8022
0.3922	9.0	90	0.6689	{'precision': 0.707742639040349, 'recall': 0.8022249690976514, 'f1': 0.7520278099652375, 'number': 809}	{'precision': 0.21774193548387097, 'recall': 0.226890756302521, 'f1': 0.2222222222222222, 'number': 119}	{'precision': 0.7601380500431406, 'recall': 0.8272300469483568, 'f1': 0.7922661870503598, 'number': 1065}	0.7077	0.7812	0.7427	0.8038
0.3518	10.0	100	0.6692	{'precision': 0.7065677966101694, 'recall': 0.8244746600741656, 'f1': 0.7609811751283514, 'number': 809}	{'precision': 0.23529411764705882, 'recall': 0.23529411764705882, 'f1': 0.23529411764705882, 'number': 119}	{'precision': 0.7663469921534438, 'recall': 0.8253521126760563, 'f1': 0.7947558770343581, 'number': 1065}	0.7122	0.7898	0.7490	0.8092
0.3165	11.0	110	0.6863	{'precision': 0.714902807775378, 'recall': 0.8182941903584673, 'f1': 0.7631123919308358, 'number': 809}	{'precision': 0.2631578947368421, 'recall': 0.29411764705882354, 'f1': 0.27777777777777773, 'number': 119}	{'precision': 0.7724867724867724, 'recall': 0.8225352112676056, 'f1': 0.7967257844474761, 'number': 1065}	0.7173	0.7893	0.7516	0.8083
0.3043	12.0	120	0.6898	{'precision': 0.7173678532901834, 'recall': 0.8220024721878862, 'f1': 0.7661290322580644, 'number': 809}	{'precision': 0.27692307692307694, 'recall': 0.3025210084033613, 'f1': 0.2891566265060241, 'number': 119}	{'precision': 0.7812223206377326, 'recall': 0.828169014084507, 'f1': 0.8040109389243391, 'number': 1065}	0.7242	0.7943	0.7576	0.8084
0.2853	13.0	130	0.6935	{'precision': 0.7167755991285403, 'recall': 0.8133498145859085, 'f1': 0.7620150550086855, 'number': 809}	{'precision': 0.30303030303030304, 'recall': 0.33613445378151263, 'f1': 0.3187250996015936, 'number': 119}	{'precision': 0.7855251544571933, 'recall': 0.8356807511737089, 'f1': 0.8098271155595996, 'number': 1065}	0.7274	0.7968	0.7605	0.8109
0.2724	14.0	140	0.6985	{'precision': 0.7212581344902386, 'recall': 0.8220024721878862, 'f1': 0.7683419988445985, 'number': 809}	{'precision': 0.2900763358778626, 'recall': 0.31932773109243695, 'f1': 0.304, 'number': 119}	{'precision': 0.786096256684492, 'recall': 0.828169014084507, 'f1': 0.8065843621399177, 'number': 1065}	0.7287	0.7953	0.7606	0.8091
0.2741	15.0	150	0.6993	{'precision': 0.7155172413793104, 'recall': 0.8207663782447466, 'f1': 0.7645365572826713, 'number': 809}	{'precision': 0.2781954887218045, 'recall': 0.31092436974789917, 'f1': 0.2936507936507936, 'number': 119}	{'precision': 0.783303730017762, 'recall': 0.828169014084507, 'f1': 0.8051118210862619, 'number': 1065}	0.7238	0.7943	0.7574	0.8095

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.13.1
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jun 26, 2023
Updated: Jun 27, 2023