layoutlm-funsd2

Benedict-L/layoutlm-funsd2

layoutlm-funsd2 is a machine learning model from Benedict-L. Use it for the machine learning task on the model card, and read the license before you ship it in a product. 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

Library: transformers
License: mit
Model type: layoutlm
Access: Public
Created: Jun 20, 2024
Updated: Jun 20, 2024
SHA: ffd9c00a

Base models

microsoft/layoutlm-base-uncased

Datasets

funsd

Model card

From the Hugging Face model README

layoutlm-funsd2

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: 0.6614
Answer: {'precision': 0.6683778234086243, 'recall': 0.8046971569839307, 'f1': 0.7302299495232752, 'number': 809}
Header: {'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}
Question: {'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065}
Overall Precision: 0.7010
Overall Recall: 0.7918
Overall F1: 0.7436
Overall Accuracy: 0.8029

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: 12
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.8071	1.0	10	1.5850	{'precision': 0.011918951132300357, 'recall': 0.012360939431396786, 'f1': 0.012135922330097087, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.17111459968602827, 'recall': 0.10234741784037558, 'f1': 0.1280846063454759, 'number': 1065}	0.0806	0.0597	0.0686	0.3795
1.4934	2.0	20	1.2707	{'precision': 0.09924812030075188, 'recall': 0.0815822002472188, 'f1': 0.08955223880597016, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4546952224052718, 'recall': 0.5183098591549296, 'f1': 0.484422992540588, 'number': 1065}	0.3289	0.3101	0.3192	0.5753
1.1823	3.0	30	0.9970	{'precision': 0.4033214709371293, 'recall': 0.42027194066749074, 'f1': 0.4116222760290557, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5919540229885057, 'recall': 0.6769953051643193, 'f1': 0.6316250547525186, 'number': 1065}	0.5106	0.5324	0.5212	0.6915
0.9185	4.0	40	0.8213	{'precision': 0.6075156576200418, 'recall': 0.7194066749072929, 'f1': 0.6587436332767402, 'number': 809}	{'precision': 0.05128205128205128, 'recall': 0.01680672268907563, 'f1': 0.025316455696202535, 'number': 119}	{'precision': 0.6559048428207307, 'recall': 0.7248826291079812, 'f1': 0.6886708296164139, 'number': 1065}	0.6237	0.6804	0.6508	0.7467
0.7233	5.0	50	0.7353	{'precision': 0.638974358974359, 'recall': 0.7700865265760197, 'f1': 0.6984304932735426, 'number': 809}	{'precision': 0.22093023255813954, 'recall': 0.15966386554621848, 'f1': 0.18536585365853656, 'number': 119}	{'precision': 0.6809716599190283, 'recall': 0.7896713615023474, 'f1': 0.731304347826087, 'number': 1065}	0.6459	0.7441	0.6915	0.7794
0.6262	6.0	60	0.7036	{'precision': 0.632512315270936, 'recall': 0.7935723114956736, 'f1': 0.7039473684210525, 'number': 809}	{'precision': 0.24324324324324326, 'recall': 0.15126050420168066, 'f1': 0.18652849740932642, 'number': 119}	{'precision': 0.7235345581802275, 'recall': 0.7765258215962442, 'f1': 0.7490942028985508, 'number': 1065}	0.6662	0.7461	0.7039	0.7818
0.5552	7.0	70	0.6694	{'precision': 0.6639089968976215, 'recall': 0.7935723114956736, 'f1': 0.722972972972973, 'number': 809}	{'precision': 0.24770642201834864, 'recall': 0.226890756302521, 'f1': 0.23684210526315788, 'number': 119}	{'precision': 0.730999146029035, 'recall': 0.8037558685446009, 'f1': 0.7656529516994633, 'number': 1065}	0.6787	0.7652	0.7193	0.7913
0.5016	8.0	80	0.6598	{'precision': 0.6592517694641051, 'recall': 0.8059332509270705, 'f1': 0.7252502780867631, 'number': 809}	{'precision': 0.24324324324324326, 'recall': 0.226890756302521, 'f1': 0.23478260869565218, 'number': 119}	{'precision': 0.7482817869415808, 'recall': 0.8178403755868544, 'f1': 0.781516375056079, 'number': 1065}	0.6846	0.7777	0.7282	0.7931
0.4496	9.0	90	0.6561	{'precision': 0.6663265306122449, 'recall': 0.8071693448702101, 'f1': 0.7300167691447736, 'number': 809}	{'precision': 0.2743362831858407, 'recall': 0.2605042016806723, 'f1': 0.26724137931034486, 'number': 119}	{'precision': 0.7584708948740226, 'recall': 0.819718309859155, 'f1': 0.7879061371841156, 'number': 1065}	0.6939	0.7812	0.7350	0.7982
0.4481	10.0	100	0.6633	{'precision': 0.6711340206185566, 'recall': 0.8046971569839307, 'f1': 0.7318718381112984, 'number': 809}	{'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119}	{'precision': 0.7640350877192983, 'recall': 0.8178403755868544, 'f1': 0.7900226757369614, 'number': 1065}	0.7003	0.7797	0.7379	0.7987
0.4012	11.0	110	0.6624	{'precision': 0.6625766871165644, 'recall': 0.8009888751545118, 'f1': 0.7252378287632905, 'number': 809}	{'precision': 0.3333333333333333, 'recall': 0.3025210084033613, 'f1': 0.3171806167400881, 'number': 119}	{'precision': 0.7696969696969697, 'recall': 0.8347417840375587, 'f1': 0.8009009009009008, 'number': 1065}	0.7019	0.7893	0.7430	0.8074
0.4065	12.0	120	0.6614	{'precision': 0.6683778234086243, 'recall': 0.8046971569839307, 'f1': 0.7302299495232752, 'number': 809}	{'precision': 0.3130434782608696, 'recall': 0.3025210084033613, 'f1': 0.3076923076923077, 'number': 119}	{'precision': 0.7667814113597247, 'recall': 0.8366197183098592, 'f1': 0.8001796138302649, 'number': 1065}	0.7010	0.7918	0.7436	0.8029

Framework versions

Transformers 4.41.2
Pytorch 2.3.1+cu121
Datasets 2.19.2
Tokenizers 0.19.1

Library: transformers
Type: layoutlm
License: mit
Created: Jun 20, 2024
Updated: Jun 20, 2024