layoutlm-funsd

petebch666/layoutlm-funsd

layoutlm-funsd is a token classification model from petebch666. 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 25, 2022
Updated: Oct 25, 2022
SHA: e7834eec

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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: 0.6820
Answer: {'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}
Header: {'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}
Question: {'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}
Overall Precision: 0.7194
Overall Recall: 0.7797
Overall F1: 0.7484
Overall Accuracy: 0.8102

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
1.7857	1.0	10	1.5985	{'precision': 0.009248554913294798, 'recall': 0.009888751545117428, 'f1': 0.00955794504181601, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.1273972602739726, 'recall': 0.08732394366197183, 'f1': 0.10362116991643454, 'number': 1065}	0.0633	0.0507	0.0563	0.3562
1.4597	2.0	20	1.2331	{'precision': 0.18717683557394002, 'recall': 0.22373300370828184, 'f1': 0.20382882882882883, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4439461883408072, 'recall': 0.5577464788732395, 'f1': 0.4943820224719101, 'number': 1065}	0.3362	0.3889	0.3606	0.6007
1.0902	3.0	30	0.9489	{'precision': 0.4371069182389937, 'recall': 0.515451174289246, 'f1': 0.47305728871242203, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6257615317667538, 'recall': 0.6751173708920187, 'f1': 0.6495031616982836, 'number': 1065}	0.5311	0.5700	0.5499	0.6910
0.8339	4.0	40	0.7979	{'precision': 0.5977366255144033, 'recall': 0.7181705809641533, 'f1': 0.652442448062886, 'number': 809}	{'precision': 0.13513513513513514, 'recall': 0.08403361344537816, 'f1': 0.10362694300518135, 'number': 119}	{'precision': 0.6854545454545454, 'recall': 0.707981220657277, 'f1': 0.6965357967667436, 'number': 1065}	0.6267	0.6749	0.6499	0.7453
0.6983	5.0	50	0.7659	{'precision': 0.6392896781354052, 'recall': 0.7119901112484549, 'f1': 0.6736842105263159, 'number': 809}	{'precision': 0.19626168224299065, 'recall': 0.17647058823529413, 'f1': 0.18584070796460178, 'number': 119}	{'precision': 0.6688102893890675, 'recall': 0.7812206572769953, 'f1': 0.7206582936336077, 'number': 1065}	0.6345	0.7170	0.6733	0.7610
0.5815	6.0	60	0.6907	{'precision': 0.6410256410256411, 'recall': 0.7725587144622992, 'f1': 0.7006726457399104, 'number': 809}	{'precision': 0.23863636363636365, 'recall': 0.17647058823529413, 'f1': 0.20289855072463767, 'number': 119}	{'precision': 0.7027463651050081, 'recall': 0.8169014084507042, 'f1': 0.7555362570560139, 'number': 1065}	0.6588	0.7607	0.7061	0.7913
0.5044	7.0	70	0.6802	{'precision': 0.6727078891257996, 'recall': 0.7799752781211372, 'f1': 0.7223812249570692, 'number': 809}	{'precision': 0.26605504587155965, 'recall': 0.24369747899159663, 'f1': 0.2543859649122807, 'number': 119}	{'precision': 0.7305699481865285, 'recall': 0.7943661971830986, 'f1': 0.7611336032388665, 'number': 1065}	0.6830	0.7556	0.7175	0.7902
0.4534	8.0	80	0.6595	{'precision': 0.7018701870187019, 'recall': 0.788627935723115, 'f1': 0.7427240977881256, 'number': 809}	{'precision': 0.234375, 'recall': 0.25210084033613445, 'f1': 0.242914979757085, 'number': 119}	{'precision': 0.7378559463986599, 'recall': 0.8272300469483568, 'f1': 0.779991146525011, 'number': 1065}	0.6943	0.7772	0.7334	0.8074
0.3971	9.0	90	0.6625	{'precision': 0.6967032967032967, 'recall': 0.7836835599505563, 'f1': 0.7376381617219313, 'number': 809}	{'precision': 0.27007299270072993, 'recall': 0.31092436974789917, 'f1': 0.2890625, 'number': 119}	{'precision': 0.7433930093776641, 'recall': 0.8187793427230047, 'f1': 0.7792672028596961, 'number': 1065}	0.6950	0.7742	0.7325	0.8060
0.3593	10.0	100	0.6634	{'precision': 0.7079152731326644, 'recall': 0.7849196538936959, 'f1': 0.7444314185228605, 'number': 809}	{'precision': 0.2714285714285714, 'recall': 0.31932773109243695, 'f1': 0.29343629343629346, 'number': 119}	{'precision': 0.7571305099394987, 'recall': 0.8225352112676056, 'f1': 0.7884788478847885, 'number': 1065}	0.7060	0.7772	0.7399	0.8115
0.3209	11.0	110	0.6655	{'precision': 0.6973262032085561, 'recall': 0.8059332509270705, 'f1': 0.7477064220183487, 'number': 809}	{'precision': 0.2903225806451613, 'recall': 0.3025210084033613, 'f1': 0.2962962962962963, 'number': 119}	{'precision': 0.7788632326820604, 'recall': 0.8234741784037559, 'f1': 0.8005476951163851, 'number': 1065}	0.7162	0.7852	0.7492	0.8129
0.3064	12.0	120	0.6771	{'precision': 0.7104072398190046, 'recall': 0.7762669962917181, 'f1': 0.74187832250443, 'number': 809}	{'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}	{'precision': 0.7795698924731183, 'recall': 0.8169014084507042, 'f1': 0.797799174690509, 'number': 1065}	0.7166	0.7712	0.7429	0.8088
0.286	13.0	130	0.6765	{'precision': 0.7030905077262694, 'recall': 0.7873918417799752, 'f1': 0.7428571428571429, 'number': 809}	{'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}	{'precision': 0.769298245614035, 'recall': 0.8234741784037559, 'f1': 0.7954648526077097, 'number': 1065}	0.7088	0.7792	0.7424	0.8111
0.2806	14.0	140	0.6820	{'precision': 0.7052980132450332, 'recall': 0.7898640296662547, 'f1': 0.7451895043731779, 'number': 809}	{'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}	{'precision': 0.7793594306049823, 'recall': 0.8225352112676056, 'f1': 0.8003654636820466, 'number': 1065}	0.7145	0.7797	0.7457	0.8106
0.2736	15.0	150	0.6820	{'precision': 0.7084257206208425, 'recall': 0.7898640296662547, 'f1': 0.7469316189362946, 'number': 809}	{'precision': 0.2689655172413793, 'recall': 0.3277310924369748, 'f1': 0.2954545454545454, 'number': 119}	{'precision': 0.7870619946091644, 'recall': 0.8225352112676056, 'f1': 0.8044077134986226, 'number': 1065}	0.7194	0.7797	0.7484	0.8102

Framework versions

Transformers 4.23.1
Pytorch 1.12.1+cu113
Datasets 2.6.1
Tokenizers 0.13.1

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Oct 25, 2022
Updated: Oct 25, 2022