layoutlm-funsd

mouadhamri/layoutlm-funsd

layoutlm-funsd is a token classification model from mouadhamri. 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: Aug 26, 2023
Updated: Aug 26, 2023
SHA: eda49c4f

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: 0.7011
Answer: {'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}
Header: {'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}
Question: {'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}
Overall Precision: 0.7250
Overall Recall: 0.7873
Overall F1: 0.7549
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

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7566	1.0	10	1.5349	{'precision': 0.03646308113035551, 'recall': 0.049443757725587144, 'f1': 0.04197271773347323, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.16700819672131148, 'recall': 0.15305164319248826, 'f1': 0.15972562469377757, 'number': 1065}	0.0979	0.1019	0.0999	0.4336
1.4057	2.0	20	1.1865	{'precision': 0.17656500802568217, 'recall': 0.13597033374536466, 'f1': 0.15363128491620115, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.471847739888977, 'recall': 0.5586854460093896, 'f1': 0.5116079105760963, 'number': 1065}	0.3742	0.3537	0.3637	0.6016
1.0729	3.0	30	0.9241	{'precision': 0.49693251533742333, 'recall': 0.5006180469715699, 'f1': 0.4987684729064039, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6378708551483421, 'recall': 0.6863849765258216, 'f1': 0.6612392582541836, 'number': 1065}	0.5691	0.5700	0.5696	0.7181
0.8134	4.0	40	0.7831	{'precision': 0.6211640211640211, 'recall': 0.7255871446229913, 'f1': 0.669327251995439, 'number': 809}	{'precision': 0.09375, 'recall': 0.05042016806722689, 'f1': 0.0655737704918033, 'number': 119}	{'precision': 0.6889081455805892, 'recall': 0.7464788732394366, 'f1': 0.7165389815232085, 'number': 1065}	0.6417	0.6964	0.6679	0.7640
0.6582	5.0	50	0.7298	{'precision': 0.6422018348623854, 'recall': 0.7787391841779975, 'f1': 0.7039106145251396, 'number': 809}	{'precision': 0.2361111111111111, 'recall': 0.14285714285714285, 'f1': 0.17801047120418848, 'number': 119}	{'precision': 0.7311233885819521, 'recall': 0.7455399061032864, 'f1': 0.7382612738261274, 'number': 1065}	0.6737	0.7230	0.6975	0.7761
0.553	6.0	60	0.6763	{'precision': 0.6673532440782698, 'recall': 0.8009888751545118, 'f1': 0.7280898876404494, 'number': 809}	{'precision': 0.25806451612903225, 'recall': 0.20168067226890757, 'f1': 0.22641509433962265, 'number': 119}	{'precision': 0.735445205479452, 'recall': 0.8065727699530516, 'f1': 0.7693685624720108, 'number': 1065}	0.6859	0.7682	0.7247	0.7962
0.4805	7.0	70	0.6797	{'precision': 0.6904255319148936, 'recall': 0.8022249690976514, 'f1': 0.7421383647798742, 'number': 809}	{'precision': 0.25925925925925924, 'recall': 0.23529411764705882, 'f1': 0.24669603524229072, 'number': 119}	{'precision': 0.7363945578231292, 'recall': 0.8131455399061033, 'f1': 0.7728692547969657, 'number': 1065}	0.6938	0.7742	0.7318	0.7970
0.4259	8.0	80	0.6726	{'precision': 0.689401888772298, 'recall': 0.8121137206427689, 'f1': 0.7457434733257663, 'number': 809}	{'precision': 0.24786324786324787, 'recall': 0.24369747899159663, 'f1': 0.24576271186440676, 'number': 119}	{'precision': 0.7463581833761782, 'recall': 0.8178403755868544, 'f1': 0.7804659498207885, 'number': 1065}	0.6960	0.7812	0.7362	0.8020
0.3787	9.0	90	0.6784	{'precision': 0.7043956043956044, 'recall': 0.792336217552534, 'f1': 0.7457824316463061, 'number': 809}	{'precision': 0.26229508196721313, 'recall': 0.2689075630252101, 'f1': 0.26556016597510373, 'number': 119}	{'precision': 0.779707495429616, 'recall': 0.8009389671361502, 'f1': 0.7901806391848076, 'number': 1065}	0.7178	0.7657	0.7410	0.8026
0.3411	10.0	100	0.6821	{'precision': 0.7015086206896551, 'recall': 0.8046971569839307, 'f1': 0.7495682210708117, 'number': 809}	{'precision': 0.2708333333333333, 'recall': 0.3277310924369748, 'f1': 0.2965779467680608, 'number': 119}	{'precision': 0.775200713648528, 'recall': 0.815962441314554, 'f1': 0.7950594693504116, 'number': 1065}	0.7109	0.7822	0.7449	0.8047
0.313	11.0	110	0.7129	{'precision': 0.7111111111111111, 'recall': 0.7911001236093943, 'f1': 0.7489760093622002, 'number': 809}	{'precision': 0.2835820895522388, 'recall': 0.31932773109243695, 'f1': 0.30039525691699603, 'number': 119}	{'precision': 0.7816711590296496, 'recall': 0.8169014084507042, 'f1': 0.7988980716253444, 'number': 1065}	0.7210	0.7767	0.7478	0.7994
0.297	12.0	120	0.6955	{'precision': 0.708779443254818, 'recall': 0.8182941903584673, 'f1': 0.759609868043603, 'number': 809}	{'precision': 0.291044776119403, 'recall': 0.3277310924369748, 'f1': 0.308300395256917, 'number': 119}	{'precision': 0.783978397839784, 'recall': 0.8178403755868544, 'f1': 0.8005514705882352, 'number': 1065}	0.7214	0.7888	0.7536	0.8103
0.2907	13.0	130	0.7098	{'precision': 0.7092511013215859, 'recall': 0.796044499381953, 'f1': 0.7501456027955737, 'number': 809}	{'precision': 0.3142857142857143, 'recall': 0.3697478991596639, 'f1': 0.33976833976833976, 'number': 119}	{'precision': 0.7896678966789668, 'recall': 0.8037558685446009, 'f1': 0.796649604467194, 'number': 1065}	0.7242	0.7747	0.7486	0.8052
0.2701	14.0	140	0.7006	{'precision': 0.7133479212253829, 'recall': 0.8059332509270705, 'f1': 0.7568195008705745, 'number': 809}	{'precision': 0.3037037037037037, 'recall': 0.3445378151260504, 'f1': 0.3228346456692913, 'number': 119}	{'precision': 0.7894736842105263, 'recall': 0.8169014084507042, 'f1': 0.8029533917858791, 'number': 1065}	0.7266	0.7842	0.7543	0.8091
0.2649	15.0	150	0.7011	{'precision': 0.7142857142857143, 'recall': 0.8096415327564895, 'f1': 0.7589803012746235, 'number': 809}	{'precision': 0.2962962962962963, 'recall': 0.33613445378151263, 'f1': 0.31496062992125984, 'number': 119}	{'precision': 0.7859712230215827, 'recall': 0.8206572769953052, 'f1': 0.8029398254478639, 'number': 1065}	0.7250	0.7873	0.7549	0.8102

Framework versions

Transformers 4.32.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

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