layoutlm-funsd

Haios141/layoutlm-funsd

layoutlm-funsd is a token classification model from Haios141. 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: Jul 22, 2023
Updated: Aug 11, 2023
SHA: ec6c57bc

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: 1.3057
Answer: {'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809}
Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}
Question: {'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065}
Overall Precision: 0.2807
Overall Recall: 0.2730
Overall F1: 0.2768
Overall Accuracy: 0.5691

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: 5e-06
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
1.9048	1.0	10	1.8492	{'precision': 0.02683982683982684, 'recall': 0.07663782447466007, 'f1': 0.039756332157742866, 'number': 809}	{'precision': 0.003424657534246575, 'recall': 0.008403361344537815, 'f1': 0.004866180048661801, 'number': 119}	{'precision': 0.08558262014483213, 'recall': 0.12206572769953052, 'f1': 0.10061919504643962, 'number': 1065}	0.0468	0.0968	0.0631	0.2625
1.8261	2.0	20	1.7805	{'precision': 0.02488425925925926, 'recall': 0.05315203955500618, 'f1': 0.03389830508474576, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.11639344262295082, 'recall': 0.13333333333333333, 'f1': 0.12428884026258205, 'number': 1065}	0.0620	0.0928	0.0744	0.3314
1.7557	3.0	30	1.7197	{'precision': 0.018808777429467086, 'recall': 0.029666254635352288, 'f1': 0.02302158273381295, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.15336134453781514, 'recall': 0.13708920187793427, 'f1': 0.14476945959345563, 'number': 1065}	0.0763	0.0853	0.0805	0.3579
1.7002	4.0	40	1.6648	{'precision': 0.019029495718363463, 'recall': 0.024721878862793572, 'f1': 0.02150537634408602, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.19602977667493796, 'recall': 0.14835680751173708, 'f1': 0.16889363976483165, 'number': 1065}	0.0959	0.0893	0.0925	0.3775
1.645	5.0	50	1.6121	{'precision': 0.019801980198019802, 'recall': 0.024721878862793572, 'f1': 0.021990104452996154, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.22172452407614782, 'recall': 0.18591549295774648, 'f1': 0.20224719101123598, 'number': 1065}	0.1146	0.1094	0.1119	0.4091
1.5951	6.0	60	1.5596	{'precision': 0.029411764705882353, 'recall': 0.037082818294190356, 'f1': 0.032804811372334604, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.23694779116465864, 'recall': 0.2215962441314554, 'f1': 0.2290150412421155, 'number': 1065}	0.1319	0.1335	0.1327	0.4421
1.5418	7.0	70	1.5109	{'precision': 0.040755467196819085, 'recall': 0.05067985166872682, 'f1': 0.04517906336088154, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.27926267281105993, 'recall': 0.28450704225352114, 'f1': 0.2818604651162791, 'number': 1065}	0.1645	0.1726	0.1685	0.4719
1.4954	8.0	80	1.4653	{'precision': 0.050359712230215826, 'recall': 0.06056860321384425, 'f1': 0.05499438832772166, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3016421780466724, 'recall': 0.3276995305164319, 'f1': 0.31413141314131415, 'number': 1065}	0.1869	0.1997	0.1931	0.4973
1.4558	9.0	90	1.4245	{'precision': 0.054140127388535034, 'recall': 0.0630407911001236, 'f1': 0.05825242718446602, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3177966101694915, 'recall': 0.352112676056338, 'f1': 0.3340757238307349, 'number': 1065}	0.2008	0.2137	0.2070	0.5168
1.4126	10.0	100	1.3893	{'precision': 0.07432432432432433, 'recall': 0.0815822002472188, 'f1': 0.07778432527990571, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.33669185558354325, 'recall': 0.37652582159624415, 'f1': 0.3554964539007092, 'number': 1065}	0.2246	0.2343	0.2294	0.5339
1.3759	11.0	110	1.3592	{'precision': 0.08333333333333333, 'recall': 0.0865265760197775, 'f1': 0.08489993935718616, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3618807724601176, 'recall': 0.40469483568075115, 'f1': 0.38209219858156024, 'number': 1065}	0.2467	0.2514	0.2490	0.5470
1.3663	12.0	120	1.3358	{'precision': 0.08531994981179424, 'recall': 0.08405438813349815, 'f1': 0.08468244084682441, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.37638062871707734, 'recall': 0.415962441314554, 'f1': 0.39518287243532557, 'number': 1065}	0.2589	0.2564	0.2576	0.5545
1.3323	13.0	130	1.3192	{'precision': 0.0916030534351145, 'recall': 0.08899876390605686, 'f1': 0.090282131661442, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.38649789029535864, 'recall': 0.4300469483568075, 'f1': 0.40711111111111115, 'number': 1065}	0.2689	0.2659	0.2674	0.5635
1.3268	14.0	140	1.3094	{'precision': 0.09585492227979274, 'recall': 0.09147095179233622, 'f1': 0.09361163820366855, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.3974358974358974, 'recall': 0.43661971830985913, 'f1': 0.4161073825503355, 'number': 1065}	0.2775	0.2704	0.2740	0.5671
1.2988	15.0	150	1.3057	{'precision': 0.09480519480519481, 'recall': 0.09023485784919653, 'f1': 0.09246358454718177, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4032534246575342, 'recall': 0.4422535211267606, 'f1': 0.4218540080609046, 'number': 1065}	0.2807	0.2730	0.2768	0.5691

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu117
Datasets 2.14.4
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jul 22, 2023
Updated: Aug 11, 2023