layoutlm-funsd

jaeger1/layoutlm-funsd

layoutlm-funsd is a token classification model from jaeger1. 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: May 29, 2023
Updated: Jun 2, 2023
SHA: a08bd38a

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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: 0.6657
Answer: {'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}
Header: {'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}
Question: {'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}
Overall Precision: 0.7331
Overall Recall: 0.7923
Overall F1: 0.7615
Overall Accuracy: 0.8136

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
1.7892	1.0	10	1.6086	{'precision': 0.020948180815876516, 'recall': 0.023485784919653894, 'f1': 0.022144522144522148, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.20356472795497185, 'recall': 0.20375586854460093, 'f1': 0.20366025340215863, 'number': 1065}	0.1196	0.1184	0.1190	0.3742
1.4438	2.0	20	1.2175	{'precision': 0.22015915119363394, 'recall': 0.20519159456118666, 'f1': 0.2124120281509917, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.4544037412314887, 'recall': 0.5474178403755868, 'f1': 0.4965928449744464, 'number': 1065}	0.3677	0.3758	0.3717	0.5883
1.0512	3.0	30	0.9159	{'precision': 0.5192519251925193, 'recall': 0.5834363411619283, 'f1': 0.5494761350407451, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.6103327495621717, 'recall': 0.6544600938967137, 'f1': 0.6316266425011329, 'number': 1065}	0.5615	0.5866	0.5737	0.7102
0.8045	4.0	40	0.7549	{'precision': 0.6132264529058116, 'recall': 0.7564894932014833, 'f1': 0.6773657996679578, 'number': 809}	{'precision': 0.22, 'recall': 0.09243697478991597, 'f1': 0.13017751479289943, 'number': 119}	{'precision': 0.6795580110497238, 'recall': 0.6929577464788732, 'f1': 0.6861924686192468, 'number': 1065}	0.6378	0.6829	0.6596	0.7538
0.6559	5.0	50	0.6887	{'precision': 0.6546227417640808, 'recall': 0.761433868974042, 'f1': 0.704, 'number': 809}	{'precision': 0.25, 'recall': 0.16806722689075632, 'f1': 0.20100502512562815, 'number': 119}	{'precision': 0.6964285714285714, 'recall': 0.7323943661971831, 'f1': 0.7139588100686498, 'number': 1065}	0.6614	0.7105	0.6851	0.7764
0.547	6.0	60	0.6515	{'precision': 0.6659793814432989, 'recall': 0.7985166872682324, 'f1': 0.7262507026419337, 'number': 809}	{'precision': 0.2891566265060241, 'recall': 0.20168067226890757, 'f1': 0.23762376237623764, 'number': 119}	{'precision': 0.7140439932318104, 'recall': 0.7924882629107981, 'f1': 0.7512238540275923, 'number': 1065}	0.6774	0.7597	0.7162	0.7928
0.4923	7.0	70	0.6337	{'precision': 0.6784188034188035, 'recall': 0.7849196538936959, 'f1': 0.7277936962750717, 'number': 809}	{'precision': 0.2761904761904762, 'recall': 0.24369747899159663, 'f1': 0.2589285714285714, 'number': 119}	{'precision': 0.7371575342465754, 'recall': 0.8084507042253521, 'f1': 0.7711598746081505, 'number': 1065}	0.6904	0.7652	0.7258	0.8052
0.4463	8.0	80	0.6478	{'precision': 0.7045454545454546, 'recall': 0.7663782447466008, 'f1': 0.7341622261693309, 'number': 809}	{'precision': 0.2831858407079646, 'recall': 0.2689075630252101, 'f1': 0.27586206896551724, 'number': 119}	{'precision': 0.751937984496124, 'recall': 0.819718309859155, 'f1': 0.7843665768194069, 'number': 1065}	0.7080	0.7652	0.7355	0.8048
0.3974	9.0	90	0.6389	{'precision': 0.7029379760609358, 'recall': 0.7985166872682324, 'f1': 0.7476851851851851, 'number': 809}	{'precision': 0.2748091603053435, 'recall': 0.3025210084033613, 'f1': 0.288, 'number': 119}	{'precision': 0.7609254498714653, 'recall': 0.8338028169014085, 'f1': 0.7956989247311828, 'number': 1065}	0.7082	0.7878	0.7458	0.8060
0.3599	10.0	100	0.6429	{'precision': 0.7177777777777777, 'recall': 0.7985166872682324, 'f1': 0.7559976594499708, 'number': 809}	{'precision': 0.2966101694915254, 'recall': 0.29411764705882354, 'f1': 0.2953586497890296, 'number': 119}	{'precision': 0.7795275590551181, 'recall': 0.8366197183098592, 'f1': 0.8070652173913043, 'number': 1065}	0.7274	0.7888	0.7569	0.8139
0.3227	11.0	110	0.6510	{'precision': 0.710239651416122, 'recall': 0.8059332509270705, 'f1': 0.755066589461494, 'number': 809}	{'precision': 0.28205128205128205, 'recall': 0.2773109243697479, 'f1': 0.2796610169491525, 'number': 119}	{'precision': 0.7882037533512064, 'recall': 0.828169014084507, 'f1': 0.8076923076923077, 'number': 1065}	0.7275	0.7863	0.7557	0.8111
0.3156	12.0	120	0.6579	{'precision': 0.7245575221238938, 'recall': 0.8096415327564895, 'f1': 0.7647402218330415, 'number': 809}	{'precision': 0.2920353982300885, 'recall': 0.2773109243697479, 'f1': 0.28448275862068967, 'number': 119}	{'precision': 0.7926391382405745, 'recall': 0.8291079812206573, 'f1': 0.8104635153740247, 'number': 1065}	0.7372	0.7883	0.7619	0.8123
0.2935	13.0	130	0.6596	{'precision': 0.7119386637458927, 'recall': 0.8034610630407911, 'f1': 0.7549361207897795, 'number': 809}	{'precision': 0.2846715328467153, 'recall': 0.3277310924369748, 'f1': 0.3046875, 'number': 119}	{'precision': 0.7852112676056338, 'recall': 0.8375586854460094, 'f1': 0.8105406633348478, 'number': 1065}	0.7232	0.7933	0.7566	0.8131
0.2814	14.0	140	0.6629	{'precision': 0.7189901207464325, 'recall': 0.8096415327564895, 'f1': 0.7616279069767442, 'number': 809}	{'precision': 0.2923076923076923, 'recall': 0.31932773109243695, 'f1': 0.3052208835341365, 'number': 119}	{'precision': 0.7924528301886793, 'recall': 0.828169014084507, 'f1': 0.8099173553719008, 'number': 1065}	0.7312	0.7903	0.7596	0.8132
0.2762	15.0	150	0.6657	{'precision': 0.7226519337016575, 'recall': 0.8084054388133498, 'f1': 0.763127187864644, 'number': 809}	{'precision': 0.29545454545454547, 'recall': 0.3277310924369748, 'f1': 0.3107569721115538, 'number': 119}	{'precision': 0.7931960608773501, 'recall': 0.831924882629108, 'f1': 0.8120989917506873, 'number': 1065}	0.7331	0.7923	0.7615	0.8136

Framework versions

Transformers 4.29.2
Pytorch 2.0.1+cu118
Datasets 2.12.0
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: May 29, 2023
Updated: Jun 2, 2023