layoutlm-funsd

joeljoseph1599/layoutlm-funsd

layoutlm-funsd is a token classification model from joeljoseph1599. 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. --

Downloads · 30 days

2% of all-time downloads

All-time downloads

278

Public

Repo size

6.8 GB

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.bin451 MB · 100%

At a glance

Task: Token Classification
Library: transformers
Model type: layoutlm
Access: Public
Created: Jul 31, 2023
Updated: Aug 2, 2023
SHA: 6df84255

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: 0.6510
Answer: {'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}
Header: {'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}
Question: {'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065}
Overall Precision: 0.7090
Overall Recall: 0.7737
Overall F1: 0.7399
Overall Accuracy: 0.8032

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
1.7428	1.0	10	1.5458	{'precision': 0.030690537084398978, 'recall': 0.04449938195302843, 'f1': 0.036326942482341064, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.18740157480314962, 'recall': 0.22347417840375586, 'f1': 0.2038543897216274, 'number': 1065}	0.1122	0.1375	0.1235	0.4326
1.3991	2.0	20	1.2229	{'precision': 0.1326676176890157, 'recall': 0.11495673671199011, 'f1': 0.12317880794701987, 'number': 809}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119}	{'precision': 0.5, 'recall': 0.5352112676056338, 'f1': 0.5170068027210885, 'number': 1065}	0.3597	0.3327	0.3457	0.5731
1.0911	3.0	30	0.9391	{'precision': 0.47231638418079097, 'recall': 0.5166872682323856, 'f1': 0.4935064935064935, 'number': 809}	{'precision': 0.058823529411764705, 'recall': 0.01680672268907563, 'f1': 0.026143790849673203, 'number': 119}	{'precision': 0.6528268551236749, 'recall': 0.6938967136150235, 'f1': 0.6727355484751935, 'number': 1065}	0.5651	0.5815	0.5732	0.7183
0.8461	4.0	40	0.7784	{'precision': 0.6047717842323651, 'recall': 0.7206427688504327, 'f1': 0.6576424139875917, 'number': 809}	{'precision': 0.15384615384615385, 'recall': 0.06722689075630252, 'f1': 0.0935672514619883, 'number': 119}	{'precision': 0.6666666666666666, 'recall': 0.7455399061032864, 'f1': 0.7039007092198581, 'number': 1065}	0.6275	0.6949	0.6595	0.7638
0.6966	5.0	50	0.7307	{'precision': 0.6315228966986155, 'recall': 0.7330037082818294, 'f1': 0.6784897025171623, 'number': 809}	{'precision': 0.21052631578947367, 'recall': 0.13445378151260504, 'f1': 0.1641025641025641, 'number': 119}	{'precision': 0.6925064599483204, 'recall': 0.7549295774647887, 'f1': 0.7223719676549865, 'number': 1065}	0.6494	0.7090	0.6779	0.7703
0.6037	6.0	60	0.6834	{'precision': 0.657922350472193, 'recall': 0.7750309023485785, 'f1': 0.7116912599318955, 'number': 809}	{'precision': 0.3150684931506849, 'recall': 0.19327731092436976, 'f1': 0.23958333333333334, 'number': 119}	{'precision': 0.7021103896103896, 'recall': 0.812206572769953, 'f1': 0.7531562908141053, 'number': 1065}	0.6709	0.7602	0.7128	0.7915
0.5421	7.0	70	0.6692	{'precision': 0.671306209850107, 'recall': 0.7750309023485785, 'f1': 0.7194492254733217, 'number': 809}	{'precision': 0.2823529411764706, 'recall': 0.20168067226890757, 'f1': 0.23529411764705882, 'number': 119}	{'precision': 0.7227467811158799, 'recall': 0.7906103286384977, 'f1': 0.7551569506726458, 'number': 1065}	0.6836	0.7491	0.7149	0.7931
0.5085	8.0	80	0.6549	{'precision': 0.6901874310915105, 'recall': 0.7737948084054388, 'f1': 0.7296037296037297, 'number': 809}	{'precision': 0.3023255813953488, 'recall': 0.2184873949579832, 'f1': 0.25365853658536586, 'number': 119}	{'precision': 0.7408637873754153, 'recall': 0.8375586854460094, 'f1': 0.7862494490965183, 'number': 1065}	0.7028	0.7747	0.7370	0.7982
0.4692	9.0	90	0.6517	{'precision': 0.6973684210526315, 'recall': 0.7861557478368356, 'f1': 0.7391051714119697, 'number': 809}	{'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119}	{'precision': 0.7534364261168385, 'recall': 0.8234741784037559, 'f1': 0.7868999551368328, 'number': 1065}	0.7100	0.7727	0.7400	0.8025
0.4538	10.0	100	0.6510	{'precision': 0.7025527192008879, 'recall': 0.7824474660074165, 'f1': 0.7403508771929823, 'number': 809}	{'precision': 0.28421052631578947, 'recall': 0.226890756302521, 'f1': 0.25233644859813087, 'number': 119}	{'precision': 0.7480916030534351, 'recall': 0.828169014084507, 'f1': 0.7860962566844921, 'number': 1065}	0.7090	0.7737	0.7399	0.8032

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.2
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Jul 31, 2023
Updated: Aug 2, 2023