layoutlm-funsd

dwitidibyajyoti/layoutlm-funsd

layoutlm-funsd is a token classification model from dwitidibyajyoti. 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

14% of all-time downloads

All-time downloads

Public

Repo size

901 MB

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: Sep 2, 2023
Updated: Sep 2, 2023
SHA: 949463b1

Try a prompt

Base models

microsoft/layoutlm-base-uncased

Model card

From the Hugging Face model README

layoutlm-funsd

This model is a fine-tuned version of microsoft/layoutlm-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8927
Column: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}
Ignore: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}
Key: {'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}
Value: {'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}
Overall Precision: 0.6875
Overall Recall: 0.4231
Overall F1: 0.5238
Overall Accuracy: 0.7947

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	Column	Ignore	Key	Value	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
2.4627	1.0	2	2.1288	{'precision': 0.23529411764705882, 'recall': 0.16, 'f1': 0.19047619047619052, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.06060606060606061, 'recall': 0.06060606060606061, 'f1': 0.06060606060606061, 'number': 33}	0.0870	0.0769	0.0816	0.6887
2.1025	2.0	4	1.7650	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6921
1.7503	3.0	6	1.4611	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6904
1.4557	4.0	8	1.2624	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6904
1.3067	5.0	10	1.1889	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6904
1.1884	6.0	12	1.1436	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6904
1.1456	7.0	14	1.0901	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 17}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 33}	0.0	0.0	0.0	0.6904
1.0915	8.0	16	1.0410	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 0.11764705882352941, 'f1': 0.21052631578947367, 'number': 17}	{'precision': 0.3333333333333333, 'recall': 0.030303030303030304, 'f1': 0.05555555555555555, 'number': 33}	0.6	0.0385	0.0723	0.6937
1.0428	9.0	18	0.9990	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 0.29411764705882354, 'f1': 0.45454545454545453, 'number': 17}	{'precision': 0.23529411764705882, 'recall': 0.12121212121212122, 'f1': 0.16, 'number': 33}	0.2727	0.1154	0.1622	0.7252
0.9819	10.0	20	0.9639	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 0.4117647058823529, 'f1': 0.5833333333333334, 'number': 17}	{'precision': 0.2631578947368421, 'recall': 0.15151515151515152, 'f1': 0.19230769230769232, 'number': 33}	0.3243	0.1538	0.2087	0.7517
0.9592	11.0	22	0.9344	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 0.6470588235294118, 'f1': 0.7857142857142858, 'number': 17}	{'precision': 0.3684210526315789, 'recall': 0.21212121212121213, 'f1': 0.2692307692307693, 'number': 33}	0.4737	0.2308	0.3103	0.7781
0.9011	12.0	24	0.9105	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}	{'precision': 0.64, 'recall': 0.48484848484848486, 'f1': 0.5517241379310344, 'number': 33}	0.66	0.4231	0.5156	0.7930
0.9426	13.0	26	0.8927	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}	{'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}	0.6875	0.4231	0.5238	0.7947
0.8809	14.0	28	0.8821	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}	{'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}	0.6875	0.4231	0.5238	0.7947
0.9188	15.0	30	0.8774	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 25}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 3}	{'precision': 1.0, 'recall': 1.0, 'f1': 1.0, 'number': 17}	{'precision': 0.6666666666666666, 'recall': 0.48484848484848486, 'f1': 0.5614035087719298, 'number': 33}	0.6875	0.4231	0.5238	0.7947

Framework versions

Transformers 4.32.1
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3

Task: Token Classification
Library: transformers
Type: layoutlm
Created: Sep 2, 2023
Updated: Sep 2, 2023