layoutlmv3-base-ner

Jacques2207/layoutlmv3-base-ner

layoutlmv3-base-ner is a token classification model from Jacques2207. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as cc-by-nc-sa-4.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Token Classification
Library: transformers
License: cc-by-nc-sa-4.0
Model type: layoutlmv3
Access: Public
Created: Mar 14, 2023
Updated: Mar 21, 2023
SHA: 22dfafa8

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Model card

From the Hugging Face model README

layoutlmv3-base-ner

This model is a fine-tuned version of microsoft/layoutlmv3-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.1562
Footer: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
Header: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}
Able: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}
Aption: {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}
Ext: {'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}
Overall Precision: 0.0310
Overall Recall: 0.1739
Overall F1: 0.0526
Overall Accuracy: 0.8882

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: 2
eval_batch_size: 2
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Footer	Header	Able	Aption	Ext	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
2.0796	1.0	5	1.4462	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}	{'precision': 0.05063291139240506, 'recall': 0.4, 'f1': 0.0898876404494382, 'number': 10}	0.0255	0.1739	0.0444	0.8518
1.2478	2.0	10	1.1562	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 1}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 5}	{'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 2}	{'precision': 0.06153846153846154, 'recall': 0.4, 'f1': 0.10666666666666667, 'number': 10}	0.0310	0.1739	0.0526	0.8882

Framework versions

Transformers 4.26.0
Pytorch 1.12.1
Datasets 2.9.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: layoutlmv3
License: cc-by-nc-sa-4.0
Created: Mar 14, 2023
Updated: Mar 21, 2023