lilt-en-funsd

LeamonLee/lilt-en-funsd

lilt-en-funsd is a token classification model from LeamonLee. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as mit.

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

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At a glance

Task: Token Classification
Library: transformers
License: mit
Model type: lilt
Access: Public
Created: Dec 24, 2023
Updated: Dec 24, 2023
SHA: 19529251

Try a prompt

Base models

SCUT-DLVCLab/lilt-roberta-en-base

Model card

From the Hugging Face model README

lilt-en-funsd

This model is a fine-tuned version of SCUT-DLVCLab/lilt-roberta-en-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.6973
Answer: {'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}
Header: {'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119}
Question: {'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077}
Overall Precision: 0.8792
Overall Recall: 0.8927
Overall F1: 0.8859
Overall Accuracy: 0.8011

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-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
training_steps: 2500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.1857	26.32	500	1.4181	{'precision': 0.8298109010011123, 'recall': 0.9130966952264382, 'f1': 0.8694638694638694, 'number': 817}	{'precision': 0.6923076923076923, 'recall': 0.5294117647058824, 'f1': 0.5999999999999999, 'number': 119}	{'precision': 0.886672710788758, 'recall': 0.9080779944289693, 'f1': 0.8972477064220182, 'number': 1077}	0.8538	0.8877	0.8704	0.7981
0.0068	52.63	1000	1.6084	{'precision': 0.8581235697940504, 'recall': 0.9179926560587516, 'f1': 0.8870490833826139, 'number': 817}	{'precision': 0.5877192982456141, 'recall': 0.5630252100840336, 'f1': 0.5751072961373391, 'number': 119}	{'precision': 0.9083255378858747, 'recall': 0.9015784586815228, 'f1': 0.9049394221808015, 'number': 1077}	0.8692	0.8882	0.8786	0.7956
0.0018	78.95	1500	1.6068	{'precision': 0.8742655699177438, 'recall': 0.9106487148102815, 'f1': 0.8920863309352519, 'number': 817}	{'precision': 0.6050420168067226, 'recall': 0.6050420168067226, 'f1': 0.6050420168067226, 'number': 119}	{'precision': 0.902867715078631, 'recall': 0.9062209842154132, 'f1': 0.9045412418906396, 'number': 1077}	0.8737	0.8902	0.8819	0.8095
0.0007	105.26	2000	1.6522	{'precision': 0.8611111111111112, 'recall': 0.9106487148102815, 'f1': 0.8851873884592504, 'number': 817}	{'precision': 0.6126126126126126, 'recall': 0.5714285714285714, 'f1': 0.591304347826087, 'number': 119}	{'precision': 0.9098513011152416, 'recall': 0.9090064995357474, 'f1': 0.9094287041337669, 'number': 1077}	0.8732	0.8897	0.8814	0.8028
0.0002	131.58	2500	1.6973	{'precision': 0.8658109684947491, 'recall': 0.9082007343941249, 'f1': 0.886499402628435, 'number': 817}	{'precision': 0.6770833333333334, 'recall': 0.5462184873949579, 'f1': 0.6046511627906976, 'number': 119}	{'precision': 0.9074243813015582, 'recall': 0.9192200557103064, 'f1': 0.9132841328413284, 'number': 1077}	0.8792	0.8927	0.8859	0.8011

Framework versions

Transformers 4.36.2
Pytorch 2.1.0+cu121
Datasets 2.16.0
Tokenizers 0.15.0

Task: Token Classification
Library: transformers
Type: lilt
License: mit
Created: Dec 24, 2023
Updated: Dec 24, 2023