lilt-ru-bio

Tatiana239/lilt-ru-bio

lilt-ru-bio is a token classification model from Tatiana239. 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: lilt
Access: Public
Created: Jan 17, 2023
Updated: Jan 17, 2023
SHA: 2bfbb58c

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Datasets

funsd-layoutlmv3

Model card

From the Hugging Face model README

lilt-ru-bio

This model was trained from scratch on the funsd-layoutlmv3 dataset. It achieves the following results on the evaluation set:

Loss: 1.4705
Answer: {'precision': 0.8711583924349882, 'recall': 0.9020807833537332, 'f1': 0.8863499699338545, 'number': 817}
Header: {'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}
Question: {'precision': 0.8966455122393472, 'recall': 0.9182915506035283, 'f1': 0.9073394495412844, 'number': 1077}
Overall Precision: 0.8732
Overall Recall: 0.8892
Overall F1: 0.8811
Overall Accuracy: 0.8223

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: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Answer	Header	Question	Overall Precision	Overall Recall	Overall F1	Overall Accuracy
0.0199	5.26	100	1.3310	{'precision': 0.8788627935723115, 'recall': 0.8702570379436965, 'f1': 0.8745387453874538, 'number': 817}	{'precision': 0.6288659793814433, 'recall': 0.5126050420168067, 'f1': 0.5648148148148148, 'number': 119}	{'precision': 0.8519148936170213, 'recall': 0.9294336118848654, 'f1': 0.8889875666074601, 'number': 1077}	0.8520	0.8808	0.8661	0.8038
0.0085	10.53	200	1.5426	{'precision': 0.8631578947368421, 'recall': 0.9033047735618115, 'f1': 0.8827751196172249, 'number': 817}	{'precision': 0.5641025641025641, 'recall': 0.5546218487394958, 'f1': 0.559322033898305, 'number': 119}	{'precision': 0.899812734082397, 'recall': 0.8922934076137419, 'f1': 0.8960372960372962, 'number': 1077}	0.8652	0.8768	0.8710	0.8120
0.0047	15.79	300	1.5043	{'precision': 0.8698710433763188, 'recall': 0.9082007343941249, 'f1': 0.8886227544910178, 'number': 817}	{'precision': 0.5508474576271186, 'recall': 0.5462184873949579, 'f1': 0.5485232067510548, 'number': 119}	{'precision': 0.8980716253443526, 'recall': 0.9080779944289693, 'f1': 0.9030470914127423, 'number': 1077}	0.8665	0.8867	0.8765	0.8086
0.0017	21.05	400	1.4705	{'precision': 0.8711583924349882, 'recall': 0.9020807833537332, 'f1': 0.8863499699338545, 'number': 817}	{'precision': 0.6336633663366337, 'recall': 0.5378151260504201, 'f1': 0.5818181818181819, 'number': 119}	{'precision': 0.8966455122393472, 'recall': 0.9182915506035283, 'f1': 0.9073394495412844, 'number': 1077}	0.8732	0.8892	0.8811	0.8223
0.0012	26.32	500	1.5088	{'precision': 0.8744075829383886, 'recall': 0.9033047735618115, 'f1': 0.8886213124623721, 'number': 817}	{'precision': 0.5904761904761905, 'recall': 0.5210084033613446, 'f1': 0.5535714285714286, 'number': 119}	{'precision': 0.8935395814376706, 'recall': 0.9117920148560817, 'f1': 0.9025735294117648, 'number': 1077}	0.8701	0.8852	0.8776	0.8174

Framework versions

Transformers 4.25.1
Pytorch 1.12.1
Datasets 2.8.0
Tokenizers 0.13.2

Task: Token Classification
Library: transformers
Type: lilt
Created: Jan 17, 2023
Updated: Jan 17, 2023