gyr66/RoBERTa-ext-large-crf-chinese-finetuned-ner

Model

RoBERTa-ext-large-crf-chinese-finetuned-ner

0

7 commits

1 linked in READMEs

updated Jan 3, 2024

README

RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of chinese-roberta-wwm-ext-large on the gyr66/privacy_detection dataset. It achieves the following results on the evaluation set:

Loss: 0.7186
Precision: 0.6813
Recall: 0.7573
F1: 0.7173
Accuracy: 0.9639

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: 2e-05
train_batch_size: 4
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	Precision	Recall	F1	Accuracy
0.0197	1.0	503	0.6375	0.6663	0.7314	0.6973	0.9621
0.0251	2.0	1006	0.6048	0.6494	0.7435	0.6933	0.9611
0.0176	3.0	1509	0.6196	0.6669	0.7389	0.7011	0.9618
0.0116	4.0	2012	0.6361	0.6511	0.7560	0.6997	0.9624
0.0082	5.0	2515	0.6682	0.6746	0.7387	0.7052	0.9622
0.0067	6.0	3018	0.6587	0.6715	0.7409	0.7045	0.9635
0.0046	7.0	3521	0.6846	0.6770	0.7613	0.7167	0.9636
0.0019	8.0	4024	0.7081	0.6766	0.7510	0.7118	0.9630
0.0014	9.0	4527	0.7064	0.6812	0.7553	0.7163	0.9641
0.001	10.0	5030	0.7186	0.6813	0.7573	0.7173	0.9639

Framework versions

Transformers 4.35.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.0

bert

custom_code

generated_from_trainer

model-index

safetensors

token-classification

transformers

Contributors

7 commits

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Model

RoBERTa-ext-large-crf-chinese-finetuned-ner

0

7 commits

1 linked in READMEs

updated Jan 3, 2024

README

RoBERTa-ext-large-crf-chinese-finetuned-ner

This model is a fine-tuned version of chinese-roberta-wwm-ext-large on the gyr66/privacy_detection dataset. It achieves the following results on the evaluation set:

Loss: 0.7186
Precision: 0.6813
Recall: 0.7573
F1: 0.7173
Accuracy: 0.9639

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: 2e-05
train_batch_size: 4
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

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0.0197	1.0	503	0.6375	0.6663	0.7314	0.6973	0.9621
0.0251	2.0	1006	0.6048	0.6494	0.7435	0.6933	0.9611
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0.0014	9.0	4527	0.7064	0.6812	0.7553	0.7163	0.9641
0.001	10.0	5030	0.7186	0.6813	0.7573	0.7173	0.9639

Framework versions

Transformers 4.35.2
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Datasets 2.16.1
Tokenizers 0.15.0

bert

custom_code

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Contributors

7 commits