metadata

tags:
  - generated_from_trainer
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: RoBERTa-ext-large-crf-chinese-finetuned-ner
    results: []

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

This model was trained from scratch on an unknown 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