metadata

tags:
  - generated_from_trainer
datasets:
  - chn_senti_corp
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: kt_punc
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: chn_senti_corp
          type: chn_senti_corp
          args: default
        metrics:
          - name: Precision
            type: precision
            value: 0.7078651685393258
          - name: Recall
            type: recall
            value: 0.7313662547821116
          - name: F1
            type: f1
            value: 0.7194238380517767
          - name: Accuracy
            type: accuracy
            value: 0.957316742326961

kt_punc

This model is a fine-tuned version of bert-base-chinese on the chn_senti_corp dataset. It achieves the following results on the evaluation set:

Loss: 0.1703
Precision: 0.7079
Recall: 0.7314
F1: 0.7194
Accuracy: 0.9573

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: 16
eval_batch_size: 16
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
0.1661	1.0	600	0.1351	0.6566	0.6833	0.6697	0.9498
0.1246	2.0	1200	0.1330	0.6854	0.6665	0.6758	0.9521
0.1121	3.0	1800	0.1303	0.6885	0.6994	0.6939	0.9537
0.1008	4.0	2400	0.1359	0.6836	0.7248	0.7036	0.9543
0.0809	5.0	3000	0.1404	0.7035	0.7082	0.7059	0.9559
0.0696	6.0	3600	0.1449	0.6986	0.7224	0.7103	0.9560
0.0628	7.0	4200	0.1563	0.7063	0.7214	0.7138	0.9567
0.0561	8.0	4800	0.1618	0.7024	0.7333	0.7175	0.9568
0.0525	9.0	5400	0.1669	0.7083	0.7335	0.7207	0.9574
0.0453	10.0	6000	0.1703	0.7079	0.7314	0.7194	0.9573

Framework versions

Transformers 4.19.1
Pytorch 1.11.0+cu113
Datasets 2.2.1
Tokenizers 0.12.1