metadata

tags:
  - generated_from_trainer
datasets:
  - nsmc
metrics:
  - accuracy
  - f1
  - recall
  - precision
base_model: beomi/kcbert-base
model-index:
  - name: kcbert-base-finetuned-nsmc
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: nsmc
          type: nsmc
          args: default
        metrics:
          - type: accuracy
            value: 0.90198
            name: Accuracy
          - type: f1
            value: 0.9033161705233671
            name: F1
          - type: recall
            value: 0.9095062169785088
            name: Recall
          - type: precision
            value: 0.8972098126812446
            name: Precision

kcbert-base-finetuned-nsmc

This model is a fine-tuned version of beomi/kcbert-base on the nsmc dataset. It achieves the following results on the evaluation set:

Loss: 0.4197
Accuracy: 0.9020
F1: 0.9033
Recall: 0.9095
Precision: 0.8972

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: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Recall	Precision
0.3028	0.32	3000	0.2994	0.8769	0.8732	0.8422	0.9066
0.2833	0.64	6000	0.2766	0.8880	0.8844	0.8512	0.9203
0.2719	0.96	9000	0.2527	0.8980	0.8981	0.8933	0.9030
0.1938	1.28	12000	0.2934	0.8969	0.8965	0.8869	0.9062
0.1907	1.6	15000	0.3141	0.8992	0.8999	0.9003	0.8996
0.1824	1.92	18000	0.3537	0.8986	0.8964	0.8711	0.9232
0.1261	2.24	21000	0.4197	0.9020	0.9033	0.9095	0.8972
0.1237	2.56	24000	0.4170	0.8995	0.9017	0.9156	0.8882
0.1182	2.88	27000	0.4165	0.9020	0.9036	0.9130	0.8945

Framework versions

Transformers 4.11.3
Pytorch 1.9.1
Datasets 1.14.0
Tokenizers 0.10.3