README.md

metadata

base_model: kykim/albert-kor-base
tags:
  - generated_from_trainer
datasets:
  - nsmc
metrics:
  - accuracy
model-index:
  - name: electra2
    results:
      - task:
          name: Text Classification
          type: text-classification
        dataset:
          name: nsmc
          type: nsmc
          config: default
          split: test
          args: default
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.89276

electra2

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

• Loss: 0.3037
• Accuracy: 0.8928

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-06
• 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: cosine
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.2815	1.0	9375	0.2801	0.8863
0.2329	2.0	18750	0.2705	0.8920
0.1949	3.0	28125	0.3037	0.8928

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu118
• Datasets 2.14.4
• Tokenizers 0.13.3