bert-base-cased-Korean-sentiment

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bert-base-cased-Korean-sentiment

This model is a fine-tuned version of bert-base-multilingual-cased on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.2338
• Accuracy: 0.9234
• F1: 0.9238

Model description

This is a fine-tuned model for a sentiment analysis for the Korean language based on customer reviews in the Korean language

Intended uses & limitations

from transformers import pipeline

sentiment_model = pipeline(model="WhitePeak/bert-base-cased-Korean-sentiment")
sentiment_mode("매우 좋아")

Result:

LABEL_0: negative
LABEL_1: positive

Training and evaluation data

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: 2

Training results

Framework versions

• Transformers 4.33.2
• Pytorch 2.0.1+cu118
• Datasets 2.14.5
• Tokenizers 0.13.3