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
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Model tree for WhitePeak/bert-base-cased-Korean-sentiment
Base model
google-bert/bert-base-multilingual-cased