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--- |
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license: apache-2.0 |
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base_model: bert-base-multilingual-cased |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: bert-base-cased-Korean-sentiment |
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results: [] |
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datasets: |
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- WhitePeak/shopping_review |
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language: |
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- ko |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# bert-base-cased-Korean-sentiment |
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This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2338 |
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- Accuracy: 0.9234 |
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- F1: 0.9238 |
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## Model description |
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This is a fine-tuned model for a sentiment analysis for the Korean language based on customer reviews in the Korean language |
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## Intended uses & limitations |
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```python |
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from transformers import pipeline |
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sentiment_model = pipeline(model="WhitePeak/bert-base-cased-Korean-sentiment") |
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sentiment_mode("매우 좋아") |
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``` |
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Result: |
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``` |
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LABEL_0: negative |
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LABEL_1: positive |
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``` |
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## Training and evaluation data |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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### Framework versions |
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- Transformers 4.33.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
