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--- |
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license: cc-by-sa-4.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: bert-finetuned-japanese-sentiment |
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results: [] |
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language: |
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- ja |
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pipeline_tag: text-classification |
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metrics: |
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- accuracy |
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--- |
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# bert-finetuned-japanese-sentiment |
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This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v2](https://huggingface.co/cl-tohoku/bert-base-japanese-v2) on product amazon reviews japanese dataset. |
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## Model description |
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Model Train for amazon reviews Japanese sentence sentiments. |
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Sentiment analysis is a common task in natural language processing. It consists of classifying the polarity of a given text at the sentence or document level. For instance, the sentence "The food is good" has a positive sentiment, while the sentence "The food is bad" has a negative sentiment. |
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In this model, we fine-tuned a BERT model on a Japanese sentiment analysis dataset. The dataset contains 20,000 sentences extracted from Amazon reviews. Each sentence is labeled as positive, neutral, or negative. The model was trained for 5 epochs with a batch size of 16. |
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## Training and evaluation data |
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- Epochs: 6 |
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- Training Loss: 0.087600 |
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- Validation Loss: 1.028876 |
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- Accuracy: 0.813202 |
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- Precision: 0.712440 |
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- Recall: 0.756031 |
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- F1: 0.728455 |
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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: 0 |
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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: 6 |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 2.0.0+cu118 |
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- Tokenizers 0.13.2 |
