bert-finetuned-japanese-sentiment
This model is a fine-tuned version of
cl-tohoku/bert-base-japanese-v2 on product amazon reviews japanese dataset.
Model description
Model Train for amazon reviews Japanese sentence sentiments.
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.
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.
Training and evaluation data
- Epochs: 6
- Training Loss: 0.087600
- Validation Loss: 1.028876
- Accuracy: 0.813202
- Precision: 0.712440
- Recall: 0.756031
- F1: 0.728455
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
Framework versions
- Transformers 4.27.4
- Pytorch 2.0.0+cu118
- Tokenizers 0.13.2
