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