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…
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