jp-speech-classifier

This model is a fine-tuned version of cl-tohoku/bert-base-japanese-v3 on a dataset created from speech records in the Japanese diet. It achieves the following results on the evaluation set:

• Loss: 1.1895
• Accuracy: 0.7053

Model description

This model classifies Japanese sentences into factual, question, descriptive, opinion based and other sentences.

Intended uses & limitations

This model can be used for any purpose that requires sentence categorization of Japanese sentences. The dataset is fairly small but it gets the job done.

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	1.0	72	1.1048	0.6772
No log	2.0	144	1.1895	0.7053

Framework versions

• Transformers 4.31.0
• Pytorch 2.0.1+cu117
• Datasets 2.13.1
• Tokenizers 0.13.3