---
license: apache-2.0
base_model: cl-tohoku/bert-base-japanese-v3
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: jp-speech-classifier
  results: []
---

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# jp-speech-classifier

This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-v3](https://huggingface.co/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