---
language: ja
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
widget:
- source_sentence: "揚げ物が食べたい"
- sentences:
  - 天ぷらが食べたい
  - りんごが食べたい
  - 海外旅行に行きたい
  - 揚げ物は食べたくない
  - 外をランニングするのが好きです
license: cc-by-sa-4.0
---

# Sentence BERT base Japanese model

This repository contains a Sentence BERT base model for Japanese.

## Pretrained model

Pretrained BERT model [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) v1.0 is used

This model is trained on Japanese Wikipedia data and relased under [Creative Commons Attribution-ShareAlike 3.0](https://creativecommons.org/licenses/by-sa/3.0/) .

## Training data

[Japanese SNLI dataset](https://nlp.ist.i.kyoto-u.ac.jp/index.php?%E6%97%A5%E6%9C%AC%E8%AA%9ESNLI%28JSNLI%29%E3%83%87%E3%83%BC%E3%82%BF%E3%82%BB%E3%83%83%E3%83%88) released under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/) is used for training.

Original training dataset is splitted into train/valid dataset. Finally, follwoing data is prepared.

* Train data: 523,005 samples
* Valid data: 10,000 samples
* Test data: 3,916 samples

## Model description

`SentenceTransformer` model from the [sentence-transformers](https://github.com/UKPLab/sentence-transformers) library is used for training.
The model detail is as below.

```py
>>> sentence_transformers.SentenceTransformer("colorfulscoop/sbert-base-ja")
SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```

## Training

This model finetuned [colorfulscoop/bert-base-ja](https://huggingface.co/colorfulscoop/bert-base-ja) with Softmax classifier of 3 labels of SNLI. AdamW optimizer with learning rate of 2e-05 linearly warmed-up in 10% of train data was used. The model was trained in 1 epoch with batch size 8.

Note: in a original paper of [Sentence BERT](https://arxiv.org/abs/1908.10084), a batch size of the model trained on SNLI and Multi-Genle NLI was 16. In this model, the dataset is around half smaller than the origial one, therefore the batch size was set to half of the original batch size of 16.

Trainind was conducted on Ubuntu 18.04.5 LTS with one RTX 2080 Ti.

After training, test set accuracy reached to 0.8529.

Training code is available in [a GitHub repository](https://github.com/colorfulscoop/sbert-ja).

## Usage

First, install dependecies.

```sh
$ pip install sentence-transformers==2.0.0
```

Then initialize `SentenceTransformer` model and use `encode` method to convert to vectors.

```py
>>> from sentence_transformers import SentenceTransformer
>>> model = SentenceTransformer("colorfulscoop/sbert-base-ja")
>>> sentences = ["外をランニングするのが好きです", "海外旅行に行くのが趣味です"]
>>> model.encode(sentences)
```

## License

All the models included in this repository are licensed under [Creative Commons Attribution-ShareAlike 4.0](https://creativecommons.org/licenses/by-sa/4.0/).

**Disclaimer:** Use of this model is at your sole risk. Colorful Scoop makes no warranty or guarantee of any outputs from the model. Colorful Scoop is not liable for any trouble, loss, or damage arising from the model output.

**Author:** Colorful Scoop