readme: ---
  language: ja
  thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png
  tags:
  - ja
  - japanese
  - gpt2
  - text-generation
  - lm
  - nlp
  license: mit
  datasets:
  - cc100
  - wikipedia
  widget:
  - text: "生命、宇宙、そして万物についての究極の疑問の答えは"
  ---
  
  # japanese-gpt2-medium
  
  ![rinna-icon](./rinna.png)
  
  This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/)
  
  # How to use the model
  
  *NOTE:* Use `T5Tokenizer` to initiate the tokenizer.
  
  ~~~~
  from transformers import T5Tokenizer, AutoModelForCausalLM
  
  tokenizer = T5Tokenizer.from_pretrained("rinna/japanese-gpt2-medium")
  tokenizer.do_lower_case = True  # due to some bug of tokenizer config loading
  
  model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-medium")
  ~~~~
  
  # Model architecture
  A 24-layer, 1024-hidden-size transformer-based language model.
  
  # Training
  The model was trained on [Japanese CC-100](http://data.statmt.org/cc-100/ja.txt.xz) and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective on 8\\*V100 GPUs for around 30 days. It reaches around 18 perplexity on a chosen validation set from the same data.
  
  # Tokenization
  The model uses a [sentencepiece](https://github.com/google/sentencepiece)-based tokenizer, the vocabulary was trained on the Japanese Wikipedia using the official sentencepiece training script.
  
  # Licenese
  [The MIT license](https://opensource.org/licenses/MIT)
  