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--- |
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language: ja |
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thumbnail: https://github.com/rinnakk/japanese-gpt2/blob/master/rinna.png |
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tags: |
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- ja |
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- japanese |
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- gpt2 |
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- text-generation |
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- lm |
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- nlp |
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license: mit |
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datasets: |
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- cc100 |
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- wikipedia |
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widget: |
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- text: "生命、宇宙、そして万物についての究極の疑問の答えは" |
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--- |
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# japanese-gpt2-xsmall |
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This repository provides an extra-small-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/) |
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# How to use the model |
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~~~~ |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-xsmall", use_fast=False) |
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tokenizer.do_lower_case = True # due to some bug of tokenizer config loading |
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model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt2-xsmall") |
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~~~~ |
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# Model architecture |
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A 6-layer, 512-hidden-size transformer-based language model. |
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# Training |
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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 4 days. It reaches around 28 perplexity on a chosen validation set from CC-100. |
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# Tokenization |
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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. |
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# Licenese |
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[The MIT license](https://opensource.org/licenses/MIT) |
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