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This is a Japanese CLIP (Contrastive Language-Image Pre-Training) model trained by rinna Co., Ltd.. Please see japanese-clip for the other available models. The model was trained a ViT-B/16 Transformer architecture as an image encoder and uses a 12-layer BERT as a text encoder. The image encoder was initialized from the AugReg `vit-base-patch16-224` model.

Embeddings & similarity
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This repository provides a small-sized Japanese GPT-NeoX model. The model was trained using code based on EleutherAI/gpt-neox. * 2023/03/20 Update the model weight and config files such that it can be loaded via Huggingface's official GPT-NeoX implementation. tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-small", use_fast=False) model = GPTNeoXForCausalLM.from_pretrained("rinna/japanese-gpt-neox-small") ~~~~

Text generation
Input Text→ Output Text
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This repository provides a medium-sized Japanese GPT-2 model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt2-medium", use_fast=False) tokenizer.do_lower_case = True # due to some bug of tokenizer config loading The model was trained on Japanese CC-100 and Japanese Wikipedia 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.

Text generation
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This repository provides a base-sized Japanese RoBERTa model. The model was trained using code from Github repository rinnakk/japanese-pretrained-models by rinna Co., Ltd. tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-roberta-base", use_fast=False) tokenizer.do_lower_case = True # due to some bug of tokenizer config loading To predict a masked token, be sure to add a `[CLS]` token before the sentence for the model to correctly encode it, as it is used during the model training.

Fill-mask
Input Text→ Output Text
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