metadata
language: ja
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png
tags:
- gpt_neox
- text-generation
- lm
- nlp
license: mit
datasets:
- cc100
- wikipedia
- mc4
inference: false
japanese-gpt-neox-3.6b
Overview
This repository provides a Japanese GPT-NeoX model of 3.6 billion parameters.
Library
The model was trained using code based on EleutherAI/gpt-neox.
Model architecture
A 36-layer, 2816-hidden-size transformer-based language model.
Pre-training
The model was trained on around 312.5B tokens from Japanese CC-100, Japanese C4, and Japanese Wikipedia to optimize a traditional language modelling objective.
A final validation perplexity of 8.68 has been reached.
Model Series
Contributors
How to use the model
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False)
model = AutoModelForCausalLM.from_pretrained("rinna/japanese-gpt-neox-3.6b")
if torch.cuda.is_available():
model = model.to("cuda")
text = "西田幾多郎は、"
token_ids = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt")
with torch.no_grad():
output_ids = model.generate(
token_ids.to(model.device),
max_new_tokens=100,
min_new_tokens=100,
do_sample=True,
temperature=0.8,
pad_token_id=tokenizer.pad_token_id,
bos_token_id=tokenizer.bos_token_id,
eos_token_id=tokenizer.eos_token_id
)
output = tokenizer.decode(output_ids.tolist()[0])
print(output)
"""西田幾多郎は、この「絶対矛盾的自己同一」を「世界の自己同一」と置きかえ、さらに西田哲学を出発点として「絶対無」を「世界の成立」に変え、世界と自己を一つの統一物とみなす哲学として展開する。この世界と自己は絶対矛盾的自己同一として同一の性質を有し、同じ働きをする。西田哲学においては、この世界と自己は矛盾しあうのではなく、同一の性質をもっている。この世界と自己は同一である。絶対"""
Tokenization
The model uses a sentencepiece-based tokenizer.
- The tokenizer has a vocabulary size of 32,000.
- It uses sentencepiece's byte fallback feature to decompose unknown text pieces into UTF-8 byte pieces and to avoid producing
<UNK>tokens. - sentencepiece's
--add_dummy_prefixoption was turned off so that a leading whitespace will not be prepended automatically.print(tokenizer.tokenize("吾輩は猫である")) # ['吾', '輩', 'は', '猫', 'である'] # instead of ['▁', '吾', '輩', 'は', '猫', 'である'] as in rinna/japanese-gpt-1b - sentencepiece's
--remove_extra_whitespacesoption was turned off so that leading, trailing, and duplicate whitespaces are reserved.print(tokenizer.tokenize(" 吾輩は 猫である ")) # ['▁', '▁', '吾', '輩', 'は', '▁', '▁', '猫', 'である', '▁', '▁', '▁'] # instead of ['▁', '吾', '輩', 'は', '▁猫', 'である'] as in rinna/japanese-gpt-1b - Don't forget to set
use_fast=Falseto make the above features function correctly.good_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b", use_fast=False) bad_tokenizer = AutoTokenizer.from_pretrained("rinna/japanese-gpt-neox-3.6b") print(good_tokenizer.decode(good_tokenizer.encode("გამარჯობა 吾輩は 猫である "))) # 'გამარჯობა 吾輩は 猫である </s>' print(bad_tokenizer.decode(bad_tokenizer.encode("გამარჯობა 吾輩は 猫である "))) # 'გამარ[UNK]ობა 吾輩は 猫である </s>'
How to cite
@misc{rinna-japanese-gpt-neox-3.6b,
title = {rinna/japanese-gpt-neox-3.6b},
author = {Zhao, Tianyu and Sawada, Kei},
url = {https://huggingface.co/rinna/japanese-gpt-neox-3.6b}
}
@inproceedings{sawada2024release,
title = {Release of Pre-Trained Models for the {J}apanese Language},
author = {Sawada, Kei and Zhao, Tianyu and Shing, Makoto and Mitsui, Kentaro and Kaga, Akio and Hono, Yukiya and Wakatsuki, Toshiaki and Mitsuda, Koh},
booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
month = {5},
year = {2024},
pages = {13898--13905},
url = {https://aclanthology.org/2024.lrec-main.1213},
note = {\url{https://arxiv.org/abs/2404.01657}}
}