|
--- |
|
language: ja |
|
thumbnail: https://github.com/rinnakk/japanese-pretrained-models/blob/master/rinna.png |
|
tags: |
|
- ja |
|
- 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](https://github.com/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](http://data.statmt.org/cc-100/ja.txt.xz), [Japanese C4](https://huggingface.co/datasets/mc4), and [Japanese Wikipedia](https://dumps.wikimedia.org/other/cirrussearch) to optimize a traditional language modelling objective. |
|
|
|
A final validation perplexity of **8.68** has been reached. |
|
|
|
* **Authors** |
|
|
|
[Tianyu Zhao](https://huggingface.co/tianyuz) and [Kei Sawada](https://huggingface.co/keisawada) |
|
|
|
# How to use the model |
|
|
|
~~~~python |
|
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](https://github.com/google/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_prefix` option 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_whitespaces` option 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=False` to 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>' |
|
~~~ |
|
|
|
# Licenese |
|
[The MIT license](https://opensource.org/licenses/MIT) |
|
|
