nagisa_bert

nagisa_bert

A BERT model for nagisa. The model is available in Transformers 🤗.

A tokenizer for nagisa_bert is available here.

Install

To use this model, the following python library must be installed. You can install nagisa_bert by using the pip command.

pip install nagisa_bert

Supported Platforms:

• 🐧 Linux, 🍎 macOS, 🪟 Windows: Python 3.10 - 3.14

Usage

This model is available in Transformer's pipeline method.

from transformers import pipeline
from nagisa_bert import NagisaBertTokenizer

text = "nagisaで[MASK]できるモデルです"
tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
fill_mask = pipeline("fill-mask", model='taishi-i/nagisa_bert', tokenizer=tokenizer)
print(fill_mask(text))

[{'score': 0.1385931372642517,
  'sequence': 'nagisa で 使用 できる モデル です',
  'token': 8092,
  'token_str': '使 用'},
 {'score': 0.11947669088840485,
  'sequence': 'nagisa で 利用 できる モデル です',
  'token': 8252,
  'token_str': '利 用'},
 {'score': 0.04910655692219734,
  'sequence': 'nagisa で 作成 できる モデル です',
  'token': 9559,
  'token_str': '作 成'},
 {'score': 0.03792576864361763,
  'sequence': 'nagisa で 購入 できる モデル です',
  'token': 9430,
  'token_str': '購 入'},
 {'score': 0.026893319562077522,
  'sequence': 'nagisa で 入手 できる モデル です',
  'token': 11273,
  'token_str': '入 手'}]

Tokenization and vectorization.

from transformers import BertModel
from nagisa_bert import NagisaBertTokenizer

text = "nagisaで[MASK]できるモデルです"
tokenizer = NagisaBertTokenizer.from_pretrained("taishi-i/nagisa_bert")
tokens = tokenizer.tokenize(text)
print(tokens)
# ['na', '##g', '##is', '##a', 'で', '[MASK]', 'できる', 'モデル', 'です']

model = BertModel.from_pretrained("taishi-i/nagisa_bert")
h = model(**tokenizer(text, return_tensors="pt")).last_hidden_state
print(h)

tensor([[[-0.2912, -0.6818, -0.4097,  ...,  0.0262, -0.3845,  0.5816],
         [ 0.2504,  0.2143,  0.5809,  ..., -0.5428,  1.1805,  1.8701],
         [ 0.1890, -0.5816, -0.5469,  ..., -1.2081, -0.2341,  1.0215],
         ...,
         [-0.4360, -0.2546, -0.2824,  ...,  0.7420, -0.2904,  0.3070],
         [-0.6598, -0.7607,  0.0034,  ...,  0.2982,  0.5126,  1.1403],
         [-0.2505, -0.6574, -0.0523,  ...,  0.9082,  0.5851,  1.2625]]],
       grad_fn=<NativeLayerNormBackward0>)

Model description

Architecture

The model architecture is the same as the BERT bert-base-uncased architecture (12 layers, 768 dimensions of hidden states, and 12 attention heads).

Training Data

The models is trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 8, 2022 with make_corpus_wiki.py and create_pretraining_data.py.

Training

The model is trained with the default parameters of transformers.BertConfig. Due to GPU memory limitations, the batch size is set to small; 16 instances per batch, and 2M training steps.

Tutorial

You can find here a list of the notebooks on Japanese NLP using pre-trained models and transformers.