Bert Base Japanese Upos

🚀 bert-base-japanese-upos

A BERT model pre - trained on Japanese Wikipedia texts for POS - tagging and dependency - parsing, offering accurate short - unit - word tagging.

🚀 Quick Start

This BERT model is pre - trained on Japanese Wikipedia texts for POS - tagging and dependency - parsing. It is derived from [bert - base - japanese - char - extended](https://huggingface.co/KoichiYasuoka/bert - base - japanese - char - extended), and tags every short - unit - word by UPOS (Universal Part - Of - Speech).

✨ Features

• Pre - trained on Japanese Wikipedia texts.
• Suitable for POS - tagging and dependency - parsing.
• Tags short - unit - words with UPOS.

📦 Installation

No specific installation steps are provided in the original README. If you want to use this model, you need to install relevant Python libraries such as torch and transformers or esupar. For example, you can use the following commands:

pip install torch transformers
pip install esupar

💻 Usage Examples

Basic Usage

import torch
from transformers import AutoTokenizer,AutoModelForTokenClassification
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/bert-base-japanese-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/bert-base-japanese-upos")
s="国境の長いトンネルを抜けると雪国であった。"
p=[model.config.id2label[q] for q in torch.argmax(model(tokenizer.encode(s,return_tensors="pt"))["logits"],dim=2)[0].tolist()[1:-1]]
print(list(zip(s,p)))

Advanced Usage

import esupar
nlp=esupar.load("KoichiYasuoka/bert-base-japanese-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))

📚 Documentation

esupar is a Tokenizer, POS - tagger, and Dependency - parser with BERT/RoBERTa/DeBERTa models. You can refer to it for more information.

📄 License

This model is released under the "cc - by - sa - 4.0" license.