README.md

---
license: mit
language: ja
tags:
    - luke
    - pytorch
    - transformers
    - jsts
    - stsb
    - sentence-similarity
    - SentenceSimilarity
    
---

# このモデルはluke-japanese-baseをファインチューニングして、JSTS(文章の類似度計算)に用いれるようにしたものです。
このモデルはluke-japanese-baseを
yahoo japan/JGLUEのJSTS( https://github.com/yahoojapan/JGLUE )
を用いてファインチューニングしたものです。

文章の類似度(5が最高値)を計算するタスクに用いることができます。

# This model is fine-tuned model for JSTS which is based on luke-japanese-base

This model is fine-tuned by using yahoo japan JGLUE JSTS dataset.

You could use this model for calculating sentence-similarity.

# モデルの精度 accuracy of model
モデルの精度は

Pearson  (ピアソンの積率相関係数) : 0.8971

# How to use 使い方
transformers, sentencepieceをinstallして、以下のコードを実行することで、jsts(文章の類似度計算)タスクを解かせることができます。
please execute this code.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import numpy as np

tokenizer=AutoTokenizer.from_pretrained('Mizuiro-sakura/luke-japanese-base-finetuned-jsts')
model=AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-base-finetuned-jsts')
sentence1='今日は銀座に買い物に出かけた'
sentence2='今日は銀座に服を買いに出かけた'

token=tokenizer(sentence1,sentence2)

import torch
tensor_input_ids = torch.tensor(token["input_ids"])
tensor_attention_masks = torch.tensor(token["attention_mask"])

outputs=model(tensor_input_ids.unsqueeze(0), tensor_attention_masks.unsqueeze(0))

print(outputs.logits[0][1]*5)
```


# what is Luke?　Lukeとは？[1]
LUKE (Language Understanding with Knowledge-based Embeddings) is a new pre-trained contextualized representation of words and entities based on transformer. LUKE treats words and entities in a given text as independent tokens, and outputs contextualized representations of them. LUKE adopts an entity-aware self-attention mechanism that is an extension of the self-attention mechanism of the transformer, and considers the types of tokens (words or entities) when computing attention scores.

LUKE achieves state-of-the-art results on five popular NLP benchmarks including SQuAD v1.1 (extractive question answering), CoNLL-2003 (named entity recognition), ReCoRD (cloze-style question answering), TACRED (relation classification), and Open Entity (entity typing). luke-japaneseは、単語とエンティティの知識拡張型訓練済み Transformer モデルLUKEの日本語版です。LUKE は単語とエンティティを独立したトークンとして扱い、これらの文脈を考慮した表現を出力します。

# Acknowledgments　謝辞
Lukeの開発者である山田先生とStudio ousiaさんには感謝いたします。 I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia.

# Citation
[1]@inproceedings{yamada2020luke, title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, booktitle={EMNLP}, year={2020} }