Update README.md

README.md

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 ---
 license: mit
+language: ja
+tags:
+    - luke
+    - pytorch
+    - transformers
+    - marcja
+    - marc-ja
+    - sentiment-analysis
+    - SentimentAnalysis
+    
 ---
+
+# このモデルはluke-japanese-baseをファインチューニングして、MARC-ja(positive or negativeの二値分類)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MARC-ja( 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。
+
+positive or negativeの二値分類タスクに用いることができます。
+
+# This model is fine-tuned model for MARC-ja which is based on luke-japanese-base
+
+This model is fine-tuned by using yahoo japan JGLUE MARC-ja dataset.
+
+You could use this model for binary classification (positive or negative) tasks.
+
 # モデルの精度 accuracy of model
 precision : 0.967
 accuracy : 0.967
 recall : 0.967
 f1 : 0.967
+
+# How to use 使い方
+sentencepieceとtransformersをインストールして (pip install sentencepiece , pip install transformers) 
+以下のコードを実行することで、MARC-jaタスクを解かせることができます。
+After install transformers and sentencepiec, please execute this code.
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained('Mizuiro-sakura/luke-japanese-base-marcja')
+model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-base-marcja')
+
+text = 'この商品は素晴らしい！とても匂いが良く、満足でした。'
+
+token = tokenizer.encode_plus(text, truncation=True, max_length=128, padding="max_length", return_tenosr='pt')
+result = model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0))
+
+if torch.argmax(result['logits'])==0:
+    print('positive')
+if torch.argmax(result['logits'])==1:
+    print('negative')
+```
+
+
+# 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} }
+
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