Update README.md

README.md

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 ---
 license: mit
+language: ja
+tags:
+  - luke
+  - sentiment-analysis
+  - wrime
+  - SentimentAnalysis
+  - pytorch
 ---
+
+# このモデルはLuke-japanese-large-liteをファインチューニングしたものです。
+このモデルは８つの感情（喜び、悲しみ、期待、驚き、怒り、恐れ、嫌悪、信頼）の内、どの感情が文章に含まれているのか分析することができます。
+このモデルはwrimeデータセット（
+https://huggingface.co/datasets/shunk031/wrime
+）を用いて学習を行いました。
+
+# This model is based on Luke-japanese-large-lite
+This model is fine-tuned model which besed on studio-ousia/Luke-japanese-large-lite.
+This could be able to analyze which emotions (joy or sadness or anticipation or surprise or anger or fear or disdust or trust ) are included.
+This model was fine-tuned by using wrime dataset.
+
+# 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。
+
+# how to use 使い方
+ステップ1：pythonとpytorch, sentencepieceのインストールとtransformersのアップデート（バージョンが古すぎるとLukeTokenizerが入っていないため）
+update transformers and install sentencepiece, python and pytorch
+
+ステップ2：下記のコードを実行する
+Please execute this code
+
+
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig
+import torch
+tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime")
+config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True)    
+model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', config=config)
+
+text='すごく楽しかった。また行きたい。'
+
+max_seq_length=512
+token=tokenizer(text,
+        truncation=True,
+        max_length=max_seq_length,
+        padding="max_length")
+output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0))
+max_index=torch.argmax(torch.tensor(output.logits))
+
+if max_index==0:
+    print('joy、うれしい')
+elif max_index==1:
+    print('sadness、悲しい')
+elif max_index==2:
+    print('anticipation、期待')
+elif max_index==3:
+    print('surprise、驚き')
+elif max_index==4:
+    print('anger、怒り')
+elif max_index==5:
+    print('fear、恐れ')
+elif max_index==6:
+    print('disgust、嫌悪')
+elif max_index==7:
+    print('trust、信頼')
+```
+
+# 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}
+}