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--- |
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license: mit |
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language: ja |
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tags: |
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- luke |
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- sentiment-analysis |
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- wrime |
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- SentimentAnalysis |
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- pytorch |
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--- |
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# 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。 |
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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。 |
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このモデルはwrimeデータセット（ |
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https://huggingface.co/datasets/shunk031/wrime |
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）を用いて学習を行いました。 |
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# This model is based on Luke-japanese-large-lite |
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This model is fine-tuned model which besed on studio-ousia/Luke-japanese-large-lite. |
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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. |
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This model was fine-tuned by using wrime dataset. |
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# what is Luke? Lukeとは？[1] |
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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. |
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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). |
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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。 |
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# how to use 使い方 |
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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） |
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update transformers and install sentencepiece, python and pytorch |
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ステップ2：下記のコードを実行する |
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Please execute this code |
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```python |
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, LukeConfig |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime") |
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config = LukeConfig.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', output_hidden_states=True) |
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model = AutoModelForSequenceClassification.from_pretrained('Mizuiro-sakura/luke-japanese-large-sentiment-analysis-wrime', config=config) |
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text='すごく楽しかった。また行きたい。' |
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max_seq_length=512 |
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token=tokenizer(text, |
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truncation=True, |
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max_length=max_seq_length, |
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padding="max_length") |
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output=model(torch.tensor(token['input_ids']).unsqueeze(0), torch.tensor(token['attention_mask']).unsqueeze(0)) |
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max_index=torch.argmax(torch.tensor(output.logits)) |
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if max_index==0: |
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print('joy、うれしい') |
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elif max_index==1: |
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print('sadness、悲しい') |
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elif max_index==2: |
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print('anticipation、期待') |
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elif max_index==3: |
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print('surprise、驚き') |
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elif max_index==4: |
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print('anger、怒り') |
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elif max_index==5: |
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print('fear、恐れ') |
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elif max_index==6: |
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print('disgust、嫌悪') |
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elif max_index==7: |
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print('trust、信頼') |
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``` |
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# Acknowledgments 謝辞 |
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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。 |
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I would like to thank Mr.Yamada @ikuyamada and Studio ousia @StudioOusia. |
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# Citation |
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[1]@inproceedings{yamada2020luke, |
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title={LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention}, |
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author={Ikuya Yamada and Akari Asai and Hiroyuki Shindo and Hideaki Takeda and Yuji Matsumoto}, |
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booktitle={EMNLP}, |
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year={2020} |
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} |
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