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+ > 上述数据集为ABSA(Aspect-Based Sentiment Analysis)领域数据集,基本形式为从句子中抽取:方面术语、方面类别(术语类别)、术语在上下文中情感极性以及针对该术语的观点词,不同数据集抽取不同的信息,这点在jsonl文件的“instruction”键中有分别提到,在此我将其改造为了生成任务,需要模型按照一定格式生成抽取结果。
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+ #### 以acos数据集中抽取的jsonl文件一条数据举例:
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+
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+ ```
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+ {
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+ "task_type": "generation",
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+ "dataset": "acos",
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+ "input": ["the computer has difficulty switching between tablet and computer ."],
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+ "output": "[['computer', 'laptop usability', 'negative', 'difficulty']]",
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+ "situation": "none",
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+ "label": "",
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+ "extra": "",
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+ "instruction": "
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+ Task: Extracting aspect terms and their corresponding aspect categories, sentiment polarities, and opinion words.
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+ Input: A sentence
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+ Output: A list of 4-tuples, where each tuple contains the extracted aspect term, its aspect category, sentiment polarity, and opinion words (if any). Supplement: \"Null\" means that there is no occurrence in the sentence.
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+ Example:
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+ Sentence: \"Also it's not a true SSD drive in there but eMMC, which makes a difference.\"
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+ Output: [['SSD drive', 'hard_disc operation_performance', 'negative', 'NULL']]'
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+ "
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+ }
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+ ```
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+ > 此处未设置label和extra,在instruction中以如上所示的字符串模板,并给出一个例子进行one-shot,ABSA领域数据集(absa-quad,acos,arts,aste-data-v2,mams,semeval-2014,semeval-2015,semeval-2016,towe)每个数据集对应instruction模板相同,内容有细微不同,且部分数据集存在同一数据集不同数据instruction内容不同的情况。