Qifan Zhang commited on
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feat: update description

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- TransDis系统,是一个基于Transformer语言模型的语义距离评分系统,用于自动评估中文(或其他语言)的多用途任务(AUT)中的独创性和灵活性。
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- 输入被试(id)+提示词+回答的数据,每行1个用途,用逗号隔开。您可以通过文本框直接输入数据,也可以上传用逗号隔开的CSV格式文件作为输入,CSV输入优先级高于文本框输入。
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- 您可以选择用于评分的模型,请注意sentence-transformers_paraphrase-multilingual-mpnet-base-v2和sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2可用于多语言,cyclone_simcse-chinese-roberta-wwm-ext仅适用于中文,sentence-transformers/all-mpnet-base-v2和sentence-transformers/all-MiniLM-L12-v2仅适用于英文。
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- 如发生错误,请试着简化你的数据——用更少的行试试。如果不行,则可能是输入格式错误,请尝试重新保存为逗号分隔的CSV,然后再上传CSV文件。
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- 如需更多帮助或报告其他bug,请联系ydd409@163.com。
 
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- TranDis, a semantic distance scoring system based on transformer-based language models, can be a useful tool to automatically assess originality and flexibility for AUT in Chinese or other languages.
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- Enter your participant (id) + prompt + response data, one per line, with a COMMA between each variable. You can either input data directly into the text box or upload a comma-separated CSV file as input. Please note that if both methods are used, the CSV input will take precedence over the text box input.
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- You can choose the model to use for scoring. Please note that sentence-transformers_paraphrase-multilingual-mpnet-base-v2 and sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2 are applicable to multiple languages; cyclone_simcse-chinese-roberta-wwm-ext is only applicable to Chinese; sentence-transformers/all-mpnet-base-v2 and sentence-transformers/all-MiniLM-L12-v2 are only applicable to English.
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- If an error occurred, try simplifying your data - does it work with fewer rows? If not, the input format may be wrong.
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- For more help, or to report possible bugs in our system, contact ydd409@163.com
 
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+ TransDis系统,是一个基于Transformer语言模型的语义距离评分系统,用于自动评估中文(或其他语言)的多用途任务(AUT)中的独创性和灵活性(论文预印本见,https://arxiv.org/abs/2306.14790)。
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+ 输入被试(id)+提示词+回答的数据,每行1个用途,用逗号隔开。您可以通过文本框直接输入数据,也可以上传用逗号隔开的CSV格式文件或xlsx文件作为输入,CSV输入优先级高于文本框输入。
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+ 您可以选择用于评分的模型,请注意sentence-transformers_paraphrase-multilingual-mpnet-base-v2和sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2可用于多语言,其他模型仅适用于英文或中文。
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+ 我们提供Pooling方法的选择,对于bert-base-chinese建议使用mean pooling。
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+ 如发生错误,请试着简化你的数据——用更少的行试试。如果不行,则可能是输入格式错误,请尝试重新保存为逗号分隔的CSV,然后再上传CSV文件。
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+ 如需更多帮助或报告其他bug,请联系ydd409@163.com。
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+ TranDis, a semantic distance scoring system based on transformer-based language models, can be a useful tool to automatically assess originality and flexibility for AUT in Chinese or other languages (for a pre-print, see https://arxiv.org/abs/2306.14790).
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+ Enter your participant ID + prompt + response data, one per line, with a COMMA between each variable. You can either input data directly into the text box or upload a comma-separated CSV file or a XLSX file as input. Please note that if both methods are used, the CSV input will take precedence over the text box input.
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+ You can choose the model to use for scoring. Please note that sentence-transformers_paraphrase-multilingual-mpnet-base-v2 and sentence-transformers_paraphrase-multilingual-MiniLM-L12-v2 are applicable to multiple languages; cyclone_simcse-chinese-roberta-wwm-ext is only applicable to Chinese; sentence-transformers/all-mpnet-base-v2 and sentence-transformers/all-MiniLM-L12-v2 are only applicable to English.
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+ If an error occurs, try simplifying your data - does it work with fewer rows? If not, the input format may be incorrect.
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+ For more assistance or to report potential issues with our system, please contact ydd409@163.com.