虚假评论数据集-测试
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虚假评论数据集-测试
This dataset currently uses the default introduction template. Please promptly improve the dataset card content in accordance with the [Dataset File Specification](https://www.modelscope.cn/docs/%E6%95%B0%E6%8D%AE%E9%9B%86%E6%96%87%E4%BB%B6%E8%A7%84%E8%8C%83). Thank you for your understanding. ### Clone with HTTP bash git clone https://www.modelscope.cn/datasets/jimmyliu12345/yelpzip.git --- license: Apache License 2.0 #Fake Review Detection tags: - Alibaba - arxiv:1810.99999 - my free-style tag text: #Secondary-level categories can only be assigned to one task_categories fill_mask: #Third-level categories support multiple selections languages: - en --- <!--- 以上YAML section提供属性/tags描述--- > <!--- 以下为markdown格式的dataset描述--- > ## Dataset Description Yelp business review data ### Dataset Overview Provides an introduction to the dataset and its supported usage scenarios (including supported languages, etc.). ### Supported Tasks This dataset supports various training tasks and related benchmark results. ## Dataset Format and Structure ### Data Format text: Review text userid: User ID product_id rating label date ### Dataset Loading Method Provide detailed instructions for loading and using the dataset via the MaaS/Dataset SDK, including code examples and other relevant methods. ### Data Splitting Whether the dataset has been pre-split (e.g., whether there are preset train/test/validation data splits). If yes, how was the data splitting implemented? If no pre-splitting is performed, are there any recommendations for splitting during dataset usage (such as split ratios)? ## Relevant Information for Dataset Generation ### Raw Data Describe the source of the raw data, how the initial data collection was conducted, and whether any processing procedures such as normalization have been applied. ### Dataset Annotation Whether the dataset contains annotations; if so, describe the relevant information. #### Annotation Process How was the annotation implemented, and what is the detailed workflow? #### Annotators Relevant information about the annotators, especially when the annotators differ from the original data providers. ## Dataset Copyright Information Copyright information related to the dataset, authorized usage scenarios and users, whether it is open source, and which open-source license is adopted, etc. ## Citation Method Whether the dataset has associated academic articles, and if there is a recommended citation format for referencing the dataset in research papers, etc. ## Other Relevant Information Personal and sensitive information that may be contained in the dataset, relevant background considerations for using the dataset; potential social significance of the dataset, as well as possible bias information and limitations, etc.
