TheHidden/autotrain-data-facebook2
收藏资源简介：
--- task_categories: - summarization --- # AutoTrain Dataset for project: facebook2 ## Dataset Description This dataset has been automatically processed by AutoTrain for project facebook2. ### Languages The BCP-47 code for the dataset's language is unk. ## Dataset Structure ### Data Instances A sample from this dataset looks as follows: ```json [ { "text": "\u201cWhen the vessel is anchored or docked, the sonar is set up with continuous surveillance 360\u00b0 around the vessel for detection of underwater threats; such as divers, diver delivery vehicles, or submersibles.\u201d", "target": "identify underwater threats" }, { "text": "\u201cLimiter: Useful to reduce external interference, such as acoustic interference from other sonar/sounder equipment on board and electrical interference from power supplies and power cables.\u201d", "target": "identify the limiter settings" } ] ``` ### Dataset Fields The dataset has the following fields (also called "features"): ```json { "text": "Value(dtype='string', id=None)", "target": "Value(dtype='string', id=None)" } ``` ### Dataset Splits This dataset is split into a train and validation split. The split sizes are as follow: | Split name | Num samples | | ------------ | ------------------- | | train | 264 | | valid | 66 |
AutoTrain Dataset for project: facebook2
数据集描述
该数据集由AutoTrain自动处理，用于facebook2项目。
语言
数据集的语言BCP-47代码为unk。
数据集结构
数据实例
数据集的一个样本如下：
json [ { "text": "u201cWhen the vessel is anchored or docked, the sonar is set up with continuous surveillance 360u00b0 around the vessel for detection of underwater threats; such as divers, diver delivery vehicles, or submersibles.u201d", "target": "identify underwater threats" }, { "text": "u201cLimiter: Useful to reduce external interference, such as acoustic interference from other sonar/sounder equipment on board and electrical interference from power supplies and power cables.u201d", "target": "identify the limiter settings" } ]
数据集字段
数据集包含以下字段（也称为“特征”）：
json { "text": "Value(dtype=string, id=None)", "target": "Value(dtype=string, id=None)" }
数据集分割
数据集被分割为训练集和验证集，分割大小如下：
| 分割名称 | 样本数量 |
|---|---|
| train | 264 |
| valid | 66 |
