mindspore.dataset.YelpReviewDataset
===================================

.. py:class:: mindspore.dataset.YelpReviewDataset(dataset_dir, usage=None, num_samples=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, num_parallel_workers=None, cache=None)

    Yelp Review Full和Yelp Review Polarity数据集。

    生成的数据集有两列 `[label, text]`,两列的数据类型均为string。

    参数:
        - **dataset_dir** (str) - 包含数据集文件的根目录路径。
        - **usage** (str, 可选) - 指定数据集的子集,可取值为 ``'train'`` 、 ``'test'`` 或 ``'all'`` 。默认值: ``None`` ,读取全部样本。
          对于Polarity数据集, ``'train'`` 将读取560,000个训练样本, ``'test'`` 将读取38,000个测试样本, ``'all'`` 将读取所有598,000个样本。
          对于Full数据集, ``'train'`` 将读取650,000个训练样本, ``'test'`` 将读取50,000个测试样本, ``'all'`` 将读取所有700,000个样本。默认值: ``None`` ,读取所有样本。
        - **num_samples** (int, 可选) - 指定从数据集中读取的样本数。默认值: ``None`` ,读取全部样本。
        - **shuffle** (Union[bool, :class:`~.dataset.Shuffle`], 可选) - 每个epoch中数据混洗的模式,支持传入bool类型与枚举类型进行指定。默认值: ``Shuffle.GLOBAL`` 。
          如果 `shuffle` 为 ``False`` ,则不混洗,如果 `shuffle` 为 ``True`` ,等同于将 `shuffle` 设置为 ``mindspore.dataset.Shuffle.GLOBAL`` 。
          通过传入枚举变量设置数据混洗的模式:

          - ``Shuffle.GLOBAL`` :混洗文件和样本。
          - ``Shuffle.FILES`` :仅混洗文件。

        - **num_shards** (int, 可选) - 指定分布式训练时将数据集进行划分的分片数。默认值: ``None`` 。指定此参数后, `num_samples` 表示每个分片的最大样本数。一般在 `数据并行模式训练 <https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/parallel/data_parallel.html#数据并行模式加载数据集>`_ 的时候使用。
        - **shard_id** (int, 可选) - 指定分布式训练时使用的分片ID号。默认值: ``None`` 。只有当指定了 `num_shards` 时才能指定此参数。
        - **num_parallel_workers** (int, 可选) - 指定读取数据的工作线程数。默认值: ``None`` ,使用全局默认线程数(8),也可以通过 :func:`mindspore.dataset.config.set_num_parallel_workers` 配置全局线程数。
        - **cache** (:class:`~.dataset.DatasetCache`, 可选) - 单节点数据缓存服务,用于加快数据集处理,详情请阅读 `单节点数据缓存 <https://www.mindspore.cn/docs/zh-CN/r2.5.0/model_train/dataset/cache.html>`_ 。默认值: ``None`` ,不使用缓存。

    异常:
        - **RuntimeError** - `dataset_dir` 参数所指向的文件目录不存在或缺少数据集文件。
        - **RuntimeError** - 指定了 `num_shards` 参数,但是未指定 `shard_id` 参数。
        - **RuntimeError** - 指定了 `shard_id` 参数,但是未指定 `num_shards` 参数。
        - **ValueError** - `num_parallel_workers` 参数超过系统最大线程数。

    教程样例:
        - `使用数据Pipeline加载 & 处理数据集
          <https://www.mindspore.cn/docs/zh-CN/r2.5.0/api_python/samples/dataset/dataset_gallery.html>`_

    **关于YelpReview数据集:**

    Yelp Review Full数据集包括来自Yelp的评论数据。这些数据时从2015年的Yelp数据集挑战赛数据中提取的,主要用于文本分类。

    Yelp Review Polarity数据集在Full数据集的基础上,对产品评分进行了分级,评论分数1和2视为负面评论,4和5视为正面评论。

    Yelp Reviews Polarity和Yelp Reviews Full datasets具有相同的目录结构。
    可以将数据集文件解压缩到以下结构,并通过MindSpore的API读取:

    .. code-block::

        .
        └── yelp_review_dir
             ├── train.csv
             ├── test.csv
             └── readme.txt

    **引用:**

    .. code-block::

        @article{zhangCharacterlevelConvolutionalNetworks2015,
          archivePrefix = {arXiv},
          eprinttype = {arxiv},
          eprint = {1509.01626},
          primaryClass = {cs},
          title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
          abstract = {This article offers an empirical exploration on the use of character-level convolutional networks
                      (ConvNets) for text classification. We constructed several large-scale datasets to show that
                      character-level convolutional networks could achieve state-of-the-art or competitive results.
                      Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF
                      variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
          journal = {arXiv:1509.01626 [cs]},
          author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
          month = sep,
          year = {2015},
        }
    
    .. code-block::

        @article{zhangCharacterlevelConvolutionalNetworks2015,
          archivePrefix = {arXiv},
          eprinttype = {arxiv},
          eprint = {1509.01626},
          primaryClass = {cs},
          title = {Character-Level {{Convolutional Networks}} for {{Text Classification}}},
          abstract = {This article offers an empirical exploration on the use of character-level convolutional networks
                      (ConvNets) for text classification. We constructed several large-scale datasets to show that
                      character-level convolutional networks could achieve state-of-the-art or competitive results.
                      Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF
                      variants, and deep learning models such as word-based ConvNets and recurrent neural networks.},
          journal = {arXiv:1509.01626 [cs]},
          author = {Zhang, Xiang and Zhao, Junbo and LeCun, Yann},
          month = sep,
          year = {2015},
        }


.. include:: mindspore.dataset.api_list_nlp.txt