forked from huawei/mindspore2022
217 lines
17 KiB
ReStructuredText
217 lines
17 KiB
ReStructuredText
mindspore.dataset.CLUEDataset
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=============================
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.. py:class:: mindspore.dataset.CLUEDataset(dataset_files, task='AFQMC', usage='train', num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)
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读取和解析CLUE数据集的源文件构建数据集。
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目前支持的CLUE分类任务包括：'AFQMC'、'TNEWS 、'IFLYTEK'、'CMNLI'、'WSC'和'CSL'。更多CLUE数据集的说明详见 `CLUE GitHub <https://github.com/CLUEbenchmark/CLUE>`_ 。
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**参数：**
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- **dataset_files** (Union[str, list[str]]) - 数据集文件路径，支持单文件路径字符串、多文件路径字符串列表或可被glob库模式匹配的字符串，文件列表将在内部进行字典排序。
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- **task** (str, 可选) - 任务类型，可取值为 'AFQMC' 、'TNEWS'、'IFLYTEK'、'CMNLI'、'WSC' 或 'CSL'。默认值：'AFQMC'。
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- **usage** (str, 可选) - 指定数据集的子集，可取值为'train'，'test'或'eval'，默认值：'train'。
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- **num_samples** (int, 可选) - 指定从数据集中读取的样本数。默认值：None，读取所有样本。
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- **num_parallel_workers** (int, 可选) - 指定读取数据的工作线程数。默认值：None，使用mindspore.dataset.config中配置的线程数。
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- **shuffle** (Union[bool, 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：mindspore.dataset.Shuffle.GLOBAL。
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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。
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通过传入枚举变量设置数据混洗的模式：
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- **Shuffle.GLOBAL**：混洗文件和样本。
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- **Shuffle.FILES**：仅混洗文件。
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- **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。
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- **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。
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- **cache** (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/programming_guide/zh-CN/master/cache.html>`_ 。默认值：None，不使用缓存。
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根据给定的 `task` 参数 和 `usage` 配置，数据集会生成不同的输出列：
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+-------------------------+------------------------------+-----------------------------+
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| `task` | `usage` | 输出列 |
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+=========================+==============================+=============================+
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| AFQMC | train | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| | | [label, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [id, dtype=uint32] |
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| | | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| | | [label, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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| TNEWS | train | [label, dtype=string] |
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| | | [label_des, dtype=string] |
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| | | [sentence, dtype=string] |
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| | | [keywords, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [label, dtype=uint32] |
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| | | [keywords, dtype=string] |
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| | | [sentence, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [label, dtype=string] |
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| | | [label_des, dtype=string] |
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| | | [sentence, dtype=string] |
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| | | [keywords, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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| IFLYTEK | train | [label, dtype=string] |
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| | | [label_des, dtype=string] |
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| | | [sentence, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [id, dtype=uint32] |
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| | | [sentence, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [label, dtype=string] |
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| | | [label_des, dtype=string] |
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| | | [sentence, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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| CMNLI | train | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| | | [label, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [id, dtype=uint32] |
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| | | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [sentence1, dtype=string] |
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| | | [sentence2, dtype=string] |
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| | | [label, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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| WSC | train | [span1_index, dtype=uint32]|
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| | | [span2_index, dtype=uint32]|
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| | | [span1_text, dtype=string] |
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| | | [span2_text, dtype=string] |
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| | | [idx, dtype=uint32] |
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| | | [text, dtype=string] |
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| | | [label, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [span1_index, dtype=uint32]|
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| | | [span2_index, dtype=uint32]|
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| | | [span1_text, dtype=string] |
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| | | [span2_text, dtype=string] |
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| | | [idx, dtype=uint32] |
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| | | [text, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [span1_index, dtype=uint32]|
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| | | [span2_index, dtype=uint32]|
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| | | [span1_text, dtype=string] |
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| | | [span2_text, dtype=string] |
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| | | [idx, dtype=uint32] |
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| | | [text, dtype=string] |
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| | | [label, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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| CSL | train | [id, dtype=uint32] |
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| | | [abst, dtype=string] |
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| | | [keyword, dtype=string] |
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| | | [label, dtype=string] |
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| +------------------------------+-----------------------------+
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| | test | [id, dtype=uint32] |
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| | | [abst, dtype=string] |
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| | | [keyword, dtype=string] |
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| +------------------------------+-----------------------------+
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| | eval | [id, dtype=uint32] |
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| | | [abst, dtype=string] |
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| | | [keyword, dtype=string] |
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| | | [label, dtype=string] |
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+-------------------------+------------------------------+-----------------------------+
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**异常：**
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- **ValueError** - `dataset_files` 参数所指向的文件无效或不存在。
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- **ValueError** - `task` 参数不为 'AFQMC'、'TNEWS'、'IFLYTEK'、'CMNLI'、'WSC' 或 'CSL'。
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- **ValueError** - `usage` 参数不为 'train'、'test' 或 'eval'。
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- **ValueError** - `num_parallel_workers` 参数超过系统最大线程数。
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- **RuntimeError** - 指定了 `num_shards` 参数，但是未指定 `shard_id` 参数。
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- **RuntimeError** - 指定了 `shard_id` 参数，但是未指定 `num_shards` 参数。
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- **ValueError** - `shard_id` 参数错误（小于0或者大于等于 `num_shards` ）。
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**关于CLUE数据集：**
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CLUE，又名中文语言理解测评基准，包含许多有代表性的数据集，涵盖单句分类、句对分类和机器阅读理解等任务。
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您可以将数据集解压成如下的文件结构，并通过MindSpore的API进行读取，以 'afqmc' 数据集为例：
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.. code-block::
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.
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└── afqmc_public
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├── train.json
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├── test.json
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└── dev.json
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**引用：**
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.. code-block::
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@article{CLUEbenchmark,
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title = {CLUE: A Chinese Language Understanding Evaluation Benchmark},
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author = {Liang Xu, Xuanwei Zhang, Lu Li, Hai Hu, Chenjie Cao, Weitang Liu, Junyi Li, Yudong Li,
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Kai Sun, Yechen Xu, Yiming Cui, Cong Yu, Qianqian Dong, Yin Tian, Dian Yu, Bo Shi, Jun Zeng,
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Rongzhao Wang, Weijian Xie, Yanting Li, Yina Patterson, Zuoyu Tian, Yiwen Zhang, He Zhou,
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Shaoweihua Liu, Qipeng Zhao, Cong Yue, Xinrui Zhang, Zhengliang Yang, Zhenzhong Lan},
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journal = {arXiv preprint arXiv:2004.05986},
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year = {2020},
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howpublished = {https://github.com/CLUEbenchmark/CLUE}
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}
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.. include:: mindspore.dataset.Dataset.rst
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.. include:: mindspore.dataset.Dataset.b.rst
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.. include:: mindspore.dataset.Dataset.c.rst
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.. include:: mindspore.dataset.Dataset.d.rst
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.. include:: mindspore.dataset.Dataset.zip.rst |