hummingbird
/
fastNLP
Not watched
2
0
Fork 0
Code
Releases 13 Wiki Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain
Browse Source

添加了 tutorial_4 的 ipynb

tags/v0.5.5
ChenXin 4 years ago
parent
8971611efc
commit
f4057d20c9
2 changed files with 309 additions and 2 deletions
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +0
    -2
      docs/source/tutorials/文本分类.rst
  2. +309
    -0
      tutorials/tutorial_4_load_dataset.ipynb

+ 0
- 2
docs/source/tutorials/文本分类.rst View File

@@ -19,8 +19,6 @@
.. figure:: ./cn_cls_example.png
:alt: jupyter

jupyter

步骤
----



+ 309
- 0
tutorials/tutorial_4_load_dataset.ipynb View File

@@ -0,0 +1,309 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用Loader和Pipe加载并处理数据集\n",
"\n",
"这一部分是关于如何加载数据集的教程\n",
"\n",
"## Part I: 数据集容器DataBundle\n",
"\n",
"而由于对于同一个任务，训练集，验证集和测试集会共用同一个词表以及具有相同的目标值，所以在fastNLP中我们使用了 DataBundle 来承载同一个任务的多个数据集 DataSet 以及它们的词表 Vocabulary 。下面会有例子介绍 DataBundle 的相关使用。\n",
"\n",
"DataBundle 在fastNLP中主要在各个 Loader 和 Pipe 中被使用。 下面我们先介绍一下 Loader 和 Pipe 。\n",
"\n",
"## Part II: 加载的各种数据集的Loader\n",
"\n",
"在fastNLP中，所有的 Loader 都可以通过其文档判断其支持读取的数据格式，以及读取之后返回的 DataSet 的格式, 例如 ChnSentiCorpLoader \n",
"\n",
"- download() 函数：自动将该数据集下载到缓存地址，默认缓存地址为~/.fastNLP/datasets/。由于版权等原因，不是所有的Loader都实现了该方法。该方法会返回下载后文件所处的缓存地址。\n",
"\n",
"- _load() 函数：从一个数据文件中读取数据，返回一个 DataSet 。返回的DataSet的格式可从Loader文档判断。\n",
"\n",
"- load() 函数：从文件或者文件夹中读取数据为 DataSet 并将它们组装成 DataBundle。支持接受的参数类型有以下的几种\n",
"\n",
" - None, 将尝试读取自动缓存的数据，仅支持提供了自动下载数据的Loader\n",
" - 文件夹路径, 默认将尝试在该文件夹下匹配文件名中含有 train , test , dev 的文件，如果有多个文件含有相同的关键字，将无法通过该方式读取\n",
" - dict, 例如{'train':\"/path/to/tr.conll\", 'dev':\"/to/validate.conll\", \"test\":\"/to/te.conll\"}。"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In total 3 datasets:\n",
"\ttest has 1944 instances.\n",
"\ttrain has 17196 instances.\n",
"\tdev has 1858 instances.\n",
"\n"
]
}
],
"source": [
"from fastNLP.io import CWSLoader\n",
"\n",
"loader = CWSLoader(dataset_name='pku')\n",
"data_bundle = loader.load()\n",
"print(data_bundle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"这里表示一共有3个数据集。其中：\n",
"\n",
" 3个数据集的名称分别为train、dev、test，分别有17223、1831、1944个instance\n",
"\n",
"也可以取出DataSet，并打印DataSet中的具体内容"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+----------------------------------------------------------------+\n",
"| raw_words |\n",
"+----------------------------------------------------------------+\n",
"| 迈向 充满 希望 的 新 世纪 —— 一九九八年 新年 讲话 ... |\n",
"| 中共中央 总书记 、 国家 主席 江 泽民 |\n",
"+----------------------------------------------------------------+\n"
]
}
],
"source": [
"tr_data = data_bundle.get_dataset('train')\n",
"print(tr_data[:2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part III: 使用Pipe对数据集进行预处理\n",
"\n",
"通过 Loader 可以将文本数据读入，但并不能直接被神经网络使用，还需要进行一定的预处理。\n",
"\n",
"在fastNLP中，我们使用 Pipe 的子类作为数据预处理的类， Loader 和 Pipe 一般具备一一对应的关系，该关系可以从其名称判断， 例如 CWSLoader 与 CWSPipe 是一一对应的。一般情况下Pipe处理包含以下的几个过程，\n",
"1. 将raw_words或 raw_chars进行tokenize以切分成不同的词或字; \n",
"2. 再建立词或字的 Vocabulary , 并将词或字转换为index; \n",
"3. 将target 列建立词表并将target列转为index;\n",
"\n",
"所有的Pipe都可通过其文档查看该Pipe支持处理的 DataSet 以及返回的 DataBundle 中的Vocabulary的情况; 如 OntoNotesNERPipe\n",
"\n",
"各种数据集的Pipe当中，都包含了以下的两个函数:\n",
"\n",
"- process() 函数：对输入的 DataBundle 进行处理, 然后返回处理之后的 DataBundle 。process函数的文档中包含了该Pipe支持处理的DataSet的格式。\n",
"- process_from_file() 函数：输入数据集所在文件夹，使用对应的Loader读取数据(所以该函数支持的参数类型是由于其对应的Loader的load函数决定的)，然后调用相对应的process函数对数据进行预处理。相当于是把Load和process放在一个函数中执行。\n",
"\n",
"接着上面 CWSLoader 的例子，我们展示一下 CWSPipe 的功能："
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"In total 3 datasets:\n",
"\ttest has 1944 instances.\n",
"\ttrain has 17196 instances.\n",
"\tdev has 1858 instances.\n",
"In total 2 vocabs:\n",
"\tchars has 4777 entries.\n",
"\ttarget has 4 entries.\n",
"\n"
]
}
],
"source": [
"from fastNLP.io import CWSPipe\n",
"\n",
"data_bundle = CWSPipe().process(data_bundle)\n",
"print(data_bundle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"表示一共有3个数据集和2个词表。其中：\n",
"\n",
"- 3个数据集的名称分别为train、dev、test，分别有17223、1831、1944个instance\n",
"- 2个词表分别为chars词表与target词表。其中chars词表为句子文本所构建的词表，一共有4777个不同的字；target词表为目标标签所构建的词表，一共有4种标签。\n",
"\n",
"相较于之前CWSLoader读取的DataBundle，新增了两个Vocabulary。 我们可以打印一下处理之后的DataSet"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+---------------------+---------------------+---------------------+---------+\n",
"| raw_words | chars | target | seq_len |\n",
"+---------------------+---------------------+---------------------+---------+\n",
"| 迈向 充满 希望... | [1224, 178, 674,... | [0, 1, 0, 1, 0, ... | 29 |\n",
"| 中共中央 总书记... | [11, 212, 11, 33... | [0, 3, 3, 1, 0, ... | 15 |\n",
"+---------------------+---------------------+---------------------+---------+\n"
]
}
],
"source": [
"tr_data = data_bundle.get_dataset('train')\n",
"print(tr_data[:2])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"可以看到有两列为int的field: chars和target。这两列的名称同时也是DataBundle中的Vocabulary的名称。可以通过下列的代码获取并查看Vocabulary的 信息"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Vocabulary(['B', 'E', 'S', 'M']...)\n"
]
}
],
"source": [
"vocab = data_bundle.get_vocab('target')\n",
"print(vocab)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Part IV: fastNLP封装好的Loader和Pipe\n",
"\n",
"fastNLP封装了多种任务/数据集的 Loader 和 Pipe 并提供自动下载功能，具体参见文档 [数据集](https://docs.qq.com/sheet/DVnpkTnF6VW9UeXdh?c=A1A0A0)\n",
"\n",
"## Part V: 不同格式类型的基础Loader\n",
"\n",
"除了上面提到的针对具体任务的Loader，我们还提供了CSV格式和JSON格式的Loader\n",
"\n",
"**CSVLoader** 读取CSV类型的数据集文件。例子如下：\n",
"\n",
"```python\n",
"from fastNLP.io.loader import CSVLoader\n",
"data_set_loader = CSVLoader(\n",
" headers=('raw_words', 'target'), sep='\\t'\n",
")\n",
"```\n",
"\n",
"表示将CSV文件中每一行的第一项将填入'raw_words' field，第二项填入'target' field。其中项之间由'\\t'分割开来\n",
"\n",
"```python\n",
"data_set = data_set_loader._load('path/to/your/file')\n",
"```\n",
"\n",
"文件内容样例如下\n",
"\n",
"```csv\n",
"But it does not leave you with much . 1\n",
"You could hate it for the same reason . 1\n",
"The performances are an absolute joy . 4\n",
"```\n",
"\n",
"读取之后的DataSet具有以下的field\n",
"\n",
"| raw_words | target |\n",
"| --------------------------------------- | ------ |\n",
"| But it does not leave you with much . | 1 |\n",
"| You could hate it for the same reason . | 1 |\n",
"| The performances are an absolute joy . | 4 |\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**JsonLoader** 读取Json类型的数据集文件，数据必须按行存储，每行是一个包含各类属性的Json对象。例子如下\n",
"\n",
"```python\n",
"from fastNLP.io.loader import JsonLoader\n",
"loader = JsonLoader(\n",
" fields={'sentence1': 'raw_words1', 'sentence2': 'raw_words2', 'gold_label': 'target'}\n",
")\n",
"```\n",
"\n",
"表示将Json对象中'sentence1'、'sentence2'和'gold_label'对应的值赋给'raw_words1'、'raw_words2'、'target'这三个fields\n",
"\n",
"```python\n",
"data_set = loader._load('path/to/your/file')\n",
"```\n",
"\n",
"数据集内容样例如下\n",
"```\n",
"{\"annotator_labels\": [\"neutral\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"neutral\", ... }\n",
"{\"annotator_labels\": [\"contradiction\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"contradiction\", ... }\n",
"{\"annotator_labels\": [\"entailment\"], \"captionID\": \"3416050480.jpg#4\", \"gold_label\": \"entailment\", ... }\n",
"```\n",
"\n",
"读取之后的DataSet具有以下的field\n",
"\n",
"| raw_words0 | raw_words1 | target |\n",
"| ------------------------------------------------------ | ------------------------------------------------- | ------------- |\n",
"| A person on a horse jumps over a broken down airplane. | A person is training his horse for a competition. | neutral |\n",
"| A person on a horse jumps over a broken down airplane. | A person is at a diner, ordering an omelette. | contradiction |\n",
"| A person on a horse jumps over a broken down airplane. | A person is outdoors, on a horse. | entailment |"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python Now",
"language": "python",
"name": "now"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

Write Preview
Loading…
Cancel
Save