From e3b4cee023008a343f1f32e2c05a4a387eeb5e9b Mon Sep 17 00:00:00 2001 From: lxr-tech <1838593642@qq.com> Date: Tue, 3 May 2022 22:24:27 +0800 Subject: [PATCH] add fastnlp_tutorial_1 lxr 220503 --- tutorials/fastnlp_tutorial_0.ipynb | 16 +- tutorials/fastnlp_tutorial_1.ipynb | 1156 ++++++++++++++++++++ tutorials/figures/T0-fig-trainer-and-evaluator.png | Bin 104863 -> 100764 bytes 3 files changed, 1163 insertions(+), 9 deletions(-) create mode 100644 tutorials/fastnlp_tutorial_1.ipynb diff --git a/tutorials/fastnlp_tutorial_0.ipynb b/tutorials/fastnlp_tutorial_0.ipynb index 28fcfddf..26675ecf 100644 --- a/tutorials/fastnlp_tutorial_0.ipynb +++ b/tutorials/fastnlp_tutorial_0.ipynb @@ -136,7 +136,7 @@ "在`fastNLP 0.8`中，使用`pytorch.nn.Module`搭建需要训练的模型，在搭建模型过程中，除了\n", "\n", "  添加`pytorch`要求的`forward`方法外，还需要添加 **`train_step`** 和 **`evaluate_step`** 这两个方法\n", - "***\n", + "\n", "```python\n", "class Model(torch.nn.Module):\n", " def __init__(self):\n", @@ -177,9 +177,7 @@ "\n", "  从模块角度，该字典的键值和`metric`中的`update`函数的签名一致，这样的机制在传参时被称为“**参数匹配**”\n", "\n", - "***\n", - "\n", - "![fastNLP 0.8 中，Trainer 和 Evaluator 的关系图](./figures/T0-fig-trainer-and-evaluator.png)" + "" ] }, { @@ -206,7 +204,7 @@ "  而在`Trainer`和`Evaluator`中的参数`model_wo_auto_param_call`被设置为`True`时\n", "\n", "    `fastNLP 0.8`会将`batch`直接传给模型的`train_step`、`evaluate_step`或`forward`函数\n", - "***\n", + "\n", "```python\n", "class Dataset(torch.utils.data.Dataset):\n", " def __init__(self, x, y):\n", @@ -253,7 +251,7 @@ "id": "5314482b", "metadata": { "pycharm": { - "is_executing": false + "is_executing": true } }, "outputs": [], @@ -641,11 +639,11 @@ { "data": { "text/html": [ - "
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+       "
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+       "\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.29\u001b[0m\u001b[1m}\u001b[0m\n"
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diff --git a/tutorials/fastnlp_tutorial_1.ipynb b/tutorials/fastnlp_tutorial_1.ipynb
new file mode 100644
index 00000000..11bd2219
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+++ b/tutorials/fastnlp_tutorial_1.ipynb
@@ -0,0 +1,1156 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "cdc25fcd",
+   "metadata": {},
+   "source": [
+    "# T1. dataset 和 vocabulary 的基本使用\n",
+    "\n",
+    "  1   dataset 的使用与结构\n",
+    " \n",
+    "    1.1   dataset 的结构与创建\n",
+    "\n",
+    "    1.2   dataset 的数据预处理\n",
+    "\n",
+    "    1.3   延伸：instance 和 field\n",
+    "\n",
+    "  2   vocabulary 的结构与使用\n",
+    "\n",
+    "    2.1   vocabulary 的创建与修改\n",
+    "\n",
+    "    2.2   vocabulary 与 OOV 问题\n",
+    "\n",
+    "  3   dataset 和 vocabulary 的组合使用\n",
+    " \n",
+    "    3.1   从 dataframe 中加载 dataset\n",
+    "\n",
+    "    3.2   从 dataset 中获取 vocabulary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0eb18a22",
+   "metadata": {},
+   "source": [
+    "## 1. dataset 的基本使用\n",
+    "\n",
+    "### 1.1  dataset 的结构与创建\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`DataSet`模块表示数据集，**`dataset`类似于关系型数据库中的数据表**（下文统一为小写`dataset`）\n",
+    "\n",
+    "  **主要包含`field`字段和`instance`实例两个元素**，对应`table`中的`field`字段和`record`记录\n",
+    "\n",
+    "在`fastNLP 0.8`中，`DataSet`模块被定义在`fastNLP.core.dataset`路径下，导入该模块后，最简单的\n",
+    "\n",
+    "  初始化方法，即将字典形式的表格 **`{'field1': column1, 'field2': column2, ...}`** 传入构造函数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "a1d69ad2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP.core.dataset import DataSet\n",
+    "\n",
+    "data = {'idx': [0, 1, 2],  \n",
+    "        'sentence':[\"This is an apple .\", \"I like apples .\", \"Apples are good for our health .\"],\n",
+    "        'words': [['This', 'is', 'an', 'apple', '.'], \n",
+    "                  ['I', 'like', 'apples', '.'], \n",
+    "                  ['Apples', 'are', 'good', 'for', 'our', 'health', '.']],\n",
+    "        'num': [5, 4, 7]}\n",
+    "\n",
+    "dataset = DataSet(data)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9260fdc6",
+   "metadata": {},
+   "source": [
+    "  在`dataset`的实例中，字段`field`的名称和实例`instance`中的字符串也可以中文"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3d72ef00",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+------+--------------------+------------------------+------+\n",
+      "| 序号 | 句子               | 字符                   | 长度 |\n",
+      "+------+--------------------+------------------------+------+\n",
+      "| 0    | 生活就像海洋，     | ['生', '活', '就', ... | 7    |\n",
+      "| 1    | 只有意志坚强的人， | ['只', '有', '意', ... | 9    |\n",
+      "| 2    | 才能到达彼岸。     | ['才', '能', '到', ... | 7    |\n",
+      "+------+--------------------+------------------------+------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "temp = {'序号': [0, 1, 2],  \n",
+    "        '句子':[\"生活就像海洋，\", \"只有意志坚强的人，\", \"才能到达彼岸。\"],\n",
+    "        '字符': [['生', '活', '就', '像', '海', '洋', '，'], \n",
+    "                  ['只', '有', '意', '志', '坚', '强', '的', '人', '，'], \n",
+    "                  ['才', '能', '到', '达', '彼', '岸', '。']],\n",
+    "        '长度': [7, 9, 7]}\n",
+    "\n",
+    "chinese = DataSet(temp)\n",
+    "print(chinese)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "202e5490",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`drop`方法可以删除满足条件的实例，这里使用了python中的`lambda`表达式\n",
+    "\n",
+    "  注一：在`drop`方法中，通过设置`inplace`参数将删除对应实例后的`dataset`作为一个新的实例生成"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "09b478f8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1969418794120 1971237588872\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropped = dataset\n",
+    "dropped = dropped.drop(lambda ins:ins['num'] < 5, inplace=False)\n",
+    "print(id(dropped), id(dataset))\n",
+    "print(dropped)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa277674",
+   "metadata": {},
+   "source": [
+    "  注二：在`fastNLP 0.8`中，**对`dataset`使用等号**，**其效果是传引用**，**而不是赋值**（？？？）\n",
+    "\n",
+    "    如下所示，**`dropped`和`dataset`具有相同`id`**，**对`dropped`执行删除操作`dataset`同时会被修改**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "77c8583a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1971237588872 1971237588872\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dropped = dataset\n",
+    "dropped.drop(lambda ins:ins['num'] < 5)\n",
+    "print(id(dropped), id(dataset))\n",
+    "print(dropped)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a76199dc",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`delet_instance`方法可以删除对应序号的`instance`实例，序号从0开始"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "d8824b40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+--------------------+------------------------+-----+\n",
+      "| idx | sentence           | words                  | num |\n",
+      "+-----+--------------------+------------------------+-----+\n",
+      "| 0   | This is an apple . | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .    | ['I', 'like', 'appl... | 4   |\n",
+      "+-----+--------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.delete_instance(2)\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f4fa9f33",
+   "metadata": {},
+   "source": [
+    "在`dataset`中，使用`delet_field`方法可以删除对应名称的`field`字段"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f68ddb40",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+--------------------+------------------------------+\n",
+      "| idx | sentence           | words                        |\n",
+      "+-----+--------------------+------------------------------+\n",
+      "| 0   | This is an apple . | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .    | ['I', 'like', 'apples', '... |\n",
+      "+-----+--------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset.delete_field('num')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1e9d42c",
+   "metadata": {},
+   "source": [
+    "### 1.2  dataset 的数据预处理\n",
+    "\n",
+    "在`dataset`模块中，`apply`、`apply_field`、`apply_more`和`apply_field_more`函数可以进行简单的数据预处理\n",
+    "\n",
+    "  **`apply`和`apply_more`针对整条实例**，**`apply_field`和`apply_field_more`仅针对实例的部分字段**\n",
+    "\n",
+    "  **`apply`和`apply_field`仅针对单个字段**，**`apply_more`和`apply_field_more`则可以针对多个字段**\n",
+    "\n",
+    "  **`apply`和`apply_field`返回的是个列表**，**`apply_more`和`apply_field_more`返回的是个字典**\n",
+    "\n",
+    "***\n",
+    "\n",
+    "`apply`的参数包括一个函数`func`和一个新字段名`new_field_name`，函数`func`的处理对象是`dataset`模块中\n",
+    "\n",
+    "  的每个`instance`实例，函数`func`的处理结果存放在`new_field_name`对应的新建字段内"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "72a0b5f9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "data = {'idx': [0, 1, 2], \n",
+    "        'sentence':[\"This is an apple .\", \"I like apples .\", \"Apples are good for our health .\"], }\n",
+    "dataset = DataSet(data)\n",
+    "dataset.apply(lambda ins: ins['sentence'].split(), new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c10275ee",
+   "metadata": {},
+   "source": [
+    "  **`apply`使用的函数可以是一个基于`lambda`表达式的匿名函数**，**也可以是一个自定义的函数**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1a8631f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "\n",
+    "def get_words(instance):\n",
+    "    sentence = instance['sentence']\n",
+    "    words = sentence.split()\n",
+    "    return words\n",
+    "\n",
+    "dataset.apply(get_words, new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64abf745",
+   "metadata": {},
+   "source": [
+    "`apply_field`的参数，除了函数`func`外还有`field_name`和`new_field_name`，该函数`func`的处理对象仅\n",
+    "\n",
+    "  是`dataset`模块中的每个`field_name`对应的字段内容，处理结果存放在`new_field_name`对应的新建字段内"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "057c1d2c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "
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+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------------+------------------------------+\n",
+      "| idx | sentence                     | words                        |\n",
+      "+-----+------------------------------+------------------------------+\n",
+      "| 0   | This is an apple .           | ['This', 'is', 'an', 'app... |\n",
+      "| 1   | I like apples .              | ['I', 'like', 'apples', '... |\n",
+      "| 2   | Apples are good for our h... | ['Apples', 'are', 'good',... |\n",
+      "+-----+------------------------------+------------------------------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_field(lambda sent:sent.split(), field_name='sentence', new_field_name='words')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a9cc8b2",
+   "metadata": {},
+   "source": [
+    "`apply_more`的参数只有函数`func`，函数`func`的处理对象是`dataset`模块中的每个`instance`实例\n",
+    "\n",
+    "  要求函数`func`返回一个字典，根据字典的`key-value`确定存储在`dataset`中的字段名称与内容"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "51e2f02c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_more(lambda ins:{'words': ins['sentence'].split(), 'num': len(ins['sentence'].split())})\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02d2b7ef",
+   "metadata": {},
+   "source": [
+    "`apply_more`的参数只有函数`func`，函数`func`的处理对象是`dataset`模块中的每个`instance`实例\n",
+    "\n",
+    "  要求函数`func`返回一个字典，根据字典的`key-value`确定存储在`dataset`中的字段名称与内容"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "db4295d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n"
+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
\n",
+       "
\n"
+      ],
+      "text/plain": [
+       "\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+-----+------------------------+------------------------+-----+\n",
+      "| idx | sentence               | words                  | num |\n",
+      "+-----+------------------------+------------------------+-----+\n",
+      "| 0   | This is an apple .     | ['This', 'is', 'an'... | 5   |\n",
+      "| 1   | I like apples .        | ['I', 'like', 'appl... | 4   |\n",
+      "| 2   | Apples are good for... | ['Apples', 'are', '... | 7   |\n",
+      "+-----+------------------------+------------------------+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset = DataSet(data)\n",
+    "dataset.apply_field_more(lambda sent:{'words': sent.split(), 'num': len(sent.split())}, \n",
+    "                         field_name='sentence')\n",
+    "print(dataset)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c09e592",
+   "metadata": {},
+   "source": [
+    "### 1.3 延伸：instance 和 field\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`Instance`模块表示数据集`dataset`中的每条数据，被称为实例\n",
+    "\n",
+    "  构造方式类似于构造一个字典，通过键值相同的`Instance`列表，也可以初始化一个`dataset`，代码如下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "012f537c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP.core.dataset import DataSet\n",
+    "from fastNLP.core.dataset import Instance\n",
+    "\n",
+    "dataset = DataSet([\n",
+    "    Instance(sentence=\"This is an apple .\",\n",
+    "        words=['This', 'is', 'an', 'apple', '.'],\n",
+    "        num=5),\n",
+    "    Instance(sentence=\"I like apples .\",\n",
+    "        words=['I', 'like', 'apples', '.'],\n",
+    "        num=4),\n",
+    "    Instance(sentence=\"Apples are good for our health .\",\n",
+    "        words=['Apples', 'are', 'good', 'for', 'our', 'health', '.'],\n",
+    "        num=7),\n",
+    "    ])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2fafb1ef",
+   "metadata": {},
+   "source": [
+    "  通过`items`、`keys`和`values`方法，可以分别获得`dataset`的`item`列表、`key`列表、`value`列表"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "a4c1c10d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "dict_items([('sentence', 'This is an apple .'), ('words', ['This', 'is', 'an', 'apple', '.']), ('num', 5)])\n",
+      "dict_keys(['sentence', 'words', 'num'])\n",
+      "dict_values(['This is an apple .', ['This', 'is', 'an', 'apple', '.'], 5])\n"
+     ]
+    }
+   ],
+   "source": [
+    "ins = Instance(sentence=\"This is an apple .\", words=['This', 'is', 'an', 'apple', '.'], num=5)\n",
+    "\n",
+    "print(ins.items())\n",
+    "print(ins.keys())\n",
+    "print(ins.values())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5459a2d",
+   "metadata": {},
+   "source": [
+    "  通过`add_field`方法，可以在`Instance`实例中，通过参数`field_name`添加字段，通过参数`field`赋值"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "55376402",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "+--------------------+------------------------+-----+-----+\n",
+      "| sentence           | words                  | num | idx |\n",
+      "+--------------------+------------------------+-----+-----+\n",
+      "| This is an apple . | ['This', 'is', 'an'... | 5   | 0   |\n",
+      "+--------------------+------------------------+-----+-----+\n"
+     ]
+    }
+   ],
+   "source": [
+    "ins.add_field(field_name='idx', field=0)\n",
+    "print(ins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49caaa9c",
+   "metadata": {},
+   "source": [
+    "在`fastNLP 0.8`中，使用`FieldArray`模块表示数据集`dataset`中的每条字段名（注：没有`field`类）\n",
+    "\n",
+    "  通过`get_all_fields`方法可以获取`dataset`的字段列表\n",
+    "\n",
+    "  通过`get_field_names`方法可以获取`dataset`的字段名称列表，代码如下"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "fe15f4c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'sentence': ,\n",
+       " 'words': ,\n",
+       " 'num': }"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.get_all_fields()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5433815c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['num', 'sentence', 'words']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset.get_field_names()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4964eeed",
+   "metadata": {},
+   "source": [
+    "其他`dataset`的基本使用：通过`in`或者`has_field`方法可以判断`dataset`的是否包含某种字段\n",
+    "\n",
+    "  通过`rename_field`方法可以更改`dataset`中的字段名称；通过`concat`方法可以实现两个`dataset`中的拼接\n",
+    "\n",
+    "  通过`len`可以统计`dataset`中的实例数目；`dataset`的全部变量与函数可以通过`dir(dataset)`查询"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "25ce5488",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3 False\n",
+      "6 True\n",
+      "+------------------------------+------------------------------+--------+\n",
+      "| sentence                     | words                        | length |\n",
+      "+------------------------------+------------------------------+--------+\n",
+      "| This is an apple .           | ['This', 'is', 'an', 'app... | 5      |\n",
+      "| I like apples .              | ['I', 'like', 'apples', '... | 4      |\n",
+      "| Apples are good for our h... | ['Apples', 'are', 'good',... | 7      |\n",
+      "| This is an apple .           | ['This', 'is', 'an', 'app... | 5      |\n",
+      "| I like apples .              | ['I', 'like', 'apples', '... | 4      |\n",
+      "| Apples are good for our h... | ['Apples', 'are', 'good',... | 7      |\n",
+      "+------------------------------+------------------------------+--------+\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(len(dataset), dataset.has_field('length')) \n",
+    "if 'num' in dataset:\n",
+    "    dataset.rename_field('num', 'length')\n",
+    "elif 'length' in dataset:\n",
+    "    dataset.rename_field('length', 'num')\n",
+    "dataset.concat(dataset)\n",
+    "print(len(dataset), dataset.has_field('length'))  \n",
+    "print(dataset)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e30a6cd7",
+   "metadata": {},
+   "source": [
+    "## 2. vocabulary 的结构与使用\n",
+    "\n",
+    "### 2.1 vocabulary 的创建与修改\n",
+    "\n",
+    "在`fastNLP 0.8`中，使用`Vocabulary`模块表示词汇表，**`vocabulary`的核心是从单词到序号的映射**\n",
+    "\n",
+    "  可以直接通过构造函数实例化，通过查找`word2idx`属性，可以找到`vocabulary`映射对应的字典实现\n",
+    "\n",
+    "  **默认补零`padding`用``表示**，**对应序号为0**；**未知单词`unknown`用``表示**，**对应序号1**\n",
+    "\n",
+    "  通过打印`vocabulary`可以看到词汇表中的单词列表，其中，`padding`和`unknown`不会显示"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "3515e096",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vocabulary([]...)\n",
+      "{'': 0, '': 1}\n",
+      " 0\n",
+      " 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP.core.vocabulary import Vocabulary\n",
+    "\n",
+    "vocab = Vocabulary()\n",
+    "print(vocab)\n",
+    "print(vocab.word2idx)\n",
+    "print(vocab.padding, vocab.padding_idx)\n",
+    "print(vocab.unknown, vocab.unknown_idx)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "640be126",
+   "metadata": {},
+   "source": [
+    "在`vocabulary`中，通过`add_word`方法或`add_word_lst`方法，可以单独或批量添加单词\n",
+    "\n",
+    "  通过`len`或`word_count`属性，可以显示`vocabulary`的单词量和每个单词添加的次数"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "88c7472a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 Counter({'生活': 1, '就像': 1, '海洋': 1})\n",
+      "6 Counter({'生活': 1, '就像': 1, '海洋': 1, '只有': 1})\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab.add_word_lst(['生活', '就像', '海洋'])\n",
+    "print(len(vocab), vocab.word_count)\n",
+    "vocab.add_word('只有')\n",
+    "print(len(vocab), vocab.word_count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f9ec8b28",
+   "metadata": {},
+   "source": [
+    "  **通过`to_word`方法可以找到单词对应的序号**，**通过`to_index`方法可以找到序号对应的单词**\n",
+    "\n",
+    "    由于序号0和序号1已经被占用，所以**新加入的词的序号从2开始计数**，如`'生活'`对应2\n",
+    "\n",
+    "    通过`has_word`方法可以判断单词是否在词汇表中，没有的单词被判做``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "3447acde",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 0\n",
+      " 1\n",
+      "生活 2\n",
+      "只有 5\n",
+      "彼岸 1 False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(vocab.to_word(0), vocab.to_index(''))\n",
+    "print(vocab.to_word(1), vocab.to_index(''))\n",
+    "print(vocab.to_word(2), vocab.to_index('生活'))\n",
+    "print(vocab.to_word(5), vocab.to_index('只有'))\n",
+    "print('彼岸', vocab.to_index('彼岸'), vocab.has_word('彼岸'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4e36850",
+   "metadata": {},
+   "source": [
+    "**`vocabulary`允许反复添加相同单词**，**可以通过`word_count`方法看到相应单词被添加的次数**\n",
+    "\n",
+    "  但其中没有``和``，`vocabulary`的全部变量与函数可以通过`dir(vocabulary)`查询"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "490b101c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "13 Counter({'生活': 2, '就像': 2, '海洋': 2, '只有': 2, '意志': 1, '坚强的': 1, '人': 1, '才': 1, '能': 1, '到达': 1, '彼岸': 1})\n",
+      "彼岸 12 True\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab.add_word_lst(['生活', '就像', '海洋', '只有', '意志', '坚强的', '人', '才', '能', '到达', '彼岸'])\n",
+    "print(len(vocab), vocab.word_count)\n",
+    "print('彼岸', vocab.to_index('彼岸'), vocab.has_word('彼岸'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "23e32a63",
+   "metadata": {},
+   "source": [
+    "### 2.2 vocabulary 与 OOV 问题\n",
+    "\n",
+    "在`vocabulary`模块初始化的时候，可以通过指定`unknown`和`padding`为`None`，限制其存在\n",
+    "\n",
+    "  此时添加单词直接从0开始标号，如果遇到未知单词会直接报错，即 out of vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a99ff909",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'positive': 0, 'negative': 1}\n",
+      "ValueError: word `neutral` not in vocabulary\n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = Vocabulary(unknown=None, padding=None)\n",
+    "\n",
+    "vocab.add_word_lst(['positive', 'negative'])\n",
+    "print(vocab.word2idx)\n",
+    "\n",
+    "try:\n",
+    "    print(vocab.to_index('neutral'))\n",
+    "except ValueError:\n",
+    "    print(\"ValueError: word `neutral` not in vocabulary\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "618da6bd",
+   "metadata": {},
+   "source": [
+    "  相应的，如果只指定其中的`unknown`，则编号会后移一个，同时遇到未知单词全部当做``"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "432f74c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'': 0, 'positive': 1, 'negative': 2}\n",
+      "0 \n"
+     ]
+    }
+   ],
+   "source": [
+    "vocab = Vocabulary(unknown='', padding=None)\n",
+    "\n",
+    "vocab.add_word_lst(['positive', 'negative'])\n",
+    "print(vocab.word2idx)\n",
+    "\n",
+    "print(vocab.to_index('neutral'), vocab.to_word(vocab.to_index('neutral')))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6263f73",
+   "metadata": {},
+   "source": [
+    "## 3 dataset 和 vocabulary 的组合使用\n",
+    " \n",
+    "### 3.1 从 dataframe 中加载 dataset\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89059713",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3dbd985d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f634586",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5ba13989",
+   "metadata": {},
+   "source": [
+    "### 3.2 从 dataset 中获取 vocabulary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2de615b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f5eed18",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorials/figures/T0-fig-trainer-and-evaluator.png b/tutorials/figures/T0-fig-trainer-and-evaluator.png
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