diff --git a/tutorials/fastnlp_1min_tutorial.ipynb b/tutorials/fastnlp_1min_tutorial.ipynb
deleted file mode 100644
index 64d57bc4..00000000
--- a/tutorials/fastnlp_1min_tutorial.ipynb
+++ /dev/null
@@ -1,1775 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# fastNLP 1分钟上手教程"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## step 1\n",
-    "读取数据集"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from fastNLP import DataSet\n",
-    "           \n",
-    "data_path = \"./sample_data/tutorial_sample_dataset.csv\"\n",
-    "ds = DataSet.read_csv(data_path, headers=('raw_sentence', 'label'), sep='\\t')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'raw_sentence': This quiet , introspective and entertaining independent is worth seeking . type=str,\n",
-       "'label': 4 type=str}"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ds[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## step 2\n",
-    "数据预处理\n",
-    "1. 类型转换\n",
-    "2. 切分验证集\n",
-    "3. 构建词典"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[['a',\n",
-       "  'series',\n",
-       "  'of',\n",
-       "  'escapades',\n",
-       "  'demonstrating',\n",
-       "  'the',\n",
-       "  'adage',\n",
-       "  'that',\n",
-       "  'what',\n",
-       "  'is',\n",
-       "  'good',\n",
-       "  'for',\n",
-       "  'the',\n",
-       "  'goose',\n",
-       "  'is',\n",
-       "  'also',\n",
-       "  'good',\n",
-       "  'for',\n",
-       "  'the',\n",
-       "  'gander',\n",
-       "  ',',\n",
-       "  'some',\n",
-       "  'of',\n",
-       "  'which',\n",
-       "  'occasionally',\n",
-       "  'amuses',\n",
-       "  'but',\n",
-       "  'none',\n",
-       "  'of',\n",
-       "  'which',\n",
-       "  'amounts',\n",
-       "  'to',\n",
-       "  'much',\n",
-       "  'of',\n",
-       "  'a',\n",
-       "  'story',\n",
-       "  '.'],\n",
-       " ['this',\n",
-       "  'quiet',\n",
-       "  ',',\n",
-       "  'introspective',\n",
-       "  'and',\n",
-       "  'entertaining',\n",
-       "  'independent',\n",
-       "  'is',\n",
-       "  'worth',\n",
-       "  'seeking',\n",
-       "  '.'],\n",
-       " ['even',\n",
-       "  'fans',\n",
-       "  'of',\n",
-       "  'ismail',\n",
-       "  'merchant',\n",
-       "  \"'s\",\n",
-       "  'work',\n",
-       "  ',',\n",
-       "  'i',\n",
-       "  'suspect',\n",
-       "  ',',\n",
-       "  'would',\n",
-       "  'have',\n",
-       "  'a',\n",
-       "  'hard',\n",
-       "  'time',\n",
-       "  'sitting',\n",
-       "  'through',\n",
-       "  'this',\n",
-       "  'one',\n",
-       "  '.'],\n",
-       " ['a',\n",
-       "  'positively',\n",
-       "  'thrilling',\n",
-       "  'combination',\n",
-       "  'of',\n",
-       "  'ethnography',\n",
-       "  'and',\n",
-       "  'all',\n",
-       "  'the',\n",
-       "  'intrigue',\n",
-       "  ',',\n",
-       "  'betrayal',\n",
-       "  ',',\n",
-       "  'deceit',\n",
-       "  'and',\n",
-       "  'murder',\n",
-       "  'of',\n",
-       "  'a',\n",
-       "  'shakespearean',\n",
-       "  'tragedy',\n",
-       "  'or',\n",
-       "  'a',\n",
-       "  'juicy',\n",
-       "  'soap',\n",
-       "  'opera',\n",
-       "  '.'],\n",
-       " ['aggressive',\n",
-       "  'self-glorification',\n",
-       "  'and',\n",
-       "  'a',\n",
-       "  'manipulative',\n",
-       "  'whitewash',\n",
-       "  '.'],\n",
-       " ['a',\n",
-       "  'comedy-drama',\n",
-       "  'of',\n",
-       "  'nearly',\n",
-       "  'epic',\n",
-       "  'proportions',\n",
-       "  'rooted',\n",
-       "  'in',\n",
-       "  'a',\n",
-       "  'sincere',\n",
-       "  'performance',\n",
-       "  'by',\n",
-       "  'the',\n",
-       "  'title',\n",
-       "  'character',\n",
-       "  'undergoing',\n",
-       "  'midlife',\n",
-       "  'crisis',\n",
-       "  '.'],\n",
-       " ['narratively',\n",
-       "  ',',\n",
-       "  'trouble',\n",
-       "  'every',\n",
-       "  'day',\n",
-       "  'is',\n",
-       "  'a',\n",
-       "  'plodding',\n",
-       "  'mess',\n",
-       "  '.'],\n",
-       " ['the',\n",
-       "  'importance',\n",
-       "  'of',\n",
-       "  'being',\n",
-       "  'earnest',\n",
-       "  ',',\n",
-       "  'so',\n",
-       "  'thick',\n",
-       "  'with',\n",
-       "  'wit',\n",
-       "  'it',\n",
-       "  'plays',\n",
-       "  'like',\n",
-       "  'a',\n",
-       "  'reading',\n",
-       "  'from',\n",
-       "  'bartlett',\n",
-       "  \"'s\",\n",
-       "  'familiar',\n",
-       "  'quotations'],\n",
-       " ['but', 'it', 'does', \"n't\", 'leave', 'you', 'with', 'much', '.'],\n",
-       " ['you', 'could', 'hate', 'it', 'for', 'the', 'same', 'reason', '.'],\n",
-       " ['there',\n",
-       "  \"'s\",\n",
-       "  'little',\n",
-       "  'to',\n",
-       "  'recommend',\n",
-       "  'snow',\n",
-       "  'dogs',\n",
-       "  ',',\n",
-       "  'unless',\n",
-       "  'one',\n",
-       "  'considers',\n",
-       "  'cliched',\n",
-       "  'dialogue',\n",
-       "  'and',\n",
-       "  'perverse',\n",
-       "  'escapism',\n",
-       "  'a',\n",
-       "  'source',\n",
-       "  'of',\n",
-       "  'high',\n",
-       "  'hilarity',\n",
-       "  '.'],\n",
-       " ['kung',\n",
-       "  'pow',\n",
-       "  'is',\n",
-       "  'oedekerk',\n",
-       "  \"'s\",\n",
-       "  'realization',\n",
-       "  'of',\n",
-       "  'his',\n",
-       "  'childhood',\n",
-       "  'dream',\n",
-       "  'to',\n",
-       "  'be',\n",
-       "  'in',\n",
-       "  'a',\n",
-       "  'martial-arts',\n",
-       "  'flick',\n",
-       "  ',',\n",
-       "  'and',\n",
-       "  'proves',\n",
-       "  'that',\n",
-       "  'sometimes',\n",
-       "  'the',\n",
-       "  'dreams',\n",
-       "  'of',\n",
-       "  'youth',\n",
-       "  'should',\n",
-       "  'remain',\n",
-       "  'just',\n",
-       "  'that',\n",
-       "  '.'],\n",
-       " ['the', 'performances', 'are', 'an', 'absolute', 'joy', '.'],\n",
-       " ['fresnadillo',\n",
-       "  'has',\n",
-       "  'something',\n",
-       "  'serious',\n",
-       "  'to',\n",
-       "  'say',\n",
-       "  'about',\n",
-       "  'the',\n",
-       "  'ways',\n",
-       "  'in',\n",
-       "  'which',\n",
-       "  'extravagant',\n",
-       "  'chance',\n",
-       "  'can',\n",
-       "  'distort',\n",
-       "  'our',\n",
-       "  'perspective',\n",
-       "  'and',\n",
-       "  'throw',\n",
-       "  'us',\n",
-       "  'off',\n",
-       "  'the',\n",
-       "  'path',\n",
-       "  'of',\n",
-       "  'good',\n",
-       "  'sense',\n",
-       "  '.'],\n",
-       " ['i',\n",
-       "  'still',\n",
-       "  'like',\n",
-       "  'moonlight',\n",
-       "  'mile',\n",
-       "  ',',\n",
-       "  'better',\n",
-       "  'judgment',\n",
-       "  'be',\n",
-       "  'damned',\n",
-       "  '.'],\n",
-       " ['a',\n",
-       "  'welcome',\n",
-       "  'relief',\n",
-       "  'from',\n",
-       "  'baseball',\n",
-       "  'movies',\n",
-       "  'that',\n",
-       "  'try',\n",
-       "  'too',\n",
-       "  'hard',\n",
-       "  'to',\n",
-       "  'be',\n",
-       "  'mythic',\n",
-       "  ',',\n",
-       "  'this',\n",
-       "  'one',\n",
-       "  'is',\n",
-       "  'a',\n",
-       "  'sweet',\n",
-       "  'and',\n",
-       "  'modest',\n",
-       "  'and',\n",
-       "  'ultimately',\n",
-       "  'winning',\n",
-       "  'story',\n",
-       "  '.'],\n",
-       " ['a',\n",
-       "  'bilingual',\n",
-       "  'charmer',\n",
-       "  ',',\n",
-       "  'just',\n",
-       "  'like',\n",
-       "  'the',\n",
-       "  'woman',\n",
-       "  'who',\n",
-       "  'inspired',\n",
-       "  'it'],\n",
-       " ['like',\n",
-       "  'a',\n",
-       "  'less',\n",
-       "  'dizzily',\n",
-       "  'gorgeous',\n",
-       "  'companion',\n",
-       "  'to',\n",
-       "  'mr.',\n",
-       "  'wong',\n",
-       "  \"'s\",\n",
-       "  'in',\n",
-       "  'the',\n",
-       "  'mood',\n",
-       "  'for',\n",
-       "  'love',\n",
-       "  '--',\n",
-       "  'very',\n",
-       "  'much',\n",
-       "  'a',\n",
-       "  'hong',\n",
-       "  'kong',\n",
-       "  'movie',\n",
-       "  'despite',\n",
-       "  'its',\n",
-       "  'mainland',\n",
-       "  'setting',\n",
-       "  '.'],\n",
-       " ['as',\n",
-       "  'inept',\n",
-       "  'as',\n",
-       "  'big-screen',\n",
-       "  'remakes',\n",
-       "  'of',\n",
-       "  'the',\n",
-       "  'avengers',\n",
-       "  'and',\n",
-       "  'the',\n",
-       "  'wild',\n",
-       "  'wild',\n",
-       "  'west',\n",
-       "  '.'],\n",
-       " ['it',\n",
-       "  \"'s\",\n",
-       "  'everything',\n",
-       "  'you',\n",
-       "  \"'d\",\n",
-       "  'expect',\n",
-       "  '--',\n",
-       "  'but',\n",
-       "  'nothing',\n",
-       "  'more',\n",
-       "  '.'],\n",
-       " ['best', 'indie', 'of', 'the', 'year', ',', 'so', 'far', '.'],\n",
-       " ['hatfield',\n",
-       "  'and',\n",
-       "  'hicks',\n",
-       "  'make',\n",
-       "  'the',\n",
-       "  'oddest',\n",
-       "  'of',\n",
-       "  'couples',\n",
-       "  ',',\n",
-       "  'and',\n",
-       "  'in',\n",
-       "  'this',\n",
-       "  'sense',\n",
-       "  'the',\n",
-       "  'movie',\n",
-       "  'becomes',\n",
-       "  'a',\n",
-       "  'study',\n",
-       "  'of',\n",
-       "  'the',\n",
-       "  'gambles',\n",
-       "  'of',\n",
-       "  'the',\n",
-       "  'publishing',\n",
-       "  'world',\n",
-       "  ',',\n",
-       "  'offering',\n",
-       "  'a',\n",
-       "  'case',\n",
-       "  'study',\n",
-       "  'that',\n",
-       "  'exists',\n",
-       "  'apart',\n",
-       "  'from',\n",
-       "  'all',\n",
-       "  'the',\n",
-       "  'movie',\n",
-       "  \"'s\",\n",
-       "  'political',\n",
-       "  'ramifications',\n",
-       "  '.'],\n",
-       " ['it',\n",
-       "  \"'s\",\n",
-       "  'like',\n",
-       "  'going',\n",
-       "  'to',\n",
-       "  'a',\n",
-       "  'house',\n",
-       "  'party',\n",
-       "  'and',\n",
-       "  'watching',\n",
-       "  'the',\n",
-       "  'host',\n",
-       "  'defend',\n",
-       "  'himself',\n",
-       "  'against',\n",
-       "  'a',\n",
-       "  'frothing',\n",
-       "  'ex-girlfriend',\n",
-       "  '.'],\n",
-       " ['that',\n",
-       "  'the',\n",
-       "  'chuck',\n",
-       "  'norris',\n",
-       "  '``',\n",
-       "  'grenade',\n",
-       "  'gag',\n",
-       "  \"''\",\n",
-       "  'occurs',\n",
-       "  'about',\n",
-       "  '7',\n",
-       "  'times',\n",
-       "  'during',\n",
-       "  'windtalkers',\n",
-       "  'is',\n",
-       "  'a',\n",
-       "  'good',\n",
-       "  'indication',\n",
-       "  'of',\n",
-       "  'how',\n",
-       "  'serious-minded',\n",
-       "  'the',\n",
-       "  'film',\n",
-       "  'is',\n",
-       "  '.'],\n",
-       " ['the',\n",
-       "  'plot',\n",
-       "  'is',\n",
-       "  'romantic',\n",
-       "  'comedy',\n",
-       "  'boilerplate',\n",
-       "  'from',\n",
-       "  'start',\n",
-       "  'to',\n",
-       "  'finish',\n",
-       "  '.'],\n",
-       " ['it',\n",
-       "  'arrives',\n",
-       "  'with',\n",
-       "  'an',\n",
-       "  'impeccable',\n",
-       "  'pedigree',\n",
-       "  ',',\n",
-       "  'mongrel',\n",
-       "  'pep',\n",
-       "  ',',\n",
-       "  'and',\n",
-       "  'almost',\n",
-       "  'indecipherable',\n",
-       "  'plot',\n",
-       "  'complications',\n",
-       "  '.'],\n",
-       " ['a',\n",
-       "  'film',\n",
-       "  'that',\n",
-       "  'clearly',\n",
-       "  'means',\n",
-       "  'to',\n",
-       "  'preach',\n",
-       "  'exclusively',\n",
-       "  'to',\n",
-       "  'the',\n",
-       "  'converted',\n",
-       "  '.'],\n",
-       " ['while',\n",
-       "  'the',\n",
-       "  'importance',\n",
-       "  'of',\n",
-       "  'being',\n",
-       "  'earnest',\n",
-       "  'offers',\n",
-       "  'opportunities',\n",
-       "  'for',\n",
-       "  'occasional',\n",
-       "  'smiles',\n",
-       "  'and',\n",
-       "  'chuckles',\n",
-       "  ',',\n",
-       "  'it',\n",
-       "  'does',\n",
-       "  \"n't\",\n",
-       "  'give',\n",
-       "  'us',\n",
-       "  'a',\n",
-       "  'reason',\n",
-       "  'to',\n",
-       "  'be',\n",
-       "  'in',\n",
-       "  'the',\n",
-       "  'theater',\n",
-       "  'beyond',\n",
-       "  'wilde',\n",
-       "  \"'s\",\n",
-       "  'wit',\n",
-       "  'and',\n",
-       "  'the',\n",
-       "  'actors',\n",
-       "  \"'\",\n",
-       "  'performances',\n",
-       "  '.'],\n",
-       " ['the',\n",
-       "  'latest',\n",
-       "  'vapid',\n",
-       "  'actor',\n",
-       "  \"'s\",\n",
-       "  'exercise',\n",
-       "  'to',\n",
-       "  'appropriate',\n",
-       "  'the',\n",
-       "  'structure',\n",
-       "  'of',\n",
-       "  'arthur',\n",
-       "  'schnitzler',\n",
-       "  \"'s\",\n",
-       "  'reigen',\n",
-       "  '.'],\n",
-       " ['more',\n",
-       "  'vaudeville',\n",
-       "  'show',\n",
-       "  'than',\n",
-       "  'well-constructed',\n",
-       "  'narrative',\n",
-       "  ',',\n",
-       "  'but',\n",
-       "  'on',\n",
-       "  'those',\n",
-       "  'terms',\n",
-       "  'it',\n",
-       "  \"'s\",\n",
-       "  'inoffensive',\n",
-       "  'and',\n",
-       "  'actually',\n",
-       "  'rather',\n",
-       "  'sweet',\n",
-       "  '.'],\n",
-       " ['nothing', 'more', 'than', 'a', 'run-of-the-mill', 'action', 'flick', '.'],\n",
-       " ['hampered',\n",
-       "  '--',\n",
-       "  'no',\n",
-       "  ',',\n",
-       "  'paralyzed',\n",
-       "  '--',\n",
-       "  'by',\n",
-       "  'a',\n",
-       "  'self-indulgent',\n",
-       "  'script',\n",
-       "  '...',\n",
-       "  'that',\n",
-       "  'aims',\n",
-       "  'for',\n",
-       "  'poetry',\n",
-       "  'and',\n",
-       "  'ends',\n",
-       "  'up',\n",
-       "  'sounding',\n",
-       "  'like',\n",
-       "  'satire',\n",
-       "  '.'],\n",
-       " ['ice',\n",
-       "  'age',\n",
-       "  'is',\n",
-       "  'the',\n",
-       "  'first',\n",
-       "  'computer-generated',\n",
-       "  'feature',\n",
-       "  'cartoon',\n",
-       "  'to',\n",
-       "  'feel',\n",
-       "  'like',\n",
-       "  'other',\n",
-       "  'movies',\n",
-       "  ',',\n",
-       "  'and',\n",
-       "  'that',\n",
-       "  'makes',\n",
-       "  'for',\n",
-       "  'some',\n",
-       "  'glacial',\n",
-       "  'pacing',\n",
-       "  'early',\n",
-       "  'on',\n",
-       "  '.'],\n",
-       " ['there',\n",
-       "  \"'s\",\n",
-       "  'very',\n",
-       "  'little',\n",
-       "  'sense',\n",
-       "  'to',\n",
-       "  'what',\n",
-       "  \"'s\",\n",
-       "  'going',\n",
-       "  'on',\n",
-       "  'here',\n",
-       "  ',',\n",
-       "  'but',\n",
-       "  'the',\n",
-       "  'makers',\n",
-       "  'serve',\n",
-       "  'up',\n",
-       "  'the',\n",
-       "  'cliches',\n",
-       "  'with',\n",
-       "  'considerable',\n",
-       "  'dash',\n",
-       "  '.'],\n",
-       " ['cattaneo',\n",
-       "  'should',\n",
-       "  'have',\n",
-       "  'followed',\n",
-       "  'the',\n",
-       "  'runaway',\n",
-       "  'success',\n",
-       "  'of',\n",
-       "  'his',\n",
-       "  'first',\n",
-       "  'film',\n",
-       "  ',',\n",
-       "  'the',\n",
-       "  'full',\n",
-       "  'monty',\n",
-       "  ',',\n",
-       "  'with',\n",
-       "  'something',\n",
-       "  'different',\n",
-       "  '.'],\n",
-       " ['they',\n",
-       "  \"'re\",\n",
-       "  'the',\n",
-       "  'unnamed',\n",
-       "  ',',\n",
-       "  'easily',\n",
-       "  'substitutable',\n",
-       "  'forces',\n",
-       "  'that',\n",
-       "  'serve',\n",
-       "  'as',\n",
-       "  'whatever',\n",
-       "  'terror',\n",
-       "  'the',\n",
-       "  'heroes',\n",
-       "  'of',\n",
-       "  'horror',\n",
-       "  'movies',\n",
-       "  'try',\n",
-       "  'to',\n",
-       "  'avoid',\n",
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-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 将所有数字转为小写\n",
-    "ds.apply(lambda x: x['raw_sentence'].lower(), new_field_name='raw_sentence')\n",
-    "# label转int\n",
-    "ds.apply(lambda x: int(x['label']), new_field_name='label_seq', is_target=True)\n",
-    "\n",
-    "def split_sent(ins):\n",
-    "    return ins['raw_sentence'].split()\n",
-    "ds.apply(split_sent, new_field_name='words', is_input=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train size:  54\n",
-      "Test size:  23\n"
-     ]
-    }
-   ],
-   "source": [
-    "# 分割训练集/验证集\n",
-    "train_data, dev_data = ds.split(0.3)\n",
-    "print(\"Train size: \", len(train_data))\n",
-    "print(\"Test size: \", len(dev_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[120, 121, 6, 2, 122, 5, 72, 123, 3],\n",
-       " [14,\n",
-       "  4,\n",
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-       "  153,\n",
-       "  154,\n",
-       "  155,\n",
-       "  8,\n",
-       "  156,\n",
-       "  157,\n",
-       "  9,\n",
-       "  16,\n",
-       "  2,\n",
-       "  158,\n",
-       "  21,\n",
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-       "  30,\n",
-       "  98,\n",
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-       "  163,\n",
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-       "  165,\n",
-       "  3],\n",
-       " [4,\n",
-       "  112,\n",
-       "  113,\n",
-       "  15,\n",
-       "  114,\n",
-       "  35,\n",
-       "  10,\n",
-       "  68,\n",
-       "  115,\n",
-       "  69,\n",
-       "  8,\n",
-       "  23,\n",
-       "  116,\n",
-       "  5,\n",
-       "  18,\n",
-       "  36,\n",
-       "  11,\n",
-       "  4,\n",
-       "  70,\n",
-       "  7,\n",
-       "  117,\n",
-       "  7,\n",
-       "  118,\n",
-       "  119,\n",
-       "  71,\n",
-       "  3],\n",
-       " [4, 1, 1, 5, 138, 14, 2, 1, 1, 1, 12],\n",
-       " [2, 27, 11, 139, 140, 141, 15, 142, 8, 143, 3],\n",
-       " [12, 9, 14, 32, 8, 4, 59, 60, 7, 61, 2, 62, 63, 64, 65, 4, 66, 67, 3],\n",
-       " [97, 145, 14, 146, 147, 5, 148, 149, 23, 150, 3],\n",
-       " [4, 1, 1, 5, 138, 14, 2, 1, 1, 1, 12],\n",
-       " [4, 1, 1, 5, 138, 14, 2, 1, 1, 1, 12],\n",
-       " [14,\n",
-       "  4,\n",
-       "  152,\n",
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-       "  154,\n",
-       "  155,\n",
-       "  8,\n",
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-       "  3],\n",
-       " [10,\n",
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-       "  91,\n",
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-       "  4,\n",
-       "  28,\n",
-       "  94,\n",
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-       "  95,\n",
-       "  96,\n",
-       "  2,\n",
-       "  17,\n",
-       "  11,\n",
-       "  3],\n",
-       " [12, 73, 20, 33, 74, 75, 5, 76, 77, 5, 7, 78, 79, 27, 80, 3],\n",
-       " [12, 78, 1, 24, 1, 2, 13, 11, 31, 1, 16, 1, 1, 133, 16, 1, 1, 3],\n",
-       " [24, 107, 24, 108, 109, 6, 2, 110, 7, 2, 34, 34, 111, 3],\n",
-       " [2, 27, 11, 139, 140, 141, 15, 142, 8, 143, 3],\n",
-       " [24, 107, 24, 108, 109, 6, 2, 110, 7, 2, 34, 34, 111, 3],\n",
-       " [97, 145, 14, 146, 147, 5, 148, 149, 23, 150, 3],\n",
-       " [4,\n",
-       "  112,\n",
-       "  113,\n",
-       "  15,\n",
-       "  114,\n",
-       "  35,\n",
-       "  10,\n",
-       "  68,\n",
-       "  115,\n",
-       "  69,\n",
-       "  8,\n",
-       "  23,\n",
-       "  116,\n",
-       "  5,\n",
-       "  18,\n",
-       "  36,\n",
-       "  11,\n",
-       "  4,\n",
-       "  70,\n",
-       "  7,\n",
-       "  117,\n",
-       "  7,\n",
-       "  118,\n",
-       "  119,\n",
-       "  71,\n",
-       "  3],\n",
-       " [12, 9, 99, 29, 100, 101, 30, 22, 58, 31, 3],\n",
-       " [12, 9, 99, 29, 100, 101, 30, 22, 58, 31, 3],\n",
-       " [120, 121, 6, 2, 122, 5, 72, 123, 3],\n",
-       " [1, 30, 1, 5, 1, 30, 1, 4, 1, 1, 1, 10, 1, 21, 1, 7, 1, 1, 1, 14, 1, 3],\n",
-       " [1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  8,\n",
-       "  1,\n",
-       "  89,\n",
-       "  2,\n",
-       "  1,\n",
-       "  16,\n",
-       "  151,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  7,\n",
-       "  1,\n",
-       "  1,\n",
-       "  1,\n",
-       "  2,\n",
-       "  1,\n",
-       "  6,\n",
-       "  28,\n",
-       "  25,\n",
-       "  3]]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from fastNLP import Vocabulary\n",
-    "vocab = Vocabulary(min_freq=2)\n",
-    "train_data.apply(lambda x: [vocab.add(word) for word in x['words']])\n",
-    "\n",
-    "# index句子, Vocabulary.to_index(word)\n",
-    "train_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n",
-    "dev_data.apply(lambda x: [vocab.to_index(word) for word in x['words']], new_field_name='word_seq', is_input=True)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## step 3\n",
-    " 定义模型"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from fastNLP.models import CNNText\n",
-    "model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## step 4\n",
-    "开始训练"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "input fields after batch(if batch size is 2):\n",
-      "\twords: (1)type:numpy.ndarray (2)dtype:object, (3)shape:(2,) \n",
-      "\tword_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 11]) \n",
-      "target fields after batch(if batch size is 2):\n",
-      "\tlabel_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
-      "\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'numpy.ndarray' object has no attribute 'contiguous'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-7-4b34d005949c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m                   \u001b[0mdev_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdev_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m                   \u001b[0mloss\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m                   \u001b[0mmetrics\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mAccuracyMetric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m                   )\n\u001b[1;32m      8\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, train_data, model, optimizer, loss, batch_size, sampler, update_every, n_epochs, print_every, dev_data, metrics, metric_key, validate_every, save_path, prefetch, use_tqdm, device, callbacks, check_code_level)\u001b[0m\n\u001b[1;32m    447\u001b[0m             _check_code(dataset=train_data, model=model, losser=losser, metrics=metrics, dev_data=dev_data,\n\u001b[1;32m    448\u001b[0m                         \u001b[0mmetric_key\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmetric_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_level\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_code_level\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 449\u001b[0;31m                         batch_size=min(batch_size, DEFAULT_CHECK_BATCH_SIZE))\n\u001b[0m\u001b[1;32m    450\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    451\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/trainer.py\u001b[0m in \u001b[0;36m_check_code\u001b[0;34m(dataset, model, losser, metrics, batch_size, dev_data, metric_key, check_level)\u001b[0m\n\u001b[1;32m    811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m         \u001b[0mrefined_batch_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_build_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mbatch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 813\u001b[0;31m         \u001b[0mpred_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mrefined_batch_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    814\u001b[0m         \u001b[0mfunc_signature\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_func_signature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    815\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/models/cnn_text_classification.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, words, seq_len)\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mdict\u001b[0m \u001b[0mof\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLongTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_classes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m         \"\"\"\n\u001b[0;32m---> 60\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwords\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# [N,L] -> [N,L,C]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     61\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv_pool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# [N,L,C] -> [N,C]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    489\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    490\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 491\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    492\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    493\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/modules/encoder/embedding.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     33\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m \u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseq_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0membed_dim\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m         \"\"\"\n\u001b[0;32m---> 35\u001b[0;31m         \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/torch/nn/modules/sparse.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    106\u001b[0m         return F.embedding(\n\u001b[1;32m    107\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_norm\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m             self.norm_type, self.scale_grad_by_freq, self.sparse)\n\u001b[0m\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/Users/fdujyn/anaconda3/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36membedding\u001b[0;34m(input, weight, padding_idx, max_norm, norm_type, scale_grad_by_freq, sparse)\u001b[0m\n\u001b[1;32m   1062\u001b[0m                  [ 0.6262,  0.2438,  0.7471]]])\n\u001b[1;32m   1063\u001b[0m     \"\"\"\n\u001b[0;32m-> 1064\u001b[0;31m     \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontiguous\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1065\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpadding_idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1066\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpadding_idx\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'contiguous'"
-     ]
-    }
-   ],
-   "source": [
-    "from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric\n",
-    "trainer = Trainer(model=model, \n",
-    "                  train_data=train_data, \n",
-    "                  dev_data=dev_data,\n",
-    "                  loss=CrossEntropyLoss(),\n",
-    "                  metrics=AccuracyMetric()\n",
-    "                  )\n",
-    "trainer.train()\n",
-    "print('Train finished!')\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 本教程结束。更多操作请参考进阶教程。"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.6.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/tutorials/quickstart.ipynb b/tutorials/quickstart.ipynb
new file mode 100644
index 00000000..00c30c93
--- /dev/null
+++ b/tutorials/quickstart.ipynb
@@ -0,0 +1,280 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 快速入门"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP.io import CSVLoader\n",
+    "\n",
+    "loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\\t')\n",
+    "dataset = loader.load(\"./sample_data/tutorial_sample_dataset.csv\")\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str,\n",
+       "'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'words': ['a', 'series', 'of', 'escapades', 'demonstrating', 'the', 'adage', 'that', 'what', 'is', 'good', 'for', 'the', 'goose', 'is', 'also', 'good', 'for', 'the', 'gander', ',', 'some', 'of', 'which', 'occasionally', 'amuses', 'but', 'none', 'of', 'which', 'amounts', 'to', 'much', 'of', 'a', 'story', '.'] type=list}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 将所有字母转为小写, 并所有句子变成单词序列\n",
+    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence')\n",
+    "dataset.apply(lambda x: x['sentence'].split(), new_field_name='words', is_input=True)\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str,\n",
+       "'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Vocabulary\n",
+    "\n",
+    "# 使用Vocabulary类统计单词，并将单词序列转化为数字序列\n",
+    "vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words')\n",
+    "vocab.index_dataset(dataset, field_name='words',new_field_name='words')\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str,\n",
+       "'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
+       "'target': 1 type=int}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 将label转为整数，并设置为 target\n",
+    "dataset.apply(lambda x: int(x['label']), new_field_name='target', is_target=True)\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CNNText(\n",
+       "  (embed): Embedding(\n",
+       "    177, 50\n",
+       "    (dropout): Dropout(p=0.0)\n",
+       "  )\n",
+       "  (conv_pool): ConvMaxpool(\n",
+       "    (convs): ModuleList(\n",
+       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
+       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
+       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
+       "    )\n",
+       "  )\n",
+       "  (dropout): Dropout(p=0.1)\n",
+       "  (fc): Linear(in_features=12, out_features=5, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP.models import CNNText\n",
+    "model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(62, 15)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 分割训练集/验证集\n",
+    "train_data, dev_data = dataset.split(0.2)\n",
+    "len(train_data), len(dev_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input fields after batch(if batch size is 2):\n",
+      "\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
+      "target fields after batch(if batch size is 2):\n",
+      "\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "\n",
+      "training epochs started 2019-05-09-10-59-39\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.333333\n",
+      "\n",
+      "Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.533333\n",
+      "\n",
+      "Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.533333\n",
+      "\n",
+      "Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.533333\n",
+      "\n",
+      "Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.6\n",
+      "\n",
+      "Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.8\n",
+      "\n",
+      "Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.8\n",
+      "\n",
+      "Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.733333\n",
+      "\n",
+      "Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.733333\n",
+      "\n",
+      "Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.733333\n",
+      "\n",
+      "\n",
+      "In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.8\n",
+      "Reloaded the best model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'best_eval': {'AccuracyMetric': {'acc': 0.8}},\n",
+       " 'best_epoch': 6,\n",
+       " 'best_step': 12,\n",
+       " 'seconds': 0.22}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric\n",
+    "\n",
+    "# 定义trainer并进行训练\n",
+    "trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data,\n",
+    "                  loss=CrossEntropyLoss(), metrics=AccuracyMetric())\n",
+    "trainer.train()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/tutorial_one.ipynb b/tutorials/tutorial_one.ipynb
new file mode 100644
index 00000000..db302238
--- /dev/null
+++ b/tutorials/tutorial_one.ipynb
@@ -0,0 +1,831 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": true
+   },
+   "source": [
+    "# 详细指南"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据读入"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str}"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP.io import CSVLoader\n",
+    "\n",
+    "loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\\t')\n",
+    "dataset = loader.load(\"./sample_data/tutorial_sample_dataset.csv\")\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instance表示一个样本，由一个或多个field（域，属性，特征）组成，每个field有名字和值。\n",
+    "\n",
+    "在初始化Instance时即可定义它包含的域，使用 \"field_name=field_value\"的写法。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': fake data type=str,\n",
+       "'label': 0 type=str}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Instance\n",
+    "\n",
+    "dataset.append(Instance(raw_sentence='fake data', label='0'))\n",
+    "dataset[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 数据处理"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'label': 1 type=str,\n",
+       "'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+       "'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
+       "'target': 1 type=int}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Vocabulary\n",
+    "\n",
+    "# 将所有字母转为小写, 并所有句子变成单词序列\n",
+    "dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence')\n",
+    "dataset.apply_field(lambda x: x.split(), field_name='sentence', new_field_name='words')\n",
+    "\n",
+    "# 使用Vocabulary类统计单词，并将单词序列转化为数字序列\n",
+    "vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words')\n",
+    "vocab.index_dataset(dataset, field_name='words',new_field_name='words')\n",
+    "\n",
+    "# 将label转为整数\n",
+    "dataset.apply(lambda x: int(x['label']), new_field_name='target')\n",
+    "dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+      "'label': 1 type=str,\n",
+      "'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
+      "'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
+      "'target': 1 type=int,\n",
+      "'seq_len': 37 type=int}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 增加长度信息\n",
+    "dataset.apply_field(lambda x: len(x), field_name='words', new_field_name='seq_len')\n",
+    "print(dataset[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 使用内置模块CNNText\n",
+    "设置为符合内置模块的名称"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CNNText(\n",
+       "  (embed): Embedding(\n",
+       "    177, 50\n",
+       "    (dropout): Dropout(p=0.0)\n",
+       "  )\n",
+       "  (conv_pool): ConvMaxpool(\n",
+       "    (convs): ModuleList(\n",
+       "      (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
+       "      (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
+       "      (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
+       "    )\n",
+       "  )\n",
+       "  (dropout): Dropout(p=0.1)\n",
+       "  (fc): Linear(in_features=12, out_features=5, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP.models import CNNText\n",
+    "\n",
+    "model_cnn = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
+    "model_cnn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "我们在使用内置模块的时候，还应该使用应该注意把 field 设定成符合内置模型输入输出的名字。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "words\n",
+      "seq_len\n",
+      "target\n"
+     ]
+    }
+   ],
+   "source": [
+    "from fastNLP import Const\n",
+    "\n",
+    "dataset.rename_field('words', Const.INPUT)\n",
+    "dataset.rename_field('seq_len', Const.INPUT_LEN)\n",
+    "dataset.rename_field('target', Const.TARGET)\n",
+    "\n",
+    "dataset.set_input(Const.INPUT, Const.INPUT_LEN)\n",
+    "dataset.set_target(Const.TARGET)\n",
+    "\n",
+    "print(Const.INPUT)\n",
+    "print(Const.INPUT_LEN)\n",
+    "print(Const.TARGET)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 分割训练集/验证集/测试集"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(64, 7, 7)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_dev_data, test_data = dataset.split(0.1)\n",
+    "train_data, dev_data = train_dev_data.split(0.1)\n",
+    "len(train_data), len(dev_data), len(test_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 训练(model_cnn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### loss\n",
+    "训练模型需要提供一个损失函数\n",
+    "\n",
+    "下面提供了一个在分类问题中常用的交叉熵损失。注意它的**初始化参数**。\n",
+    "\n",
+    "pred参数对应的是模型的forward返回的dict的一个key的名字，这里是\"output\"。\n",
+    "\n",
+    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import CrossEntropyLoss\n",
+    "\n",
+    "# loss = CrossEntropyLoss()\n",
+    "# 等价于\n",
+    "loss = CrossEntropyLoss(pred=Const.OUTPUT, target=Const.TARGET)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Metric\n",
+    "定义评价指标\n",
+    "\n",
+    "这里使用准确率。参数的“命名规则”跟上面类似。\n",
+    "\n",
+    "pred参数对应的是模型的predict方法返回的dict的一个key的名字，这里是\"predict\"。\n",
+    "\n",
+    "target参数对应的是dataset作为标签的field的名字，这里是\"label_seq\"。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastNLP import AccuracyMetric\n",
+    "\n",
+    "# metrics=AccuracyMetric()\n",
+    "# 等价于\n",
+    "metrics=AccuracyMetric(pred=Const.OUTPUT, target=Const.TARGET)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input fields after batch(if batch size is 2):\n",
+      "\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
+      "\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "target fields after batch(if batch size is 2):\n",
+      "\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "\n",
+      "training epochs started 2019-05-12-21-38-34\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714\n",
+      "\n",
+      "Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.428571\n",
+      "\n",
+      "Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "\n",
+      "In Epoch:8/Step:16, got best dev performance:AccuracyMetric: acc=0.857143\n",
+      "Reloaded the best model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
+       " 'best_epoch': 8,\n",
+       " 'best_step': 16,\n",
+       " 'seconds': 0.21}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Trainer\n",
+    "\n",
+    "trainer = Trainer(model=model_cnn, train_data=train_data, dev_data=dev_data, loss=loss, metrics=metrics)\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 测试(model_cnn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[tester] \n",
+      "AccuracyMetric: acc=0.857143\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'AccuracyMetric': {'acc': 0.857143}}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Tester\n",
+    "\n",
+    "tester = Tester(test_data, model_cnn, metrics=AccuracyMetric())\n",
+    "tester.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 编写自己的模型\n",
+    "\n",
+    "完全支持 pytorch 的模型，与 pytorch 唯一不同的是返回结果是一个字典，字典中至少需要包含 \"pred\" 这个字段"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "\n",
+    "class LSTMText(nn.Module):\n",
+    "    def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        self.embedding = nn.Embedding(vocab_size, embedding_dim)\n",
+    "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=True, dropout=dropout)\n",
+    "        self.fc = nn.Linear(hidden_dim * 2, output_dim)\n",
+    "        self.dropout = nn.Dropout(dropout)\n",
+    "\n",
+    "    def forward(self, words):\n",
+    "        # (input) words : (batch_size, seq_len)\n",
+    "        words = words.permute(1,0)\n",
+    "        # words : (seq_len, batch_size)\n",
+    "\n",
+    "        embedded = self.dropout(self.embedding(words))\n",
+    "        # embedded : (seq_len, batch_size, embedding_dim)\n",
+    "        output, (hidden, cell) = self.lstm(embedded)\n",
+    "        # output: (seq_len, batch_size, hidden_dim * 2)\n",
+    "        # hidden: (num_layers * 2, batch_size, hidden_dim)\n",
+    "        # cell: (num_layers * 2, batch_size, hidden_dim)\n",
+    "\n",
+    "        hidden = torch.cat((hidden[-2, :, :], hidden[-1, :, :]), dim=1)\n",
+    "        hidden = self.dropout(hidden)\n",
+    "        # hidden: (batch_size, hidden_dim * 2)\n",
+    "\n",
+    "        pred = self.fc(hidden.squeeze(0))\n",
+    "        # result: (batch_size, output_dim)\n",
+    "        return {\"pred\":pred}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input fields after batch(if batch size is 2):\n",
+      "\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
+      "\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "target fields after batch(if batch size is 2):\n",
+      "\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "\n",
+      "training epochs started 2019-05-12-21-38-36\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.714286\n",
+      "\n",
+      "Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "\n",
+      "In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.857143\n",
+      "Reloaded the best model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
+       " 'best_epoch': 6,\n",
+       " 'best_step': 12,\n",
+       " 'seconds': 2.15}"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_lstm = LSTMText(len(vocab),50,5)\n",
+    "trainer = Trainer(model=model_lstm, train_data=train_data, dev_data=dev_data, loss=loss, metrics=metrics)\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[tester] \n",
+      "AccuracyMetric: acc=0.857143\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'AccuracyMetric': {'acc': 0.857143}}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tester = Tester(test_data, model_lstm, metrics=AccuracyMetric())\n",
+    "tester.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用 Batch编写自己的训练过程"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0 Avg Loss: 3.11 18ms\n",
+      "Epoch 1 Avg Loss: 2.88 30ms\n",
+      "Epoch 2 Avg Loss: 2.69 42ms\n",
+      "Epoch 3 Avg Loss: 2.47 54ms\n",
+      "Epoch 4 Avg Loss: 2.38 67ms\n",
+      "Epoch 5 Avg Loss: 2.10 78ms\n",
+      "Epoch 6 Avg Loss: 2.06 91ms\n",
+      "Epoch 7 Avg Loss: 1.92 103ms\n",
+      "Epoch 8 Avg Loss: 1.91 114ms\n",
+      "Epoch 9 Avg Loss: 1.76 126ms\n",
+      "[tester] \n",
+      "AccuracyMetric: acc=0.571429\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'AccuracyMetric': {'acc': 0.571429}}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import BucketSampler\n",
+    "from fastNLP import Batch\n",
+    "import torch\n",
+    "import time\n",
+    "\n",
+    "model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
+    "\n",
+    "def train(epoch, data):\n",
+    "    optim = torch.optim.Adam(model.parameters(), lr=0.001)\n",
+    "    lossfunc = torch.nn.CrossEntropyLoss()\n",
+    "    batch_size = 32\n",
+    "\n",
+    "    # 定义一个Batch，传入DataSet，规定batch_size和去batch的规则。\n",
+    "    # 顺序（Sequential），随机（Random），相似长度组成一个batch（Bucket）\n",
+    "    train_sampler = BucketSampler(batch_size=batch_size, seq_len_field_name='seq_len')\n",
+    "    train_batch = Batch(batch_size=batch_size, dataset=data, sampler=train_sampler)\n",
+    "    \n",
+    "    start_time = time.time()\n",
+    "    for i in range(epoch):\n",
+    "        loss_list = []\n",
+    "        for batch_x, batch_y in train_batch:\n",
+    "            optim.zero_grad()\n",
+    "            output = model(batch_x['words'])\n",
+    "            loss = lossfunc(output['pred'], batch_y['target'])\n",
+    "            loss.backward()\n",
+    "            optim.step()\n",
+    "            loss_list.append(loss.item())\n",
+    "        print('Epoch {:d} Avg Loss: {:.2f}'.format(i, sum(loss_list) / len(loss_list)),end=\" \")\n",
+    "        print('{:d}ms'.format(round((time.time()-start_time)*1000)))\n",
+    "        loss_list.clear()\n",
+    "            \n",
+    "train(10, train_data)\n",
+    "tester = Tester(test_data, model, metrics=AccuracyMetric())\n",
+    "tester.test()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 使用 Callback 实现自己想要的效果"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "input fields after batch(if batch size is 2):\n",
+      "\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 16]) \n",
+      "\tseq_len: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "target fields after batch(if batch size is 2):\n",
+      "\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
+      "\n",
+      "training epochs started 2019-05-12-21-38-40\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.285714\n",
+      "\n",
+      "Sum Time: 51ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.285714\n",
+      "\n",
+      "Sum Time: 69ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.285714\n",
+      "\n",
+      "Sum Time: 91ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Sum Time: 107ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Sum Time: 125ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Sum Time: 142ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Sum Time: 158ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.571429\n",
+      "\n",
+      "Sum Time: 176ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.714286\n",
+      "\n",
+      "Sum Time: 193ms\n",
+      "\n",
+      "\n",
+      "Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.857143\n",
+      "\n",
+      "Sum Time: 212ms\n",
+      "\n",
+      "\n",
+      "\n",
+      "In Epoch:10/Step:20, got best dev performance:AccuracyMetric: acc=0.857143\n",
+      "Reloaded the best model.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'best_eval': {'AccuracyMetric': {'acc': 0.857143}},\n",
+       " 'best_epoch': 10,\n",
+       " 'best_step': 20,\n",
+       " 'seconds': 0.2}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastNLP import Callback\n",
+    "\n",
+    "start_time = time.time()\n",
+    "\n",
+    "class MyCallback(Callback):\n",
+    "    def on_epoch_end(self):\n",
+    "        print('Sum Time: {:d}ms\\n\\n'.format(round((time.time()-start_time)*1000)))\n",
+    "        \n",
+    "\n",
+    "model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
+    "trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data,\n",
+    "                  loss=CrossEntropyLoss(), metrics=AccuracyMetric(), callbacks=[MyCallback()])\n",
+    "trainer.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.6.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}