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   "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": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\zyfeng\\miniconda3\\envs\\fastnlp\\lib\\site-packages\\tqdm\\autonotebook\\__init__.py:14: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
      "  \" (e.g. in jupyter console)\", TqdmExperimentalWarning)\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "\n",
    "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": [],
   "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": [],
   "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(embed_num=len(vocab), embed_dim=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, 25]) \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",
      "training epochs started 2019-01-12 17-00-48\n"
     ]
    },
    {
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       "model_id": "23979df0f63e446fbb0406b919b91dd3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=6), HTML(value='')), layout=Layout(display='i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation at Epoch 1/3. Step:2/6. AccuracyMetric: acc=0.173913\n",
      "Evaluation at Epoch 2/3. Step:4/6. AccuracyMetric: acc=0.26087\n",
      "Evaluation at Epoch 3/3. Step:6/6. AccuracyMetric: acc=0.304348\n",
      "\n",
      "In Epoch:3/Step:6, got best dev performance:AccuracyMetric: acc=0.304348\n",
      "Reloaded the best model.\n",
      "Train finished!\n"
     ]
    }
   ],
   "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": []
  }
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