diff --git a/docs/source/user/task1.rst b/docs/source/user/task1.rst
new file mode 100644
index 00000000..0c346999
--- /dev/null
+++ b/docs/source/user/task1.rst
@@ -0,0 +1,3 @@
+=====================
+用 fastNLP 分类
+=====================
\ No newline at end of file
diff --git a/tutorials/fastnlp_10min_tutorial.ipynb b/tutorials/fastnlp_10min_tutorial.ipynb
index 534c4e49..526fd49f 100644
--- a/tutorials/fastnlp_10min_tutorial.ipynb
+++ b/tutorials/fastnlp_10min_tutorial.ipynb
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -97,7 +97,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -107,7 +107,7 @@
        "'label': 0 type=str}"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -221,7 +221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -263,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -295,17 +295,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "{'raw_sentence': a welcome relief from baseball movies that try too hard to be mythic , this one is a sweet and modest and ultimately winning story . type=str,\n",
-      "'label': 3 type=int,\n",
-      "'words': [4, 1, 1, 18, 1, 1, 13, 1, 1, 1, 8, 26, 1, 5, 35, 1, 11, 4, 1, 10, 1, 10, 1, 1, 1, 2] type=list,\n",
-      "'seq_len': 26 type=int}\n"
+      "{'raw_sentence': the performances are an absolute joy . type=str,\n",
+      "'label': 4 type=int,\n",
+      "'words': [3, 1, 1, 26, 1, 1, 2] type=list,\n",
+      "'seq_len': 7 type=int}\n"
      ]
     }
    ],
@@ -327,9 +327,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "batch_x has:  {'words': tensor([[ 15,  72,  15,  73,  74,   7,   3,  75,   6,   3,  16,  16,\n",
+      "          76,   2],\n",
+      "        [ 15,  72,  15,  73,  74,   7,   3,  75,   6,   3,  16,  16,\n",
+      "          76,   2]])}\n",
+      "batch_y has:  {'label': tensor([ 1,  1])}\n"
+     ]
+    }
+   ],
    "source": [
     "# 如果你们需要做强化学习或者GAN之类的项目，你们也可以使用这些数据预处理的工具\n",
     "from fastNLP.core.batch import Batch\n",
@@ -352,7 +364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +372,7 @@
       "text/plain": [
        "CNNText(\n",
        "  (embed): Embedding(\n",
-       "    (embed): Embedding(59, 50, padding_idx=0)\n",
+       "    77, 50\n",
        "    (dropout): Dropout(p=0.0)\n",
        "  )\n",
        "  (conv_pool): ConvMaxpool(\n",
@@ -377,14 +389,14 @@
        ")"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "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",
+    "model = CNNText((len(vocab), 50), num_classes=5, padding=2, dropout=0.1)\n",
     "model"
    ]
   },
@@ -448,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -485,7 +497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -517,7 +529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -525,48 +537,25 @@
      "output_type": "stream",
      "text": [
       "input fields after batch(if batch size is 2):\n",
-      "\tword_seq: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \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",
-      "training epochs started 2019-01-12 17-07-51\n"
+      "\n"
      ]
     },
     {
-     "data": {
-      "text/plain": [
-       "HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=10), HTML(value='')), layout=Layout(display='…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluation at Epoch 1/5. Step:2/10. AccuracyMetric: acc=0.425926\n",
-      "Evaluation at Epoch 2/5. Step:4/10. AccuracyMetric: acc=0.425926\n",
-      "Evaluation at Epoch 3/5. Step:6/10. AccuracyMetric: acc=0.611111\n",
-      "Evaluation at Epoch 4/5. Step:8/10. AccuracyMetric: acc=0.648148\n",
-      "Evaluation at Epoch 5/5. Step:10/10. AccuracyMetric: acc=0.703704\n",
-      "\n",
-      "In Epoch:5/Step:10, got best dev performance:AccuracyMetric: acc=0.703704\n",
-      "Reloaded the best model.\n"
+     "ename": "NameError",
+     "evalue": "\nProblems occurred when calling CNNText.forward(self, words, seq_len=None)\n\tmissing param: ['words']\n\tunused field: ['word_seq']\n\tSuggestion: You need to provide ['words'] in DataSet and set it as input. ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-19-ff7d68caf88a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m                           \u001b[0msave_path\u001b[0m\u001b[0;34m=\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      8\u001b[0m                           \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                           n_epochs=5)\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0moverfit_trainer\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    808\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minfo_str\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m             _check_forward_error(forward_func=model.forward, dataset=dataset,\n\u001b[0;32m--> 810\u001b[0;31m                                  batch_x=batch_x, check_level=check_level)\n\u001b[0m\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/Users/fdujyn/anaconda3/lib/python3.6/site-packages/fastNLP/core/utils.py\u001b[0m in \u001b[0;36m_check_forward_error\u001b[0;34m(forward_func, batch_x, dataset, check_level)\u001b[0m\n\u001b[1;32m    594\u001b[0m             \u001b[0msugg_str\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0msuggestions\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[1;32m    595\u001b[0m         \u001b[0merr_str\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'\\n'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'\\n\\tSuggestion: '\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0msugg_str\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNameError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr_str\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    597\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0m_unused\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_level\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mWARNING_CHECK_LEVEL\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: \nProblems occurred when calling CNNText.forward(self, words, seq_len=None)\n\tmissing param: ['words']\n\tunused field: ['word_seq']\n\tSuggestion: You need to provide ['words'] in DataSet and set it as input. "
      ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'best_eval': {'AccuracyMetric': {'acc': 0.703704}},\n",
-       " 'best_epoch': 5,\n",
-       " 'best_step': 10,\n",
-       " 'seconds': 0.62}"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
diff --git a/tutorials/fastnlp_1min_tutorial.ipynb b/tutorials/fastnlp_1min_tutorial.ipynb
index 7a35d992..64d57bc4 100644
--- a/tutorials/fastnlp_1min_tutorial.ipynb
+++ b/tutorials/fastnlp_1min_tutorial.ipynb
@@ -21,22 +21,10 @@
    "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"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "import sys\n",
-    "sys.path.append(\"../\")\n",
-    "\n",
     "from fastNLP import DataSet\n",
-    "\n",
+    "           \n",
     "data_path = \"./sample_data/tutorial_sample_dataset.csv\"\n",
     "ds = DataSet.read_csv(data_path, headers=('raw_sentence', 'label'), sep='\\t')"
    ]
@@ -77,7 +65,1370 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "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",
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+       "  '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",
+       " ['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",
+       " ['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",
+       "  '.']]"
+      ]
+     },
+     "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",
@@ -114,7 +1465,192 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[120, 121, 6, 2, 122, 5, 72, 123, 3],\n",
+       " [14,\n",
+       "  4,\n",
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+       "  30,\n",
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+       "  164,\n",
+       "  165,\n",
+       "  3],\n",
+       " [4,\n",
+       "  112,\n",
+       "  113,\n",
+       "  15,\n",
+       "  114,\n",
+       "  35,\n",
+       "  10,\n",
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+       "  5,\n",
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+       "  11,\n",
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+       "  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",
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+       "  95,\n",
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+       "  2,\n",
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+       "  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",
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+       "  117,\n",
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+       "  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",
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+       "  1,\n",
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+       "  1,\n",
+       "  1,\n",
+       "  1,\n",
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+       "  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",
@@ -140,7 +1676,7 @@
    "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"
+    "model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n"
    ]
   },
   {
@@ -162,38 +1698,29 @@
      "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",
+      "\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",
-      "training epochs started 2019-01-12 17-00-48\n"
+      "\n"
      ]
     },
     {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "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"
+     "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'"
      ]
     }
    ],
diff --git a/tutorials/fastnlp_test_tutorial.ipynb b/tutorials/fastnlp_test_tutorial.ipynb
index 9b0c1b2e..fb87606e 100644
--- a/tutorials/fastnlp_test_tutorial.ipynb
+++ b/tutorials/fastnlp_test_tutorial.ipynb
@@ -89,7 +89,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,