diff --git a/tutorials/fastnlp_tutorial_e1.ipynb b/tutorials/fastnlp_tutorial_e1.ipynb
index 92a49925..628dd7ae 100644
--- a/tutorials/fastnlp_tutorial_e1.ipynb
+++ b/tutorials/fastnlp_tutorial_e1.ipynb
@@ -233,7 +233,7 @@
     }
    ],
    "source": [
-    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
+    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
     "\n",
     "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
     "\n",
@@ -881,6 +881,15 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.13"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
   }
  },
  "nbformat": 4,
diff --git a/tutorials/fastnlp_tutorial_e2.ipynb b/tutorials/fastnlp_tutorial_e2.ipynb
index 8e734f01..1d7746be 100644
--- a/tutorials/fastnlp_tutorial_e2.ipynb
+++ b/tutorials/fastnlp_tutorial_e2.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# E2. 使用 PrefixTuning 完成 SST2 分类"
+    "# E2. 使用 continuous prompt 完成 SST2 分类"
    ]
   },
   {
@@ -35,10 +35,11 @@
    ],
    "source": [
     "import torch\n",
-    "import torch.nn as nn\n",
     "from torch.optim import AdamW\n",
     "from torch.utils.data import DataLoader, Dataset\n",
     "\n",
+    "import torch.nn as nn\n",
+    "\n",
     "import transformers\n",
     "from transformers import AutoTokenizer\n",
     "from transformers import AutoModelForSequenceClassification\n",
@@ -48,7 +49,6 @@
     "\n",
     "import fastNLP\n",
     "from fastNLP import Trainer\n",
-    "from fastNLP.core.utils.utils import dataclass_to_dict\n",
     "from fastNLP.core.metrics import Accuracy\n",
     "\n",
     "print(transformers.__version__)"
@@ -69,6 +69,82 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ClassModel(nn.Module):\n",
+    "    def __init__(self, model_checkpoint, num_labels, pre_seq_len):\n",
+    "        nn.Module.__init__(self)\n",
+    "        self.num_labels = num_labels\n",
+    "        self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
+    "                                                                            num_labels=num_labels)\n",
+    "        self.embeddings = self.back_bone.get_input_embeddings()\n",
+    "\n",
+    "        for param in self.back_bone.parameters():\n",
+    "            param.requires_grad = False\n",
+    "        \n",
+    "        self.pre_seq_len = pre_seq_len\n",
+    "        self.prefix_tokens = torch.arange(self.pre_seq_len).long()\n",
+    "        self.prefix_encoder = nn.Embedding(self.pre_seq_len, self.embeddings.embedding_dim)\n",
+    "    \n",
+    "    def get_prompt(self, batch_size):\n",
+    "        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(self.back_bone.device)\n",
+    "        prompts = self.prefix_encoder(prefix_tokens)\n",
+    "        return prompts\n",
+    "\n",
+    "    def forward(self, input_ids, attention_mask, labels):\n",
+    "        \n",
+    "        batch_size = input_ids.shape[0]\n",
+    "        raw_embedding = self.embeddings(input_ids)\n",
+    "        \n",
+    "        prompts = self.get_prompt(batch_size=batch_size)\n",
+    "        inputs_embeds = torch.cat((prompts, raw_embedding), dim=1)\n",
+    "        prefix_attention_mask = torch.ones(batch_size, self.pre_seq_len).to(self.back_bone.device)\n",
+    "        attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=1)\n",
+    "\n",
+    "        outputs = self.back_bone(inputs_embeds=inputs_embeds, \n",
+    "                                 attention_mask=attention_mask, labels=labels)\n",
+    "        return outputs\n",
+    "\n",
+    "    def train_step(self, input_ids, attention_mask, labels):\n",
+    "        return {\"loss\": self(input_ids, attention_mask, labels).loss}\n",
+    "\n",
+    "    def evaluate_step(self, input_ids, attention_mask, labels):\n",
+    "        pred = self(input_ids, attention_mask, labels).logits\n",
+    "        pred = torch.max(pred, dim=-1)[1]\n",
+    "        return {\"pred\": pred, \"target\": labels}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_layer_norm.weight']\n",
+      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight', 'classifier.bias']\n",
+      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task == \"stsb\" else 2\n",
+    "\n",
+    "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint, pre_seq_len=16)\n",
+    "\n",
+    "# Generally, simple classification tasks prefer shorter prompts (less than 20)\n",
+    "\n",
+    "optimizers = AdamW(params=model.parameters(), lr=5e-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -84,7 +160,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "253d79d7a67e4dc88338448b5bcb3fb9",
+       "model_id": "1b73650d43f245ac8a5501dc91c6fe8c",
        "version_major": 2,
        "version_minor": 0
       },
@@ -99,48 +175,9 @@
    "source": [
     "from datasets import load_dataset, load_metric\n",
     "\n",
-    "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'input_ids': [101, 7592, 1010, 2023, 2028, 6251, 999, 102, 1998, 2023, 6251, 3632, 2007, 2009, 1012, 102], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n",
+    "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)\n",
     "\n",
-    "print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "task_to_keys = {\n",
-    "    \"cola\": (\"sentence\", None),\n",
-    "    \"mnli\": (\"premise\", \"hypothesis\"),\n",
-    "    \"mnli-mm\": (\"premise\", \"hypothesis\"),\n",
-    "    \"mrpc\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"qnli\": (\"question\", \"sentence\"),\n",
-    "    \"qqp\": (\"question1\", \"question2\"),\n",
-    "    \"rte\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"sst2\": (\"sentence\", None),\n",
-    "    \"stsb\": (\"sentence1\", \"sentence2\"),\n",
-    "    \"wnli\": (\"sentence1\", \"sentence2\"),\n",
-    "}\n",
-    "\n",
-    "sentence1_key, sentence2_key = task_to_keys[task]"
+    "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
    ]
   },
   {
@@ -149,100 +186,38 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Sentence: hide new secretions from the parental units \n"
-     ]
-    }
-   ],
-   "source": [
-    "if sentence2_key is None:\n",
-    "    print(f\"Sentence: {dataset['train'][0][sentence1_key]}\")\n",
-    "else:\n",
-    "    print(f\"Sentence 1: {dataset['train'][0][sentence1_key]}\")\n",
-    "    print(f\"Sentence 2: {dataset['train'][0][sentence2_key]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ca1fbe5e8eb059f3.arrow\n",
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-03661263fbf302f5.arrow\n",
-      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-fbe8e7a4e4f18f45.arrow\n"
+      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-294e481a713c5754.arrow\n",
+      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-ed9d9258aaf0fb54.arrow\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0be84915c90f460896b8e67299e09df4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/2 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
     "def preprocess_function(examples):\n",
-    "    if sentence2_key is None:\n",
-    "        return tokenizer(examples[sentence1_key], truncation=True)\n",
-    "    return tokenizer(examples[sentence1_key], examples[sentence2_key], truncation=True)\n",
+    "    return tokenizer(examples['sentence'], truncation=True)\n",
     "\n",
     "encoded_dataset = dataset.map(preprocess_function, batched=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class ClassModel(nn.Module):\n",
-    "    def __init__(self, num_labels, model_checkpoint):\n",
-    "        nn.Module.__init__(self)\n",
-    "        self.num_labels = num_labels\n",
-    "        self.back_bone = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, \n",
-    "                                                                            num_labels=num_labels)\n",
-    "        self.loss_fn = nn.CrossEntropyLoss()\n",
-    "\n",
-    "    def forward(self, input_ids, attention_mask):\n",
-    "        return self.back_bone(input_ids, attention_mask)\n",
-    "\n",
-    "    def train_step(self, input_ids, attention_mask, labels):\n",
-    "        pred = self(input_ids, attention_mask).logits\n",
-    "        return {\"loss\": self.loss_fn(pred, labels)}\n",
-    "\n",
-    "    def evaluate_step(self, input_ids, attention_mask, labels):\n",
-    "        pred = self(input_ids, attention_mask).logits\n",
-    "        pred = torch.max(pred, dim=-1)[1]\n",
-    "        return {\"pred\": pred, \"target\": labels}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertForSequenceClassification: ['vocab_projector.weight', 'vocab_layer_norm.bias', 'vocab_transform.bias', 'vocab_projector.bias', 'vocab_layer_norm.weight', 'vocab_transform.weight']\n",
-      "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
-      "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
-      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['pre_classifier.weight', 'classifier.weight', 'classifier.bias', 'pre_classifier.bias']\n",
-      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_labels = 3 if task.startswith(\"mnli\") else 1 if task==\"stsb\" else 2\n",
-    "\n",
-    "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n",
-    "\n",
-    "optimizers = AdamW(params=model.parameters(), lr=5e-5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -261,7 +236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -287,7 +262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -301,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -319,443 +294,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# help(model.back_bone.forward)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
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+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)"
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+  },
+  {
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+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
     {
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       "text/html": [
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-       "  <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"acc#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.890625</span>,\n",
-       "  <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"total#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">320.0</span>,\n",
-       "  <span style=\"color: #000080; text-decoration-color: #000080; font-weight: bold\">\"correct#acc\"</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">285.0</span>\n",
-       "<span style=\"font-weight: bold\">}</span>\n",
-       "</pre>\n"
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
       ],
-      "text/plain": [
-       "\u001b[1m{\u001b[0m\n",
-       "  \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.890625\u001b[0m,\n",
-       "  \u001b[1;34m\"total#acc\"\u001b[0m: \u001b[1;36m320.0\u001b[0m,\n",
-       "  \u001b[1;34m\"correct#acc\"\u001b[0m: \u001b[1;36m285.0\u001b[0m\n",
-       "\u001b[1m}\u001b[0m\n"
-      ]
+      "text/plain": []
      },
      "metadata": {},
      "output_type": "display_data"
@@ -840,20 +362,17 @@
     },
     {
      "data": {
-      "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
-       "</pre>\n"
-      ],
       "text/plain": [
-       "\n"
+       "{'acc#acc': 0.644495, 'total#acc': 872.0, 'correct#acc': 562.0}"
       ]
      },
+     "execution_count": 20,
      "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "trainer.run(num_eval_batch_per_dl=10)"
+    "trainer.evaluator.run()"
    ]
   },
   {
@@ -881,6 +400,15 @@
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.7.13"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
   }
  },
  "nbformat": 4,