From 8f211ef8ab6a84b8e734312d075628635e20d546 Mon Sep 17 00:00:00 2001
From: lxr-tech <1838593642@qq.com>
Date: Thu, 26 May 2022 21:57:44 +0800
Subject: [PATCH] add example-12 lxr 220526

---
 tutorials/fastnlp_tutorial_0.ipynb  |  13 +-
 tutorials/fastnlp_tutorial_e1.ipynb | 888 ++++++++++++++++++++++++++++
 tutorials/fastnlp_tutorial_e2.ipynb | 888 ++++++++++++++++++++++++++++
 3 files changed, 1784 insertions(+), 5 deletions(-)
 create mode 100644 tutorials/fastnlp_tutorial_e1.ipynb
 create mode 100644 tutorials/fastnlp_tutorial_e2.ipynb

diff --git a/tutorials/fastnlp_tutorial_0.ipynb b/tutorials/fastnlp_tutorial_0.ipynb
index 4e4ce55e..245eaf91 100644
--- a/tutorials/fastnlp_tutorial_0.ipynb
+++ b/tutorials/fastnlp_tutorial_0.ipynb
@@ -464,6 +464,9 @@
     }
    ],
    "source": [
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\n",
     "from fastNLP import Trainer\n",
     "\n",
     "trainer = Trainer(\n",
@@ -613,11 +616,11 @@
     {
      "data": {
       "text/html": [
-       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.41</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">41.0</span><span style=\"font-weight: bold\">}</span>\n",
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span><span style=\"color: #008000; text-decoration-color: #008000\">'acc#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.37</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'total#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">100.0</span>, <span style=\"color: #008000; text-decoration-color: #008000\">'correct#acc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">37.0</span><span style=\"font-weight: bold\">}</span>\n",
        "</pre>\n"
       ],
       "text/plain": [
-       "\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.41\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m41.0\u001b[0m\u001b[1m}\u001b[0m\n"
+       "\u001b[1m{\u001b[0m\u001b[32m'acc#acc'\u001b[0m: \u001b[1;36m0.37\u001b[0m, \u001b[32m'total#acc'\u001b[0m: \u001b[1;36m100.0\u001b[0m, \u001b[32m'correct#acc'\u001b[0m: \u001b[1;36m37.0\u001b[0m\u001b[1m}\u001b[0m\n"
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@@ -626,7 +629,7 @@
     {
      "data": {
       "text/plain": [
-       "{'acc#acc': 0.41, 'total#acc': 100.0, 'correct#acc': 41.0}"
+       "{'acc#acc': 0.37, 'total#acc': 100.0, 'correct#acc': 37.0}"
       ]
      },
      "execution_count": 9,
@@ -756,7 +759,7 @@
     {
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       "text/plain": [
-       "{'acc#acc': 0.46, 'total#acc': 100.0, 'correct#acc': 46.0}"
+       "{'acc#acc': 0.47, 'total#acc': 100.0, 'correct#acc': 47.0}"
       ]
      },
      "execution_count": 12,
@@ -793,7 +796,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.13"
   },
   "pycharm": {
    "stem_cell": {
diff --git a/tutorials/fastnlp_tutorial_e1.ipynb b/tutorials/fastnlp_tutorial_e1.ipynb
new file mode 100644
index 00000000..92a49925
--- /dev/null
+++ b/tutorials/fastnlp_tutorial_e1.ipynb
@@ -0,0 +1,888 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# E1. 使用 DistilBert 完成 SST2 分类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.18.0\n"
+     ]
+    }
+   ],
+   "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 transformers\n",
+    "from transformers import AutoTokenizer\n",
+    "from transformers import AutoModelForSequenceClassification\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\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__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n",
+    "\n",
+    "task = \"sst2\"\n",
+    "model_checkpoint = \"distilbert-base-uncased\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n",
+      "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "253d79d7a67e4dc88338448b5bcb3fb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "\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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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"
+     ]
+    }
+   ],
+   "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",
+    "\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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TestDistilBertDataset(Dataset):\n",
+    "    def __init__(self, dataset):\n",
+    "        super(TestDistilBertDataset, self).__init__()\n",
+    "        self.dataset = dataset\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "\n",
+    "    def __getitem__(self, item):\n",
+    "        item = self.dataset[item]\n",
+    "        return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_bert_collate_fn(batch):\n",
+    "    input_ids, atten_mask, labels = [], [], []\n",
+    "    max_length = [0] * 3\n",
+    "    for each_item in batch:\n",
+    "        input_ids.append(each_item[0])\n",
+    "        max_length[0] = max(max_length[0], len(each_item[0]))\n",
+    "        atten_mask.append(each_item[1])\n",
+    "        max_length[1] = max(max_length[1], len(each_item[1]))\n",
+    "        labels.append(each_item[2])\n",
+    "        max_length[2] = max(max_length[2], len(each_item[2]))\n",
+    "\n",
+    "    for i in range(3):\n",
+    "        each = (input_ids, atten_mask, labels)[i]\n",
+    "        for item in each:\n",
+    "            item.extend([0] * (max_length[i] - len(item)))\n",
+    "    return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
+    "            \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
+    "            \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=0)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n",
+    "dataloader_train = DataLoader(dataset=dataset_train, \n",
+    "                              batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n",
+    "dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n",
+    "dataloader_valid = DataLoader(dataset=dataset_valid, \n",
+    "                              batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    driver='torch',\n",
+    "    device='cuda',\n",
+    "    n_epochs=10,\n",
+    "    optimizers=optimizers,\n",
+    "    train_dataloader=dataloader_train,\n",
+    "    evaluate_dataloaders=dataloader_valid,\n",
+    "    metrics={'acc': Accuracy()}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# help(model.back_bone.forward)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[21:00:11] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Running evaluator sanity check for <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> batches.              <a href=\"file://../fastNLP/core/controllers/trainer.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">trainer.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://../fastNLP/core/controllers/trainer.py#592\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">592</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[21:00:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches.              \u001b]8;id=22992;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=669026;file://../fastNLP/core/controllers/trainer.py#592\u001b\\\u001b[2m592\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.871875</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\">279.0</span>\n",
+       "<span style=\"font-weight: bold\">}</span>\n",
+       "</pre>\n"
+      ],
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.878125</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.871875</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">4</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.903125</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">5</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.871875</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
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+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.875</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
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+    },
+    {
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.8875</span>,\n",
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+       "  <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\">284.0</span>\n",
+       "<span style=\"font-weight: bold\">}</span>\n",
+       "</pre>\n"
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
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+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.8875</span>,\n",
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+       "<span style=\"font-weight: bold\">}</span>\n",
+       "</pre>\n"
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+       "\u001b[1m{\u001b[0m\n",
+       "  \u001b[1;34m\"acc#acc\"\u001b[0m: \u001b[1;36m0.8875\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;36m284.0\u001b[0m\n",
+       "\u001b[1m}\u001b[0m\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">---------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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"
+      ],
+      "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/fastnlp_tutorial_e2.ipynb b/tutorials/fastnlp_tutorial_e2.ipynb
new file mode 100644
index 00000000..8e734f01
--- /dev/null
+++ b/tutorials/fastnlp_tutorial_e2.ipynb
@@ -0,0 +1,888 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# E2. 使用 PrefixTuning 完成 SST2 分类"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.18.0\n"
+     ]
+    }
+   ],
+   "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 transformers\n",
+    "from transformers import AutoTokenizer\n",
+    "from transformers import AutoModelForSequenceClassification\n",
+    "\n",
+    "import sys\n",
+    "sys.path.append('..')\n",
+    "\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__)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n",
+    "\n",
+    "task = \"sst2\"\n",
+    "model_checkpoint = \"distilbert-base-uncased\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using the latest cached version of the module from /remote-home/xrliu/.cache/huggingface/modules/datasets_modules/datasets/glue/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad (last modified on Thu May 26 15:30:15 2022) since it couldn't be found locally at glue., or remotely on the Hugging Face Hub.\n",
+      "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "253d79d7a67e4dc88338448b5bcb3fb9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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",
+    "\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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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"
+     ]
+    }
+   ],
+   "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",
+    "\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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class TestDistilBertDataset(Dataset):\n",
+    "    def __init__(self, dataset):\n",
+    "        super(TestDistilBertDataset, self).__init__()\n",
+    "        self.dataset = dataset\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.dataset)\n",
+    "\n",
+    "    def __getitem__(self, item):\n",
+    "        item = self.dataset[item]\n",
+    "        return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_bert_collate_fn(batch):\n",
+    "    input_ids, atten_mask, labels = [], [], []\n",
+    "    max_length = [0] * 3\n",
+    "    for each_item in batch:\n",
+    "        input_ids.append(each_item[0])\n",
+    "        max_length[0] = max(max_length[0], len(each_item[0]))\n",
+    "        atten_mask.append(each_item[1])\n",
+    "        max_length[1] = max(max_length[1], len(each_item[1]))\n",
+    "        labels.append(each_item[2])\n",
+    "        max_length[2] = max(max_length[2], len(each_item[2]))\n",
+    "\n",
+    "    for i in range(3):\n",
+    "        each = (input_ids, atten_mask, labels)[i]\n",
+    "        for item in each:\n",
+    "            item.extend([0] * (max_length[i] - len(item)))\n",
+    "    return {\"input_ids\": torch.cat([torch.tensor([item]) for item in input_ids], dim=0),\n",
+    "            \"attention_mask\": torch.cat([torch.tensor([item]) for item in atten_mask], dim=0),\n",
+    "            \"labels\": torch.cat([torch.tensor(item) for item in labels], dim=0)}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n",
+    "dataloader_train = DataLoader(dataset=dataset_train, \n",
+    "                              batch_size=32, shuffle=True, collate_fn=test_bert_collate_fn)\n",
+    "dataset_valid = TestDistilBertDataset(encoded_dataset[\"validation\"])\n",
+    "dataloader_valid = DataLoader(dataset=dataset_valid, \n",
+    "                              batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "trainer = Trainer(\n",
+    "    model=model,\n",
+    "    driver='torch',\n",
+    "    device='cuda',\n",
+    "    n_epochs=10,\n",
+    "    optimizers=optimizers,\n",
+    "    train_dataloader=dataloader_train,\n",
+    "    evaluate_dataloaders=dataloader_valid,\n",
+    "    metrics={'acc': Accuracy()}\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# help(model.back_bone.forward)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">[21:00:11] </span><span style=\"color: #000080; text-decoration-color: #000080\">INFO    </span> Running evaluator sanity check for <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span> batches.              <a href=\"file://../fastNLP/core/controllers/trainer.py\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">trainer.py</span></a><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">:</span><a href=\"file://../fastNLP/core/controllers/trainer.py#592\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">592</span></a>\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "\u001b[2;36m[21:00:11]\u001b[0m\u001b[2;36m \u001b[0m\u001b[34mINFO    \u001b[0m Running evaluator sanity check for \u001b[1;36m2\u001b[0m batches.              \u001b]8;id=22992;file://../fastNLP/core/controllers/trainer.py\u001b\\\u001b[2mtrainer.py\u001b[0m\u001b]8;;\u001b\\\u001b[2m:\u001b[0m\u001b]8;id=669026;file://../fastNLP/core/controllers/trainer.py#592\u001b\\\u001b[2m592\u001b[0m\u001b]8;;\u001b\\\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Output()"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<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"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m1\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">2</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
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+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m2\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+       "</pre>\n"
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+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m3\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+       "</pre>\n"
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+       "----------------------------- Eval. results on Epoch:\u001b[1;36m4\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m5\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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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\">0.871875</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">6</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m6\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">7</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m7\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.875</span>,\n",
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">----------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">8</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m8\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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\">0.8875</span>,\n",
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+       "</pre>\n"
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+      "text/plain": [
+       "----------------------------- Eval. results on Epoch:\u001b[1;36m9\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
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+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">---------------------------- Eval. results on Epoch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span>, Batch:<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span> -----------------------------\n",
+       "</pre>\n"
+      ],
+      "text/plain": [
+       "---------------------------- Eval. results on Epoch:\u001b[1;36m10\u001b[0m, Batch:\u001b[1;36m0\u001b[0m -----------------------------\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
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+    {
+     "data": {
+      "text/html": [
+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">{</span>\n",
+       "  <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",
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+       "\u001b[1m}\u001b[0m\n"
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
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+       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
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+       "\n"
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+     "metadata": {},
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+   ],
+   "source": [
+    "trainer.run(num_eval_batch_per_dl=10)"
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+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
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+ "nbformat_minor": 1
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