From c62f5bd2dc283b24b1d4377392c7ad0551bd461a Mon Sep 17 00:00:00 2001 From: lxr-tech <1838593642@qq.com> Date: Mon, 30 May 2022 22:48:28 +0800 Subject: [PATCH] update example-12 lxr 220530 --- tutorials/fastnlp_tutorial_1.ipynb | 2 +- tutorials/fastnlp_tutorial_3.ipynb | 2 +- tutorials/fastnlp_tutorial_e1.ipynb | 990 +++++++++++-------------- tutorials/fastnlp_tutorial_e2.ipynb | 300 +++++--- tutorials/figures/E1-fig-glue-benchmark.png | Bin 0 -> 158817 bytes tutorials/figures/E2-fig-p-tuning-v2-model.png | Bin 0 -> 50517 bytes 6 files changed, 599 insertions(+), 695 deletions(-) create mode 100644 tutorials/figures/E1-fig-glue-benchmark.png create mode 100644 tutorials/figures/E2-fig-p-tuning-v2-model.png diff --git a/tutorials/fastnlp_tutorial_1.ipynb b/tutorials/fastnlp_tutorial_1.ipynb index 09e8821d..db77e6c3 100644 --- a/tutorials/fastnlp_tutorial_1.ipynb +++ b/tutorials/fastnlp_tutorial_1.ipynb @@ -1325,7 +1325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.7.13" } }, "nbformat": 4, diff --git a/tutorials/fastnlp_tutorial_3.ipynb b/tutorials/fastnlp_tutorial_3.ipynb index 8c3c935e..353e4645 100644 --- a/tutorials/fastnlp_tutorial_3.ipynb +++ b/tutorials/fastnlp_tutorial_3.ipynb @@ -288,7 +288,7 @@ "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 index 628dd7ae..6ec04cb4 100644 --- a/tutorials/fastnlp_tutorial_e1.ipynb +++ b/tutorials/fastnlp_tutorial_e1.ipynb @@ -4,7 +4,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# E1. 使用 DistilBert 完成 SST2 分类" + "  从这篇开始，我们将开启**`fastNLP v0.8 tutorial`的`example`系列**，在接下来的\n", + "\n", + "  每篇`tutorial`里，我们将会介绍`fastNLP v0.8`在一些自然语言处理任务上的应用" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# E1. 使用 Bert + fine-tuning 完成 SST2 分类\n", + "\n", + "  1   基础介绍：`GLUE`通用语言理解评估、`SST2`文本情感二分类数据集 \n", + "\n", + "  2   准备工作：加载`tokenizer`、预处理`dataset`、`dataloader`使用\n", + "\n", + "  3   模型训练：加载`distilbert-base`、`fastNLP`参数匹配、`fine-tuning`" ] }, { @@ -48,22 +63,64 @@ "\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": "markdown", + "metadata": {}, + "source": [ + "### 1. 基础介绍：GLUE 通用语言理解评估、SST2 文本情感二分类数据集\n", + "\n", + "  本示例使用`GLUE`评估基准中的`SST2`数据集，通过`fine-tuning`方式\n", + "\n", + "    调整`distilbert-bert`分类模型，以下首先简单介绍下`GLUE`和`SST2`\n", + "\n", + "**`GLUE`**，**全称`General Language Understanding Evaluation`**，**通用语言理解评估**，\n", + "\n", + "  包含9个数据集，各语料的语言均为英语，涉及多个自然语言理解`NLU`任务，包括\n", + "\n", + "    **`CoLA`**，文本分类任务，预测单句语法正误分类；**`SST2`**，文本分类任务，预测单句情感二分类\n", + "\n", + "    **`MRPC`**，句对分类任务，预测句对语义一致性；**`STSB`**，相似度打分任务，预测句对语义相似度回归\n", + "\n", + "    **`QQP`**，句对分类任务，预测问题对语义一致性；**`MNLI`**，文本推理任务，预测句对蕴含/矛盾/中立预测\n", + "\n", + "    **`QNLI`/`RTE`/`WNLI`**，文本推理，预测是否蕴含二分类（其中，`QNLI`从`SQuAD`转化而来\n", + "\n", + "  诸如`BERT`、`T5`等经典模型都会在此基准上验证效果，更多参考[GLUE论文](https://arxiv.org/pdf/1804.07461v3.pdf)\n", + "\n", + "    此处，我们使用`SST2`来训练`bert`，实现文本分类，其他任务描述见下图" + ] + }, + { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n", + "GLUE_TASKS = ['cola', 'mnli', 'mrpc', 'qnli', 'qqp', 'rte', 'sst2', 'stsb', 'wnli']\n", + "\n", + "task = 'sst2'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "**`SST`**，**全称`Stanford Sentiment Treebank`**，**斯坦福情感树库**，**单句情感分类**数据集\n", + "\n", + "  包含电影评论语句和对应的情感极性，1 对应`positive` 正面情感，0 对应`negative` 负面情感\n", + "\n", + "  数据集包括三部分：训练集 67350 条，开发集 873 条，测试集 1821 条，更多参考[下载链接](https://gluebenchmark.com/tasks)\n", "\n", - "task = \"sst2\"\n", - "model_checkpoint = \"distilbert-base-uncased\"" + "对应到代码上，此处使用`datasets`模块中的`load_dataset`函数，指定`SST2`数据集，自动加载\n", + "\n", + "  首次下载后会保存至`~/.cache/huggingface/modules/datasets_modules/datasets/glue/`目录下" ] }, { @@ -84,7 +141,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "253d79d7a67e4dc88338448b5bcb3fb9", + "model_id": "adc9449171454f658285f220b70126e1", "version_major": 2, "version_minor": 0 }, @@ -97,9 +154,16 @@ } ], "source": [ - "from datasets import load_dataset, load_metric\n", + "from datasets import load_dataset\n", "\n", - "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)" + "dataset = load_dataset('glue', task)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "  加载之后，根据`GLUE`中`SST2`数据集的格式，尝试打印部分数据，检查加载结果" ] }, { @@ -111,62 +175,89 @@ "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" + "Sentence: hide new secretions from the parental units \n" ] } ], "source": [ - "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)\n", + "task_to_keys = {\n", + " 'cola': ('sentence', None),\n", + " 'mnli': ('premise', 'hypothesis'),\n", + " 'mnli': ('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", - "print(tokenizer(\"Hello, this one sentence!\", \"And this sentence goes with it.\"))" + "sentence1_key, sentence2_key = task_to_keys[task]\n", + "\n", + "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": 5, + "cell_type": "markdown", "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", + "### 2. 准备工作：加载 tokenizer、预处理 dataset、dataloader 使用\n", + "\n", + "  接下来进入模型训练的准备工作，分别需要使用`tokenizer`模块对数据集进行分词与标注\n", + "\n", + "    定义`SeqClsDataset`对应`dataloader`模块用来实现数据集在训练/测试时的加载\n", + "\n", + "此处的`tokenizer`和`SequenceClassificationModel`都是基于**`distilbert-base-uncased`模型**\n", "\n", - "sentence1_key, sentence2_key = task_to_keys[task]" + "  即使用较小的、不区分大小写的数据集，**对`bert-base`进行知识蒸馏后的版本**，结构上\n", + "\n", + "  模型包含1个编码层、6个自注意力层，详解见本篇末尾，更多细节请参考[DistilBert论文](https://arxiv.org/pdf/1910.01108.pdf)\n", + "\n", + "首先，通过从`transformers`库中导入`AutoTokenizer`模块，使用`from_pretrained`函数初始化\n", + "\n", + "  此处的`use_fast`表示是否使用`tokenizer`的快速版本；尝试序列化示例数据，检查加载结果\n", + "\n", + "  需要注意的是，处理后返回的两个键值，`'input_ids'`表示原始文本对应的词素编号序列\n", + "\n", + "    `'attention_mask'`表示自注意力运算时的掩模（标上`0`的部分对应`padding`的内容" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Sentence: hide new secretions from the parental units \n" + "{'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": [ - "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]}\")" + "model_checkpoint = 'distilbert-base-uncased'\n", + "\n", + "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": "markdown", + "metadata": {}, + "source": [ + "接着，定义预处理函数，**通过`dataset.map`方法**，**将数据集中的文本**，**替换为词素编号序列**" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -189,66 +280,27 @@ ] }, { - "cell_type": "code", - "execution_count": 8, + "cell_type": "markdown", "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", + "然后，通过继承`torch`中的`Dataset`类，定义`SeqClsDataset`类，需要注意的是\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", + "  其中，**`__getitem__`函数各返回值引用的键值**，**必须和原始数据集中的属性对应**\n", "\n", - "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n", + "  例如，`'label'`是`SST2`数据集中原有的内容（包括`'sentence'`和`'label'`\n", "\n", - "optimizers = AdamW(params=model.parameters(), lr=5e-5)" + "    `'input_ids'`和`'attention_mask'`则是`tokenizer`处理后添加的字段" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ - "class TestDistilBertDataset(Dataset):\n", + "class SeqClsDataset(Dataset):\n", " def __init__(self, dataset):\n", - " super(TestDistilBertDataset, self).__init__()\n", + " Dataset.__init__(self)\n", " self.dataset = dataset\n", "\n", " def __len__(self):\n", @@ -256,16 +308,27 @@ "\n", " def __getitem__(self, item):\n", " item = self.dataset[item]\n", - " return item[\"input_ids\"], item[\"attention_mask\"], [item[\"label\"]] " + " return item['input_ids'], item['attention_mask'], [item['label']] " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "再然后，**定义校对函数`collate_fn`对齐同个`batch`内的每笔数据**，需要注意的是该函数的\n", + "\n", + "  **返回值必须是字典**，**键值必须同待训练模型的`train_step`和`evaluate_step`函数的参数**\n", + "\n", + "  **相对应**；这也就是在`tutorial-0`中便被强调的，`fastNLP v0.8`的第一条**参数匹配**机制" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ - "def test_bert_collate_fn(batch):\n", + "def collate_fn(batch):\n", " input_ids, atten_mask, labels = [], [], []\n", " max_length = [0] * 3\n", " for each_item in batch:\n", @@ -280,35 +343,136 @@ " 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)}" + " 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": "markdown", + "metadata": {}, + "source": [ + "最后，分别对`tokenizer`处理过的训练集数据、验证集数据，进行预处理和批量划分" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ - "dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n", + "dataset_train = SeqClsDataset(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", + " batch_size=32, shuffle=True, collate_fn=collate_fn)\n", + "dataset_valid = SeqClsDataset(encoded_dataset['validation'])\n", "dataloader_valid = DataLoader(dataset=dataset_valid, \n", - " batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)" + " batch_size=32, shuffle=False, collate_fn=collate_fn)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. 模型训练：加载 distilbert-base、fastNLP 参数匹配、fine-tuning\n", + "\n", + "  最后就是模型训练的，分别需要使用`distilbert-base-uncased`搭建分类模型\n", + "\n", + "    初始化优化器`optimizer`、训练模块`trainer`，通过`run`函数完成训练\n", + "\n", + "此处使用的`nn.Module`模块搭建模型，与`tokenizer`类似，通过从`transformers`库中\n", + "\n", + "  导入`AutoModelForSequenceClassification`模块，基于`distilbert-base-uncased`模型初始\n", + "\n", + "需要注意的是**`AutoModelForSequenceClassification`模块的输入参数和输出结构**\n", + "\n", + "  一方面，可以**通过输入标签值`labels`**，**使用模块内的损失函数计算损失`loss`**\n", + "\n", + "    并且可以选择输入是词素编号序列`input_ids`，还是词素嵌入序列`inputs_embeds`\n", + "\n", + "  另方面，该模块不会直接输出预测结果，而是会**输出各预测分类上的几率`logits`**\n", + "\n", + "    基于上述描述，此处完成了中`train_step`和`evaluate_step`函数的定义\n", + "\n", + "    同样需要注意，函数的返回值体现了`fastNLP v0.8`的第二条**参数匹配**机制" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "class SeqClsModel(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", + "\n", + " def forward(self, input_ids, attention_mask, labels=None):\n", + " output = self.back_bone(input_ids=input_ids, \n", + " attention_mask=attention_mask, labels=labels)\n", + " return output\n", + "\n", + " def train_step(self, input_ids, attention_mask, labels):\n", + " loss = self(input_ids, attention_mask, labels).loss\n", + " return {'loss': 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": "markdown", + "metadata": {}, + "source": [ + "接着，通过确定分类数量初始化模型实例，同时调用`torch.optim.AdamW`模块初始化优化器" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "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_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_transform.bias', 'vocab_projector.bias', '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.bias', 'classifier.weight', 'classifier.bias', 'pre_classifier.weight']\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 == 'mnli' else 1 if task == 'stsb' else 2\n", + "\n", + "model = SeqClsModel(num_labels=num_labels, model_checkpoint=model_checkpoint)\n", + "\n", + "optimizers = AdamW(params=model.parameters(), lr=5e-5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "然后，使用之前完成的`dataloader_train`和`dataloader_valid`，定义训练模块`trainer`" + ] + }, + { + "cell_type": "code", + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "trainer = Trainer(\n", " model=model,\n", " driver='torch',\n", - " device='cuda',\n", + " device=1, # 'cuda'\n", " n_epochs=10,\n", " optimizers=optimizers,\n", " train_dataloader=dataloader_train,\n", @@ -318,42 +482,35 @@ ] }, { - "cell_type": "code", - "execution_count": 14, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "# help(model.back_bone.forward)" + "最后，使用`trainer.run`方法，训练模型，`n_epochs`参数中已经指定需要迭代`10`轮\n", + "\n", + "  `num_eval_batch_per_dl`参数则指定每次只对验证集中的`10`个`batch`进行评估" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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-    },
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-     "data": {
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-       "
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-      ],
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-       "\n"
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-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "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": [
-       "
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-       "  \"acc#acc\": 0.890625,\n",
-       "  \"total#acc\": 320.0,\n",
-       "  \"correct#acc\": 285.0\n",
-       "}\n",
-       "
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-      ],
-      "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": [
-       "
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-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "trainer.run(num_eval_batch_per_dl=10)"
+    "trainer.evaluator.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 附：`DistilBertForSequenceClassification`模块结构\n",
+    "\n",
+    "```\n",
+    "\n",
+    "```"
    ]
   },
   {
diff --git a/tutorials/fastnlp_tutorial_e2.ipynb b/tutorials/fastnlp_tutorial_e2.ipynb
index 1d7746be..93143090 100644
--- a/tutorials/fastnlp_tutorial_e2.ipynb
+++ b/tutorials/fastnlp_tutorial_e2.ipynb
@@ -4,7 +4,52 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# E2. 使用 continuous prompt 完成 SST2 分类"
+    "# E2. 使用 Bert + prompt 完成 SST2 分类\n",
+    "\n",
+    "  1   基础介绍：`prompt-based model`简介、与`fastNLP`的结合\n",
+    "\n",
+    "  2   准备工作：`P-Tuning v2`原理概述、`P-Tuning v2`模型搭建\n",
+    "\n",
+    "  3   模型训练：加载`tokenizer`、预处理`dataset`、模型训练与分析"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 1. 基础介绍：prompt-based model 简介、与 fastNLP 的结合\n",
+    "\n",
+    "  本示例使用`GLUE`评估基准中的`SST2`数据集，通过`prompt-based tuning`方式\n",
+    "\n",
+    "    微调`bert-base-uncased`模型，实现文本情感的二分类，在此之前本示例\n",
+    "\n",
+    "    将首先简单介绍提示学习模型的研究，以及与`fastNLP v0.8`结合的优势\n",
+    "\n",
+    "**`prompt`**，**提示词、提词器**，最早出自**`PET`**，\n",
+    "\n",
+    "  \n",
+    "\n",
+    "**`prompt-based tuning`**，**基于提示的微调**，描述\n",
+    "\n",
+    "  **`prompt-based model`**，**基于提示的模型**\n",
+    "\n",
+    "**`prompt-based model`**，**基于提示的模型**，举例\n",
+    "\n",
+    "  案例一：**`P-Tuning v1`**\n",
+    "\n",
+    "  案例二：**`PromptTuning`**\n",
+    "\n",
+    "  案例三：**`PrefixTuning`**\n",
+    "\n",
+    "  案例四：**`SoftPrompt`**\n",
+    "\n",
+    "使用`fastNLP v0.8`实现`prompt-based model`的优势\n",
+    "\n",
+    "  \n",
+    "\n",
+    "  本示例仍使用了`tutorial-E1`的`SST2`数据集，将`bert-base-uncased`作为基础模型\n",
+    "\n",
+    "    在后续实现中，意图通过将连续的`prompt`与`model`拼接，解决`SST2`二分类任务"
    ]
   },
   {
@@ -35,11 +80,10 @@
    ],
    "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",
@@ -51,19 +95,31 @@
     "from fastNLP import Trainer\n",
     "from fastNLP.core.metrics import Accuracy\n",
     "\n",
-    "print(transformers.__version__)"
+    "print(transformers.__version__)\n",
+    "\n",
+    "task = 'sst2'\n",
+    "model_checkpoint = 'bert-base-uncased'"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 2,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "GLUE_TASKS = [\"cola\", \"mnli\", \"mnli-mm\", \"mrpc\", \"qnli\", \"qqp\", \"rte\", \"sst2\", \"stsb\", \"wnli\"]\n",
+    "### 2. 准备工作：P-Tuning v2 原理概述、P-Tuning v2 模型搭建\n",
     "\n",
-    "task = \"sst2\"\n",
-    "model_checkpoint = \"distilbert-base-uncased\""
+    "  本示例使用`P-Tuning v2`作为`prompt-based tuning`与`fastNLP v0.8`结合的案例\n",
+    "\n",
+    "    以下首先简述`P-Tuning v2`的论文原理，并由此引出`fastNLP v0.8`的代码实践\n",
+    "\n",
+    "`P-Tuning v2`出自论文 [Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks](https://arxiv.org/pdf/2110.07602.pdf)\n",
+    "\n",
+    "  其主要贡献在于，在`PrefixTuning`等深度提示学习基础上，提升了其在分类标注等`NLU`任务的表现\n",
+    "\n",
+    "    并使之在中等规模模型，主要是参数量在`100M-1B`区间的模型上，获得与全参数微调相同的效果\n",
+    "\n",
+    "  其结构如图所示，\n",
+    "\n",
+    ""
    ]
   },
   {
@@ -72,7 +128,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class ClassModel(nn.Module):\n",
+    "class SeqClsModel(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",
@@ -92,7 +148,7 @@
     "        prompts = self.prefix_encoder(prefix_tokens)\n",
     "        return prompts\n",
     "\n",
-    "    def forward(self, input_ids, attention_mask, labels):\n",
+    "    def forward(self, input_ids, attention_mask, labels=None):\n",
     "        \n",
     "        batch_size = input_ids.shape[0]\n",
     "        raw_embedding = self.embeddings(input_ids)\n",
@@ -107,39 +163,64 @@
     "        return outputs\n",
     "\n",
     "    def train_step(self, input_ids, attention_mask, labels):\n",
-    "        return {\"loss\": self(input_ids, attention_mask, labels).loss}\n",
+    "        loss = self(input_ids, attention_mask, labels).loss\n",
+    "        return {'loss': 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}"
+    "        return {'pred': pred, 'target': labels}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "接着，通过确定分类数量初始化模型实例，同时调用`torch.optim.AdamW`模块初始化优化器\n",
+    "\n",
+    "  根据`P-Tuning v2`论文：*Generally, simple classification tasks prefer shorter prompts (less than 20)*\n",
+    "\n",
+    "  此处`pre_seq_len`参数设定为`20`，学习率相应做出调整，其他内容和`tutorial-E1`中的内容一致"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 4,
    "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",
+      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias']\n",
+      "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification 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 BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\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",
+    "model = SeqClsModel(model_checkpoint=model_checkpoint, num_labels=2, pre_seq_len=20)\n",
+    "\n",
+    "optimizers = AdamW(params=model.parameters(), lr=1e-2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 3. 模型训练：加载 tokenizer、预处理 dataset、模型训练与分析\n",
     "\n",
-    "model = ClassModel(num_labels=num_labels, model_checkpoint=model_checkpoint, pre_seq_len=16)\n",
+    "  本示例沿用`tutorial-E1`中的数据集，即使用`GLUE`评估基准中的`SST2`数据集\n",
     "\n",
-    "# Generally, simple classification tasks prefer shorter prompts (less than 20)\n",
+    "    以`bert-base-uncased`模型作为基准，基于`P-Tuning v2`方式微调\n",
     "\n",
-    "optimizers = AdamW(params=model.parameters(), lr=5e-3)"
+    "    数据集加载相关代码流程见下，内容和`tutorial-E1`中的内容基本一致\n",
+    "\n",
+    "首先，使用`datasets.load_dataset`加载数据集，使用`transformers.AutoTokenizer`\n",
+    "\n",
+    "  构建`tokenizer`实例，通过`dataset.map`使用`tokenizer`将文本替换为词素序号序列"
    ]
   },
   {
@@ -153,14 +234,13 @@
      "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": "1b73650d43f245ac8a5501dc91c6fe8c",
+       "model_id": "b72eeebd34354a88a99b2e07ec9a86df",
        "version_major": 2,
        "version_minor": 0
       },
@@ -175,7 +255,7 @@
    "source": [
     "from datasets import load_dataset, load_metric\n",
     "\n",
-    "dataset = load_dataset(\"glue\", \"mnli\" if task == \"mnli-mm\" else task)\n",
+    "dataset = load_dataset('glue', task)\n",
     "\n",
     "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint, use_fast=True)"
    ]
@@ -189,14 +269,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "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"
+      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-18ec0e709f05e61e.arrow\n",
+      "Loading cached processed dataset at /remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad/cache-e2f02ee7442ad73e.arrow\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0be84915c90f460896b8e67299e09df4",
+       "model_id": "d15505d825b34f649b719f1ff0d56114",
        "version_major": 2,
        "version_minor": 0
       },
@@ -216,14 +296,25 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "然后，定义`SeqClsDataset`类、定义校对函数`collate_fn`，这里沿用`tutorial-E1`中的内容\n",
+    "\n",
+    "  同样需要注意/强调的是，**`__getitem__`函数的返回值必须和原始数据集中的属性对应**\n",
+    "\n",
+    "  **`collate_fn`函数的返回值必须和`train_step`和`evaluate_step`函数的参数匹配**"
+   ]
+  },
+  {
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class TestDistilBertDataset(Dataset):\n",
+    "class SeqClsDataset(Dataset):\n",
     "    def __init__(self, dataset):\n",
-    "        super(TestDistilBertDataset, self).__init__()\n",
+    "        Dataset.__init__(self)\n",
     "        self.dataset = dataset\n",
     "\n",
     "    def __len__(self):\n",
@@ -231,16 +322,9 @@
     "\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": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def test_bert_collate_fn(batch):\n",
+    "        return item['input_ids'], item['attention_mask'], [item['label']] \n",
+    "\n",
+    "def collate_fn(batch):\n",
     "    input_ids, atten_mask, labels = [], [], []\n",
     "    max_length = [0] * 3\n",
     "    for each_item in batch:\n",
@@ -255,9 +339,16 @@
     "        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)}"
+    "    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": "markdown",
+   "metadata": {},
+   "source": [
+    "再然后，分别对`tokenizer`处理过的训练集数据、验证集数据，进行预处理和批量划分"
    ]
   },
   {
@@ -266,25 +357,43 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dataset_train = TestDistilBertDataset(encoded_dataset[\"train\"])\n",
+    "dataset_train = SeqClsDataset(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",
+    "                              batch_size=32, shuffle=True, collate_fn=collate_fn)\n",
+    "dataset_valid = SeqClsDataset(encoded_dataset['validation'])\n",
     "dataloader_valid = DataLoader(dataset=dataset_valid, \n",
-    "                              batch_size=32, shuffle=False, collate_fn=test_bert_collate_fn)"
+    "                              batch_size=32, shuffle=False, collate_fn=collate_fn)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
+      "To disable this warning, you can either:\n",
+      "\t- Avoid using `tokenizers` before the fork if possible\n",
+      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
+     ]
+    }
+   ],
    "source": [
     "trainer = Trainer(\n",
     "    model=model,\n",
     "    driver='torch',\n",
-    "    device='cuda',\n",
-    "    n_epochs=10,\n",
+    "    device=[0, 1],\n",
+    "    n_epochs=20,\n",
     "    optimizers=optimizers,\n",
     "    train_dataloader=dataloader_train,\n",
     "    evaluate_dataloaders=dataloader_valid,\n",
@@ -293,84 +402,33 @@
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "
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-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-      ],
-      "text/plain": []
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "
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-       "
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-      ],
-      "text/plain": [
-       "\n"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.run(num_eval_batch_per_dl=10)"
    ]
   },
   {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-      "text/plain": []
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-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "trainer.evaluator.run()"
    ]
diff --git a/tutorials/figures/E1-fig-glue-benchmark.png b/tutorials/figures/E1-fig-glue-benchmark.png
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