{
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   "source": [
    "# T4. fastNLP 中的预定义模型\n",
    "\n",
    "  1   fastNLP 中 modules 的介绍\n",
    " \n",
    "    1.1   modules 模块、models 模块 简介\n",
    "\n",
    "    1.2   示例一：modules 实现 LSTM 分类\n",
    "\n",
    "  2   fastNLP 中 models 的介绍\n",
    " \n",
    "    2.1   示例一：models 实现 CNN 分类\n",
    "\n",
    "    2.3   示例二：models 实现 BiLSTM 标注"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d65d53",
   "metadata": {},
   "source": [
    "## 1. fastNLP 中 modules 模块的介绍\n",
    "\n",
    "### 1.1  modules 模块、models 模块 简介\n",
    "\n",
    "在`fastNLP 1.0`中，**modules.torch 路径下定义了一些基于 pytorch 实现的基础模块**\n",
    "\n",
    "    包括长短期记忆网络`LSTM`、条件随机场`CRF`、`transformer`的编解码器模块等，详见下表\n",
    "\n",
    "代码名称|简要介绍|代码路径\n",
    "----|----|----|\n",
    " `LSTM` | 轻量封装`pytorch`的`LSTM` | `/modules/torch/encoder/lstm.py` |\n",
    " `Seq2SeqEncoder` | 序列变换编码器，基类 | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
    " `LSTMSeq2SeqEncoder` | 序列变换编码器，基于`LSTM` | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
    " `TransformerSeq2SeqEncoder` | 序列变换编码器，基于`transformer` | `/modules/torch/encoder/seq2seq_encoder.py` |\n",
    " `StarTransformer` | `Star-Transformer`的编码器部分 | `/modules/torch/encoder/star_transformer.py` |\n",
    " `VarRNN` | 实现`Variational Dropout RNN` | `/modules/torch/encoder/variational_rnn.py` |\n",
    " `VarLSTM` | 实现`Variational Dropout LSTM` | `/modules/torch/encoder/variational_rnn.py` |\n",
    " `VarGRU` | 实现`Variational Dropout GRU` | `/modules/torch/encoder/variational_rnn.py` |\n",
    " `MLP` | 多层感知机模型 | `/modules/torch/decoder/mlp.py` |\n",
    " `ConditionalRandomField` | 条件随机场模型 | `/modules/torch/decoder/crf.py` |\n",
    " `Seq2SeqDecoder` | 序列变换解码器，基类 | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
    " `LSTMSeq2SeqDecoder` | 序列变换解码器，基于`LSTM` | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
    " `TransformerSeq2SeqDecoder` | 序列变换解码器，基于`transformer` | `/modules/torch/decoder/seq2seq_decoder.py` |\n",
    " `SequenceGenerator` | 序列生成，封装`Seq2SeqDecoder` | `/models/torch/sequence_labeling.py` |\n",
    " `TimestepDropout` | 在每个`timestamp`上`dropout` | `/modules/torch/dropout.py` |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ffcf07",
   "metadata": {},
   "source": [
    "  **models.torch 路径下定义了一些基于 pytorch 、 modules 实现的预定义模型** \n",
    "\n",
    "    例如基于`CNN`的分类模型、基于`BiLSTM+CRF`的标注模型、基于[双仿射注意力机制](https://arxiv.org/pdf/1611.01734.pdf)的分析模型\n",
    "\n",
    "    基于`modules.torch`中的`LSTM`/`transformer`编/解码器模块的序列变换/生成模型，详见下表\n",
    "\n",
    "代码名称|简要介绍|代码路径\n",
    "----|----|----|\n",
    "| `BiaffineParser` | 句法分析模型，基于双仿射注意力 | `/models/torch/biaffine_parser.py` |\n",
    "| `CNNText` | 文本分类模型，基于`CNN` | `/models/torch/cnn_text_classification.py` |\n",
    "| `Seq2SeqModel` | 序列变换，基类`encoder+decoder` | `/models/torch/seq2seq_model.py` |\n",
    "| `LSTMSeq2SeqModel` | 序列变换，基于`LSTM` | `/models/torch/seq2seq_model.py` |\n",
    "| `TransformerSeq2SeqModel` | 序列变换，基于`transformer` | `/models/torch/seq2seq_model.py` |\n",
    "| `SequenceGeneratorModel` | 封装`Seq2SeqModel`，结合`SequenceGenerator` | `/models/torch/seq2seq_generator.py` |\n",
    "| `SeqLabeling` | 标注模型，基类`LSTM+FC+CRF` | `/models/torch/sequence_labeling.py` |\n",
    "| `BiLSTMCRF` | 标注模型，`BiLSTM+FC+CRF` | `/models/torch/sequence_labeling.py` |\n",
    "| `AdvSeqLabel` | 标注模型，`LN+BiLSTM*2+LN+FC+CRF` | `/models/torch/sequence_labeling.py` |"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61318354",
   "metadata": {},
   "source": [
    "上述`fastNLP`模块，不仅**为入门级用户提供了简单易用的工具**，以解决各种`NLP`任务，或复现相关论文\n",
    "\n",
    "  同时**也为专业研究人员提供了便捷可操作的接口**，封装部分代码的同时，也能指定参数修改细节\n",
    "\n",
    "  在接下来的`tutorial`中，我们将通过`SST-2`分类和`CoNLL-2003`标注，展示相关模型使用\n",
    "\n",
    "注一：**SST**，**单句情感分类**数据集，包含电影评论和对应情感极性，1 对应正面情感，0 对应负面情感\n",
    "\n",
    "  数据集包括三部分：训练集 67350 条，验证集 873 条，测试集 1821 条，更多参考[下载链接](https://gluebenchmark.com/tasks)\n",
    "\n",
    "注二：**CoNLL-2003**，**文本语法标注**数据集，包含语句和对应的词性标签`pos_tags`（名动形数量代）\n",
    "\n",
    "  语法结构标签`chunk_tags`（主谓宾定状补）、命名实体标签`ner_tags`（人名、组织名、地名、时间等）\n",
    "\n",
    "  数据集包括三部分：训练集 14041 条，验证集 3250 条，测试集 3453 条，更多参考[原始论文](https://aclanthology.org/W03-0419.pdf)"
   ]
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   "cell_type": "markdown",
   "id": "2a36bbe4",
   "metadata": {},
   "source": [
    "### 1.2  示例一：modules 实现 LSTM 分类\n",
    "\n",
    "\n",
    "  本示例使用`fastNLP 1.0`中预定义模型`modules`模块，基于`LSTM`模型，实现`SST-2`文本二分类任务\n",
    "\n",
    "数据使用方面：首先，**使用 datasets 模块中的 load_dataset 函数**，以如下形式，指定`SST-2`数据集加载\n",
    "\n",
    "    首次下载保存至`~/.cache/huggingface/modules/datasets_modules/datasets/glue/`目录下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1aa5cf6d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset glue (/remote-home/xrliu/.cache/huggingface/datasets/glue/sst2/1.0.0/dacbe3125aa31d7f70367a07a8a9e72a5a0bfeb5fc42e75c9db75b96da6053ad)\n"
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     "metadata": {},
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    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "sst2data = load_dataset('glue', 'sst2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c476abe7",
   "metadata": {},
   "source": [
    "&emsp; 接着，使用`tutorial-1`和`tutorial-2`中的知识，将数据集转化为`fastNLP`中的`DataSet`格式\n",
    "\n",
    "&emsp; &emsp; **使用 apply_more 函数、 Vocabulary 模块的 from_/index_dataset 函数预处理数据**\n",
    "\n",
    "&emsp; &emsp; &emsp; 并结合`delete_field`函数删除字段调整格式，`split`函数划分测试集和验证集\n",
    "\n",
    "&emsp; &emsp; **仅保留 'words' 字段表示输入文本单词序号序列、 'target' 字段表示文本对应预测输出结果**\n",
    "\n",
    "&emsp; &emsp; &emsp; 两者**对应到 CNNText 中 train_step 函数和 evaluate_step 函数的签名/输入参数**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "357ea748",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[38;5;2m[i 0604 16:19:46.727257 48 log.cc:351] Load log_sync: 1\u001b[m\n"
     ]
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       "</pre>\n"
      ],
      "text/plain": [
       "\n"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from fastNLP import DataSet\n",
    "\n",
    "dataset = DataSet.from_pandas(sst2data['train'].to_pandas())[:6000]\n",
    "\n",
    "dataset.apply_more(lambda ins:{'words': ins['sentence'].lower().split(), 'target': ins['label']}, \n",
    "                   progress_bar=\"tqdm\")\n",
    "dataset.delete_field('sentence')\n",
    "dataset.delete_field('label')\n",
    "dataset.delete_field('idx')\n",
    "\n",
    "from fastNLP import Vocabulary\n",
    "\n",
    "vocab = Vocabulary()\n",
    "vocab.from_dataset(dataset, field_name='words')\n",
    "vocab.index_dataset(dataset, field_name='words')\n",
    "\n",
    "train_dataset, evaluate_dataset = dataset.split(ratio=0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96380c67",
   "metadata": {},
   "source": [
    "&emsp; 然后，使用`tutorial-3`中的知识，**通过 prepare_torch_dataloader 处理数据集得到 dataloader**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b9dd1273",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import prepare_torch_dataloader\n",
    "\n",
    "train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
    "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb75aaba",
   "metadata": {},
   "source": [
    "模型使用方面，这里使用`Embedding`、`LSTM`、`MLP`等模块搭建模型，方法类似`pytorch`，结构如下所示\n",
    "\n",
    "```\n",
    "ClsByModules(\n",
    "  (embedding): Embedding(\n",
    "    (embed): Embedding(8458, 100)\n",
    "    (dropout): Dropout(p=0.0, inplace=False)\n",
    "  )\n",
    "  (lstm): LSTM(\n",
    "    (lstm): LSTM(100, 64, num_layers=2, batch_first=True, bidirectional=True)\n",
    "  )\n",
    "  (mlp): MLP(\n",
    "    (hiddens): ModuleList()\n",
    "    (output): Linear(in_features=128, out_features=2, bias=True)\n",
    "    (dropout): Dropout(p=0.5, inplace=False)\n",
    "  )\n",
    "  (loss_fn): CrossEntropyLoss()\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0b25b25c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "from fastNLP.modules.torch import LSTM, MLP\n",
    "from fastNLP.embeddings.torch import Embedding\n",
    "\n",
    "\n",
    "class ClsByModules(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, output_dim, hidden_dim=64, num_layers=2, dropout=0.5):\n",
    "        nn.Module.__init__(self)\n",
    "\n",
    "        self.embedding = Embedding((vocab_size, embedding_dim))\n",
    "        self.lstm = LSTM(embedding_dim, hidden_dim, num_layers=num_layers, bidirectional=True)\n",
    "        self.mlp = MLP([hidden_dim * 2, output_dim], dropout=dropout)\n",
    "        \n",
    "        self.loss_fn = nn.CrossEntropyLoss()\n",
    "\n",
    "    def forward(self, words):\n",
    "        output = self.embedding(words)\n",
    "        output, (hidden, cell) = self.lstm(output)\n",
    "        output = self.mlp(torch.cat((hidden[-1], hidden[-2]), dim=1))\n",
    "        return output\n",
    "    \n",
    "    def train_step(self, words, target):\n",
    "        pred = self(words)\n",
    "        return {\"loss\": self.loss_fn(pred, target)}\n",
    "\n",
    "    def evaluate_step(self, words, target):\n",
    "        pred = self(words)\n",
    "        pred = torch.max(pred, dim=-1)[1]\n",
    "        return {\"pred\": pred, \"target\": target}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4890de5a",
   "metadata": {},
   "source": [
    "&emsp; 接着，初始化模型`model`实例，同时，使用`torch.optim.AdamW`初始化`optimizer`实例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9dbbf50d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ClsByModules(vocab_size=len(vocab), embedding_dim=100, output_dim=2)\n",
    "\n",
    "from torch.optim import AdamW\n",
    "\n",
    "optimizers = AdamW(params=model.parameters(), lr=5e-5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "054538f5",
   "metadata": {},
   "source": [
    "&emsp; 最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a93432f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Trainer, Accuracy\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    driver='torch',\n",
    "    device=0,  # 'cuda'\n",
    "    n_epochs=10,\n",
    "    optimizers=optimizers,\n",
    "    train_dataloader=train_dataloader,\n",
    "    evaluate_dataloaders=evaluate_dataloader,\n",
    "    metrics={'acc': Accuracy()}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "31102e0f",
   "metadata": {},
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   "source": [
    "trainer.run(num_eval_batch_per_dl=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8bc4bfb2",
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       "{'acc#acc': 0.712222, 'total#acc': 900.0, 'correct#acc': 641.0}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "trainer.evaluator.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07538876",
   "metadata": {},
   "source": [
    "&emsp; 注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b52eafd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "383"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "\n",
    "del model\n",
    "del trainer\n",
    "del dataset\n",
    "del sst2data\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9443213",
   "metadata": {},
   "source": [
    "## 2. fastNLP 中 models 模块的介绍\n",
    "\n",
    "### 2.1  示例一：models 实现 CNN 分类\n",
    "\n",
    "&emsp; 本示例使用`fastNLP 1.0`中预定义模型`models`中的`CNNText`模型，实现`SST-2`文本二分类任务\n",
    "\n",
    "数据使用方面，此处沿用在上个示例中展示的`SST-2`数据集，数据加载过程相同且已经执行过了，因此简略\n",
    "\n",
    "模型使用方面，如上所述，这里使用**基于卷积神经网络 CNN 的预定义文本分类模型 CNNText**，结构如下所示\n",
    "\n",
    "&emsp; 首先是内置的`100`维嵌入层、`dropout`层、紧接着是三个一维卷积，将`100`维嵌入特征，分别通过\n",
    "\n",
    "&emsp; &emsp; **感受野为 1 、 3 、 5 的卷积算子变换至 30 维、 40 维、 50 维的卷积特征**，再将三者拼接\n",
    "\n",
    "&emsp; 最终再次通过`dropout`层、线性变换层，映射至二元的输出值，对应两个分类结果上的几率`logits`\n",
    "\n",
    "```\n",
    "CNNText(\n",
    "  (embed): Embedding(\n",
    "    (embed): Embedding(5194, 100)\n",
    "    (dropout): Dropout(p=0.0, inplace=False)\n",
    "  )\n",
    "  (conv_pool): ConvMaxpool(\n",
    "    (convs): ModuleList(\n",
    "      (0): Conv1d(100, 30, kernel_size=(1,), stride=(1,), bias=False)\n",
    "      (1): Conv1d(100, 40, kernel_size=(3,), stride=(1,), padding=(1,), bias=False)\n",
    "      (2): Conv1d(100, 50, kernel_size=(5,), stride=(1,), padding=(2,), bias=False)\n",
    "    )\n",
    "  )\n",
    "  (dropout): Dropout(p=0.1, inplace=False)\n",
    "  (fc): Linear(in_features=120, out_features=2, bias=True)\n",
    ")\n",
    "```\n",
    "\n",
    "对应到代码上，**从 fastNLP.models.torch 路径下导入 CNNText**，初始化`CNNText`和`optimizer`实例\n",
    "\n",
    "&emsp; 注意：初始化`CNNText`时，**二元组参数 embed 、分类数量 num_classes 是必须传入的**，其中\n",
    "\n",
    "&emsp; &emsp; **embed 表示嵌入层的嵌入抽取矩阵大小**，因此第二个元素对应的是默认隐藏层维度 `100` 维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f6e76e2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP.models.torch import CNNText\n",
    "\n",
    "model = CNNText(embed=(len(vocab), 100), num_classes=2, dropout=0.1)\n",
    "\n",
    "from torch.optim import AdamW\n",
    "\n",
    "optimizers = AdamW(params=model.parameters(), lr=5e-4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cc5ca10",
   "metadata": {},
   "source": [
    "&emsp; 最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50a13ee5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Trainer, Accuracy\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    driver='torch',\n",
    "    device=0,  # 'cuda'\n",
    "    n_epochs=10,\n",
    "    optimizers=optimizers,\n",
    "    train_dataloader=train_dataloader,\n",
    "    evaluate_dataloaders=evaluate_dataloader,\n",
    "    metrics={'acc': Accuracy()}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28903a7d",
   "metadata": {},
   "outputs": [
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   ],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f47a6a35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
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    },
    {
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      "text/plain": []
     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
       "{'acc#acc': 0.806667, 'total#acc': 900.0, 'correct#acc': 726.0}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.evaluator.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b5c0446",
   "metadata": {},
   "source": [
    "&emsp; 注：此处使用`gc`模块删除相关变量，释放内存，为接下来新的模型训练预留存储空间，下同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e9e70f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "344"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gc\n",
    "\n",
    "del model\n",
    "del trainer\n",
    "\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aec2a19",
   "metadata": {},
   "source": [
    "### 2.2  示例二：models 实现 BiLSTM 标注\n",
    "\n",
    "&emsp; 通过两个示例一的对比可以发现，得益于`models`对模型结构的封装，使用`models`明显更加便捷\n",
    "\n",
    "&emsp; &emsp; 针对更加复杂的模型时，编码更加轻松；本示例将使用`models`中的`BiLSTMCRF`模型\n",
    "\n",
    "&emsp; 避免`CRF`和`Viterbi`算法代码书写的困难，轻松实现`CoNLL-2003`中的命名实体识别`NER`任务\n",
    "\n",
    "模型使用方面，如上所述，这里使用**基于双向 LSTM +条件随机场 CRF 的标注模型 BiLSTMCRF**，结构如下所示\n",
    "\n",
    "&emsp; 其中，隐藏层维度默认`100`维，因此对应双向`LSTM`输出`200`维，`dropout`层退学概率、`LSTM`层数可调\n",
    "\n",
    "```\n",
    "BiLSTMCRF(\n",
    "  (embed): Embedding(7590, 100)\n",
    "  (lstm): LSTM(\n",
    "    (lstm): LSTM(100, 100, batch_first=True, bidirectional=True)\n",
    "  )\n",
    "  (dropout): Dropout(p=0.1, inplace=False)\n",
    "  (fc): Linear(in_features=200, out_features=9, bias=True)\n",
    "  (crf): ConditionalRandomField()\n",
    ")\n",
    "```\n",
    "\n",
    "数据使用方面，此处仍然**使用 datasets 模块中的 load_dataset 函数**，以如下形式，加载`CoNLL-2003`数据集\n",
    "\n",
    "&emsp; 首次下载后会保存至`~.cache/huggingface/datasets/conll2003/conll2003/1.0.0/`目录下"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "03e66686",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Reusing dataset conll2003 (/remote-home/xrliu/.cache/huggingface/datasets/conll2003/conll2003/1.0.0/63f4ebd1bcb7148b1644497336fd74643d4ce70123334431a3c053b7ee4e96ee)\n"
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       "  0%|          | 0/3 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "ner2data = load_dataset('conll2003', 'conll2003')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc505631",
   "metadata": {},
   "source": [
    "紧接着，使用`tutorial-1`和`tutorial-2`中的知识，将数据集转化为`fastNLP`中的`DataSet`格式\n",
    "\n",
    "&emsp; 完成数据集格式调整、文本序列化等操作；此处**需要 'words' 、 'seq_len' 、 'target' 三个字段**\n",
    "\n",
    "此外，**需要定义 NER 标签到标签序号的映射**（**词汇表 label_vocab**），数据集中标签已经完成了序号映射\n",
    "\n",
    "&emsp; 所以需要人工定义**9 个标签对应之前的 9 个分类目标**；数据集说明中规定，`'O'`表示其他标签\n",
    "\n",
    "&emsp; **后缀 '-PER' 、 '-ORG' 、 '-LOC' 、 '-MISC' 对应人名、组织名、地名、时间等其他命名**\n",
    "\n",
    "&emsp; **前缀 'B-' 表示起始标签、 'I-' 表示终止标签**；例如，`'B-PER'`表示人名实体的起始标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1f88cad4",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "Processing:   0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('..')\n",
    "\n",
    "from fastNLP import DataSet\n",
    "\n",
    "dataset = DataSet.from_pandas(ner2data['train'].to_pandas())[:4000]\n",
    "\n",
    "dataset.apply_more(lambda ins:{'words': ins['tokens'], 'seq_len': len(ins['tokens']), 'target': ins['ner_tags']}, \n",
    "                   progress_bar=\"tqdm\")\n",
    "dataset.delete_field('tokens')\n",
    "dataset.delete_field('ner_tags')\n",
    "dataset.delete_field('pos_tags')\n",
    "dataset.delete_field('chunk_tags')\n",
    "dataset.delete_field('id')\n",
    "\n",
    "from fastNLP import Vocabulary\n",
    "\n",
    "token_vocab = Vocabulary()\n",
    "token_vocab.from_dataset(dataset, field_name='words')\n",
    "token_vocab.index_dataset(dataset, field_name='words')\n",
    "label_vocab = Vocabulary(padding=None, unknown=None)\n",
    "label_vocab.add_word_lst(['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'B-MISC', 'I-MISC'])\n",
    "\n",
    "train_dataset, evaluate_dataset = dataset.split(ratio=0.85)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9889427",
   "metadata": {},
   "source": [
    "然后，同样使用`tutorial-3`中的知识，通过`prepare_torch_dataloader`处理数据集得到`dataloader`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7802a072",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import prepare_torch_dataloader\n",
    "\n",
    "train_dataloader = prepare_torch_dataloader(train_dataset, batch_size=16, shuffle=True)\n",
    "evaluate_dataloader = prepare_torch_dataloader(evaluate_dataset, batch_size=16)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bc7831b",
   "metadata": {},
   "source": [
    "接着，**从 fastNLP.models.torch 路径下导入 BiLSTMCRF**，初始化`BiLSTMCRF`实例和优化器\n",
    "\n",
    "&emsp; 注意：初始化`BiLSTMCRF`时，和`CNNText`相同，**参数 embed 、 num_classes 是必须传入的**\n",
    "\n",
    "&emsp; &emsp; 隐藏层维度`hidden_size`默认`100`维，调整`150`维；退学概率默认`0.1`，调整`0.2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "4e12c09f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP.models.torch import BiLSTMCRF\n",
    "\n",
    "model = BiLSTMCRF(embed=(len(token_vocab), 150), num_classes=len(label_vocab), \n",
    "                  num_layers=1, hidden_size=150, dropout=0.2)\n",
    "\n",
    "from torch.optim import AdamW\n",
    "\n",
    "optimizers = AdamW(params=model.parameters(), lr=1e-3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf30608f",
   "metadata": {},
   "source": [
    "最后，使用`trainer`模块，集成`model`、`optimizer`、`dataloader`、`metric`训练\n",
    "\n",
    "&emsp; **使用 SpanFPreRecMetric 作为 NER 的评价标准**，详细请参考接下来的`tutorial-5`\n",
    "\n",
    "&emsp; 同时，**初始化时需要添加 vocabulary 形式的标签与序号之间的映射 tag_vocab**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cbd6c205",
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastNLP import Trainer, SpanFPreRecMetric\n",
    "\n",
    "trainer = Trainer(\n",
    "    model=model,\n",
    "    driver='torch',\n",
    "    device=0,  # 'cuda'\n",
    "    n_epochs=10,\n",
    "    optimizers=optimizers,\n",
    "    train_dataloader=train_dataloader,\n",
    "    evaluate_dataloaders=evaluate_dataloader,\n",
    "    metrics={'F1': SpanFPreRecMetric(tag_vocab=label_vocab)}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0f8eff34",
   "metadata": {},
   "outputs": [
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