NLIIndonesia.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Natural Language Inference for Bahasa Indonesia**\n",
    "\n",
    "*Natural language inference* studies whether a *hypothesis*\n",
    "can be inferred from a *premise*, where both are a text sequence.\n",
    "In other words, natural language inference determines the logical relationship between a pair of text sequences.\n",
    "Such relationships usually fall into three types:\n",
    "\n",
    "* *Entailment*: the hypothesis can be inferred from the premise.\n",
    "* *Contradiction*: the negation of the hypothesis can be inferred from the premise.\n",
    "* *Neutral*: all the other cases.\n",
    "\n",
    "Natural language inference is also known as the recognizing textual entailment task.\n",
    "For example: \n",
    "\n",
    "The following pair will be labeled as *entailment* because \"sudah pernah menang pemilu\" in the hypothesis can be inferred from \"dipilih kembali\" in the premise.\n",
    "> Premise: \"Mengingat hasil pemilu 2007, peluang Blocher untuk dipilih kembali dianggap sangat tipis untuk meraih kemenangan.\"\n",
    "\n",
    "> Hypothesis: \"Blocher sudah pernah menang pemilu.\"\n",
    "\n",
    "The following is an example of *contradiction* as \"mengambil cuti untuk melahirkan\" indicates \"pernah mengambil cuti\" rather than \"tidak pernah\".\n",
    "\n",
    "> Premise: \"Sewaktu album ini direkam, Yuko Hara mengambil cuti untuk melahirkan dan band beristirahat untuk sementara.\"\n",
    "\n",
    "> Hypothesis: \"Yuko Hara tidak pernah mengambil cuti.\"\n",
    "\n",
    "The third example shows a *neutrality* relationship because neither \"selalu bersama\" nor \"tidak\" can be inferred from the fact that \"sama-sama sedang menggarap proyek musik\". \n",
    "\n",
    "> Premise: \"Seakan tak bisa dipisahkan, dua sahabat itu sama-sama sedang menggarap proyek musik.\"\n",
    "\n",
    "> Hypothesis: \"Dua sahabat itu selalu bersama-sama.\"\n",
    "\n",
    "Natural language inference has been a central topic for understanding natural language.\n",
    "It enjoys wide applications ranging from\n",
    "information retrieval to open-domain question answering.\n",
    "\n",
    "## **IndoNLI: A Natural Language Inference Dataset for Bahasa Indonesia**\n",
    "\n",
    "**IndoNLI Dataset** is a collection of over 18000 labeled Bahasa Indonesia sentence pairs. 12k+ training set and 5k+ test set. Datasets can be download [here](https://github.com/ir-nlp-csui/indonli) and save it in the `data` directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import json\n",
    "import torch\n",
    "import helper\n",
    "import numpy as np\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "\n",
    "data_dir = \"data\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Reading the Dataset**\n",
    "\n",
    "The original IndoNLI dataset contains much richer information than what we really need in our experiments. Thus, we define a function `read_snli` to only extract part of the dataset, then return lists of premises, hypotheses, and their labels.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_inli(data_dir, is_train):\n",
    "    \"\"\"Read the IndoNLI dataset into premises, hypotheses, and labels.\"\"\"\n",
    "    def extract_text(s):\n",
    "        # Remove information that will not be used by us\n",
    "        s = re.sub('\\\\(', '', s)\n",
    "        s = re.sub('\\\\)', '', s)\n",
    "        s = re.sub(r'([,\\.])', r' \\1 ', s)\n",
    "        # Substitute two or more consecutive whitespace with space\n",
    "        s = re.sub('\\\\s{2,}', ' ', s)\n",
    "        return s.strip()\n",
    "    label_set = {'e': 0, 'c': 1, 'n': 2}\n",
    "    if is_train:\n",
    "        file_names = [os.path.join(data_dir,'train.jsonl'), os.path.join(data_dir,'val.jsonl')]\n",
    "    else:\n",
    "        file_names = [os.path.join(data_dir,'test.jsonl')]\n",
    "\n",
    "    rows = []\n",
    "    for file_name in file_names:\n",
    "        with open(file_name, 'r') as file:\n",
    "            for line in file:\n",
    "                data = json.loads(line)  # Mengurai baris sebagai objek JSON\n",
    "                rows.append(data)   # Menambahkan objek JSON ke dalam daftar    \n",
    "    \n",
    "    premises = [extract_text(row[\"premise\"]) for row in rows] \n",
    "    hypotheses = [extract_text(row[\"hypothesis\"]) for row in rows]\n",
    "    labels = [label_set[row[\"label\"]] for row in rows]\n",
    "    \n",
    "    return premises, hypotheses, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = read_inli(data_dir, is_train=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "premise: Presiden Joko Widodo Jokowi menyampaikan prediksi bahwa wabah virus Corona COVID-19 di Indonesia akan selesai akhir tahun ini .\n",
      "hypothesis: Prediksi akhir wabah tidak disampaikan Jokowi .\n",
      "label: 1\n"
     ]
    }
   ],
   "source": [
    "print('premise:', train_data[0][0])\n",
    "print('hypothesis:', train_data[1][0])\n",
    "print('label:', train_data[2][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset for training and test has quiet balanced data in three label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4283, 4188, 4056]\n",
      "[1849, 1763, 1573]\n"
     ]
    }
   ],
   "source": [
    "test_data = read_inli(data_dir, is_train=False)\n",
    "for data in [train_data, test_data]:\n",
    "    print([[row for row in data[2]].count(i) for i in range(3)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Defining a Class for Loading the Dataset**\n",
    "\n",
    "Below we define a class for loading the IndoNLI dataset by inheriting from the `Dataset` class from `torch.utils`. The argument `num_steps` in the class constructor specifies the length of a text sequence so that each minibatch of sequences will have the same shape. In other words, tokens after the first `num_steps` ones in longer sequence are trimmed, while special tokens “&lt;pad&gt;” will be appended to shorter sequences until their length becomes `num_steps`. By implementing the `__getitem__` function, we can arbitrarily access the premise, hypothesis, and label with the index `idx`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class INLIDataset(torch.utils.data.Dataset):\n",
    "    \"\"\"A customized dataset to load the IndoNLI dataset.\"\"\"\n",
    "    def __init__(self, dataset, num_steps, vocab=None):\n",
    "        self.num_steps = num_steps\n",
    "        all_premise_tokens = helper.tokenize(dataset[0])\n",
    "        all_hypothesis_tokens = helper.tokenize(dataset[1])\n",
    "        if vocab is None:\n",
    "            self.vocab = helper.Vocab(all_premise_tokens + all_hypothesis_tokens,\n",
    "                                        min_freq=5, reserved_tokens=['<pad>'])\n",
    "        else:\n",
    "            self.vocab = vocab\n",
    "        self.premises = self._pad(all_premise_tokens)\n",
    "        self.hypotheses = self._pad(all_hypothesis_tokens)\n",
    "        self.labels = torch.tensor(dataset[2])\n",
    "        print('read ' + str(len(self.premises)) + ' examples')\n",
    "\n",
    "    def _pad(self, lines):\n",
    "        return torch.tensor([helper.truncate_pad(\n",
    "            self.vocab[line], self.num_steps, self.vocab['<pad>'])\n",
    "                            for line in lines])\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return (self.premises[idx], self.hypotheses[idx]), self.labels[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.premises)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Loading the Dataset**\n",
    "Now we can invoke the `read_inli` function and the `INLIDataset` class to read IndoNLI dataset and return `DataLoader` instances for both training and testing sets, together with the vocabulary of the training set. It is noteworthy that we must use the vocabulary constructed from the training set as that of the testing set. As a result, any new token from the testing set will be unknown to the model trained on the training set.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data_inli(batch_size, num_steps=50):\n",
    "    \"\"\"Read the IndoNLI dataset and return data iterators and vocabulary.\"\"\"\n",
    "    num_workers = 0\n",
    "    train_data = read_inli(data_dir, True)\n",
    "    test_data = read_inli(data_dir, False)\n",
    "    train_set = INLIDataset(train_data, num_steps)\n",
    "    test_set = INLIDataset(test_data, num_steps, train_set.vocab)\n",
    "    train_iter = torch.utils.data.DataLoader(train_set, batch_size,\n",
    "                                                shuffle=True,\n",
    "                                                num_workers=num_workers)\n",
    "    test_iter = torch.utils.data.DataLoader(test_set, batch_size,\n",
    "                                            shuffle=False,\n",
    "                                            num_workers=num_workers)\n",
    "    return train_iter, test_iter, train_set.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 12527 examples\n",
      "read 5185 examples\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "11374"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_size, num_steps = 128, 50\n",
    "train_iter, test_iter, vocab = load_data_inli(batch_size, num_steps)\n",
    "len(vocab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Model**\n",
    "\n",
    "We use the *decomposable attention model* invented by [Parikh *et al.* (2016)](https://paperswithcode.com/paper/a-decomposable-attention-model-for-natural). It consists of three jointly trained steps: attending, comparing, and aggregating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Attending**\n",
    "\n",
    "The first step is to align tokens in one text sequence to each token in the other sequence.\n",
    "Suppose that the premise is \"saya butuh tidur\" (i do need sleep) and the hypothesis is \"saya capek\" (i am tired).\n",
    "Due to semantical similarity,\n",
    "we may wish to align \"saya\" (i) in the hypothesis with \"saya\" (i) in the premise,\n",
    "and align \"capek\" (tired) in the hypothesis with \"tidur\" (sleep) in the premise.\n",
    "Likewise, we may wish to align \"saya\" (i) in the premise with \"saya\" (i) in the hypothesis,\n",
    "and align \"butuh\" (need) and \"tidur\" (sleep) in the premise with \"capek\" (tired) in the hypothesis.\n",
    "Note that such alignment is *soft* using weighted average,\n",
    "where ideally large weights are associated with the tokens to be aligned.\n",
    "\n",
    "For the attention weights to each pair **token_premise--token_hypothesis** we use MLP defined in the following `mlp` function. The output dimension of f is specified by the `num_hiddens`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mlp(num_inputs, num_hiddens, flatten):\n",
    "    net = []\n",
    "    net.append(nn.Dropout(0.2))\n",
    "    net.append(nn.Linear(num_inputs, num_hiddens))\n",
    "    net.append(nn.ReLU())\n",
    "    if flatten:\n",
    "        net.append(nn.Flatten(start_dim=1))\n",
    "    net.append(nn.Dropout(0.2))\n",
    "    net.append(nn.Linear(num_hiddens, num_hiddens))\n",
    "    net.append(nn.ReLU())\n",
    "    if flatten:\n",
    "        net.append(nn.Flatten(start_dim=1))\n",
    "    return nn.Sequential(*net)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we define the `Attend` class to compute the soft alignment of hypotheses (`beta`) with input premises `A` and soft alignment of premises (`alpha`) with input hypotheses `B`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Attend(nn.Module):\n",
    "    def __init__(self, num_inputs, num_hiddens, **kwargs):\n",
    "        super(Attend, self).__init__(**kwargs)\n",
    "        self.f = mlp(num_inputs, num_hiddens, flatten=False)\n",
    "\n",
    "    def forward(self, A, B):\n",
    "        # Shape of `A`/`B`: (`batch_size`, no. of tokens in sequence A/B, `embed_size`)\n",
    "        # Shape of `f_A`/`f_B`: (`batch_size`, no. of tokens in sequence A/B, `num_hiddens`)\n",
    "        f_A = self.f(A)\n",
    "        f_B = self.f(B)\n",
    "        \n",
    "        # Shape of `e`: (`batch_size`, no. of tokens in sequence A, no. of tokens in sequence B)\n",
    "        e = torch.bmm(f_A, f_B.permute(0, 2, 1))\n",
    "        \n",
    "        # Shape of `beta`: (`batch_size`, no. of tokens in sequence A, `embed_size`), \n",
    "        # where sequence B is softly aligned with each token (axis 1 of `beta`) in sequence A\n",
    "        beta = torch.bmm(F.softmax(e, dim=-1), B)\n",
    "        \n",
    "        # Shape of `alpha`: (`batch_size`, no. of tokens in sequence B, `embed_size`), \n",
    "        # where sequence A is softly aligned with each token (axis 1 of `alpha`) in sequence B\n",
    "        alpha = torch.bmm(F.softmax(e.permute(0, 2, 1), dim=-1), A)\n",
    "        \n",
    "        return beta, alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Comparing**\n",
    "\n",
    "In the next step, we compare a token in one sequence with the other sequence that is softly aligned with that token.\n",
    "Note that in soft alignment, all the tokens from one sequence, though with probably different attention weights, will be compared with a token in the other sequence.\n",
    "For example, suppose that the attending step determines that \"butuh\" (need) and \"tidur\" (sleep) in the premise are both aligned with \"capek\" (tired) in the hypothesis, the pair \"capek--butuh tidur\" will be compared.\n",
    "\n",
    "In the comparing step, we feed the concatenation of tokens from one sequence and aligned tokens from the other sequence into a function $g$ (an MLP) defined in the following `Compare` class. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Compare(nn.Module):\n",
    "    def __init__(self, num_inputs, num_hiddens, **kwargs):\n",
    "        super(Compare, self).__init__(**kwargs)\n",
    "        self.g = mlp(num_inputs, num_hiddens, flatten=False)\n",
    "\n",
    "    def forward(self, A, B, beta, alpha):\n",
    "        V_A = self.g(torch.cat([A, beta], dim=2))\n",
    "        V_B = self.g(torch.cat([B, alpha], dim=2))\n",
    "        return V_A, V_B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Aggregating**\n",
    "\n",
    "The last step we will aggregate all information to infer the logical relationship.\n",
    "We begin by summing up both sets before (`V_A` and `V_B`) along dimension 1.\n",
    "Next we feed the concatenation of both summarization results into function $h$ (an `MLP`) to obtain the classification result of the logical relationship. The aggregation step is defined in the following `Aggregate` class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Aggregate(nn.Module):\n",
    "    def __init__(self, num_inputs, num_hiddens, num_outputs, **kwargs):\n",
    "        super(Aggregate, self).__init__(**kwargs)\n",
    "        self.h = mlp(num_inputs, num_hiddens, flatten=True)\n",
    "        self.linear = nn.Linear(num_hiddens, num_outputs)\n",
    "\n",
    "    def forward(self, V_A, V_B):\n",
    "        # Sum up both sets of comparison vectors\n",
    "        V_A = V_A.sum(dim=1)\n",
    "        V_B = V_B.sum(dim=1)\n",
    "        \n",
    "        # Feed the concatenation of both summarization results into an MLP\n",
    "        Y_hat = self.linear(self.h(torch.cat([V_A, V_B], dim=1)))\n",
    "        \n",
    "        return Y_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By putting the attending, comparing, and aggregating steps together, we define the **decomposable attention model** to jointly train these three steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DecomposableAttention(nn.Module):\n",
    "    def __init__(self, vocab, embed_size, num_hiddens, num_inputs_attend=300, num_inputs_compare=600, num_inputs_agg=400, **kwargs):\n",
    "        super(DecomposableAttention, self).__init__(**kwargs)\n",
    "        self.embedding = nn.Embedding(len(vocab), embed_size)\n",
    "        self.attend = Attend(num_inputs_attend, num_hiddens)\n",
    "        self.compare = Compare(num_inputs_compare, num_hiddens)\n",
    "        # There are 3 possible outputs: entailment, contradiction, and neutral\n",
    "        self.aggregate = Aggregate(num_inputs_agg, num_hiddens, num_outputs=3)\n",
    "\n",
    "    def forward(self, X):\n",
    "        premises, hypotheses = X\n",
    "        A = self.embedding(premises)\n",
    "        B = self.embedding(hypotheses)\n",
    "        beta, alpha = self.attend(A, B)\n",
    "        V_A, V_B = self.compare(A, B, beta, alpha)\n",
    "        Y_hat = self.aggregate(V_A, V_B)\n",
    "        return Y_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Training and Evaluating the Model**\n",
    "\n",
    "### **Creating The Model**\n",
    "We use the pretrained 300-dimensional [fasttext](https://fasttext.cc/docs/en/crawl-vectors.html) Indonesian embedding to represent the input tokens.\n",
    "Thus, we predefine the dimension of vectors `embed_size` as 100.\n",
    "The output dimension of functions f at Attend step and g in compare step is set to 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 0.0318, -0.0916,  0.0199,  ...,  0.2165, -0.0551, -0.2314],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        ...,\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
       "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embed_size, num_hiddens, devices = 300, 200, helper.try_all_gpus()\n",
    "net = DecomposableAttention(vocab, embed_size, num_hiddens)\n",
    "fasttext_embedding = helper.FastTextEmbedding()\n",
    "embeds = fasttext_embedding[vocab.idx_to_token]\n",
    "net.embedding.weight.data.copy_(embeds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Putting It All Together into Training and Evaluating**\n",
    "\n",
    "Putting itt all the loaded train test dataset, trainer, and model we can tran and evaluate model on the IndoNLI dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.368, train acc 0.832, test acc 0.441\n",
      "1398.7 examples/sec on [device(type='cuda', index=0)]\n"
     ]
    },
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   "source": [
    "lr, num_epochs = 0.001, 10\n",
    "trainer = torch.optim.Adam(net.parameters(), lr=lr)\n",
    "loss = nn.CrossEntropyLoss(reduction=\"none\")\n",
    "helper.train(net, train_iter, test_iter, loss, trainer, num_epochs, devices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Result**\n",
    "The result shows that the model still far good enough compare to english NLI. Because, the Indonesian dataset compare to Englsih dataset is very huge 550k pairs againt 18k pairs. Still it's not a bad result and can be usefull for next project related Indonesian NLI. \n",
    "### **Using The Model**\n",
    "Finally, we can use the trained model to obtain the natural language inference result for a sample pair of sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_inli(net, vocab, premise, hypothesis):\n",
    "    \"\"\"Predict the logical relationship between the premise and hypothesis.\"\"\"\n",
    "    net.eval()\n",
    "    premise = torch.tensor(vocab[premise], device=torch.device('cuda:0'))\n",
    "    hypothesis = torch.tensor(vocab[hypothesis], device=torch.device('cuda:0'))\n",
    "    label = torch.argmax(net([premise.reshape((1, -1)),\n",
    "                           hypothesis.reshape((1, -1))]), dim=1)\n",
    "    return 'entailment' if label == 0 else 'contradiction' if label == 1 \\\n",
    "            else 'neutral'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'entailment'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "premise = \"Saya lapar sekali.\"\n",
    "hypothesis = \"Saya butuh makan.\"\n",
    "predict_inli(net, vocab, premise.split(), hypothesis.split())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Dump The Model**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object successfully saved to \"data/vocab.pkl\"\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "\n",
    "file_name = 'data/vocab.pkl'\n",
    "with open(file_name, 'wb') as file:\n",
    "    pickle.dump(vocab, file)\n",
    "    print(f'Object successfully saved to \"{file_name}\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(net.state_dict(), 'data/model.pt')"
   ]
  }
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