BERT-Quora-Question-Pairs.ipynb

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    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j-bQOGI43tji"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AF_3k1MUX1B",
        "outputId": "9b89f3ad-5c8e-4911-bf5f-d88f264a6b5e"
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.12.5-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 7.1 MB/s \n",
            "\u001b[?25hCollecting pyyaml>=5.1\n",
            "  Downloading PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (596 kB)\n",
            "\u001b[K     |████████████████████████████████| 596 kB 69.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.4.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.62.3)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "  Downloading tokenizers-0.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.3 MB 49.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from transformers) (21.3)\n",
            "Collecting huggingface-hub<1.0,>=0.1.0\n",
            "  Downloading huggingface_hub-0.2.1-py3-none-any.whl (61 kB)\n",
            "\u001b[K     |████████████████████████████████| 61 kB 306 kB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from transformers) (4.8.2)\n",
            "Collecting sacremoses\n",
            "  Downloading sacremoses-0.0.46-py3-none-any.whl (895 kB)\n",
            "\u001b[K     |████████████████████████████████| 895 kB 75.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/dist-packages (from huggingface-hub<1.0,>=0.1.0->transformers) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.6)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.6.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2021.10.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Installing collected packages: pyyaml, tokenizers, sacremoses, huggingface-hub, transformers\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed huggingface-hub-0.2.1 pyyaml-6.0 sacremoses-0.0.46 tokenizers-0.10.3 transformers-4.12.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAzsxAc8LP26"
      },
      "source": [
        "# from preprocessing import preprocess\n",
        "from transformers import DistilBertTokenizer, DistilBertModel\n",
        "import torch\n",
        "from torch import nn, optim\n",
        "import copy\n",
        "import random\n",
        "import sklearn.metrics\n",
        "import tqdm\n",
        "import pickle\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import math"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Of5ErFeh3zSZ"
      },
      "source": [
        "## Helper Functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6qgR1vS0Uw30"
      },
      "source": [
        "def shuffle_data(input_1, input_2, labels):\n",
        "    shuffled_input_1 = []\n",
        "    shuffled_input_2 = []\n",
        "    shuffled_labels      = []\n",
        "    indices = list(range(len(input_1)))\n",
        "    random.shuffle(indices)\n",
        "    for i in indices:\n",
        "        shuffled_input_1.append(input_1[i])\n",
        "        shuffled_input_2.append(input_2[i])\n",
        "        shuffled_labels.append(labels[i])\n",
        "    return (shuffled_input_1, shuffled_input_2, shuffled_labels)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WsS_flefU9_S"
      },
      "source": [
        "def train(model, tokenizer, X_1, X_2, Y, learning_rate=0.1, batch_size=8, num_epochs=5):\n",
        "  optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
        "  for epoch in range(num_epochs):\n",
        "      total_loss = 0.0\n",
        "      (shuffled_input_1, shuffled_input_2, shuffled_labels) = shuffle_data(X_1, X_2, Y)\n",
        "      for batch in tqdm.notebook.tqdm(range(0, len(X_1), batch_size), leave=False):\n",
        "          #Randomly shuffle examples in each epoch\n",
        "          input_1 = shuffled_input_1[batch:(batch + batch_size)]\n",
        "          input_2 = shuffled_input_2[batch:(batch + batch_size)]\n",
        "          encoded_input_1 = tokenizer(input_1, return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "          encoded_input_2 = tokenizer(input_2, return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "          labels = shuffled_labels[batch:(batch + batch_size)]\n",
        "          labels_onehot = torch.zeros(len(labels), num_classes).cuda()\n",
        "          for i in range(len(labels)):\n",
        "              labels_onehot[i][labels[i]] = 1.0\n",
        "          model.zero_grad()\n",
        "          log_probs = model.forward(encoded_input_1, encoded_input_2, train=True)\n",
        "          # print(log_probs)\n",
        "          loss_batch = 0\n",
        "          for idx in range(labels_onehot.shape[0]):\n",
        "              loss_iteration = torch.neg(log_probs[idx]).dot(labels_onehot[idx])\n",
        "              loss_batch += loss_iteration\n",
        "          loss_batch /= labels_onehot.shape[0]\n",
        "          loss_batch.backward()\n",
        "          nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "          optimizer.step()\n",
        "          total_loss += loss_batch.detach()\n",
        "      num_batches = math.ceil(len(X_1) / batch_size)\n",
        "      print(f\"avg loss on epoch {epoch} = {total_loss / num_batches}\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DD0LOlsi3RTy"
      },
      "source": [
        "def get_predictions(model, X_1, X_2, batch_size=8):\n",
        "  all_predictions = np.array([])\n",
        "  for batch in tqdm.notebook.tqdm(range(0, len(X_1), batch_size), leave=False):\n",
        "    encoded_input_1 = tokenizer(X_1[batch:batch + batch_size], return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "    encoded_input_2 = tokenizer(X_2[batch:batch + batch_size], return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "    log_probs = model.forward(encoded_input_1, encoded_input_2, train=False)\n",
        "    prediction_batch = torch.argmax(log_probs, dim=1)\n",
        "    all_predictions = np.concatenate((all_predictions, prediction_batch.cpu().numpy()))\n",
        "  return all_predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DqhWzzM0RIz6"
      },
      "source": [
        "def get_predictions_cosine_similarity(model, tokenizer, X_1, X_2, threshold=0.96, batch_size=8):\n",
        "  all_predictions = np.array([])\n",
        "  for batch in tqdm.notebook.tqdm(range(0, len(X_1), batch_size), leave=False):\n",
        "    encoded_input_1 = tokenizer(X_1[batch:batch + batch_size], return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "    encoded_input_2 = tokenizer(X_2[batch:batch + batch_size], return_tensors='pt', is_split_into_words=False, padding=True)\n",
        "    pooler_output_1 = model(encoded_input_1['input_ids'].cuda(), encoded_input_1['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "    pooler_output_2 = model(encoded_input_2['input_ids'].cuda(), encoded_input_2['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "    cos = nn.CosineSimilarity(dim=1, eps=1e-6)\n",
        "    output = cos(pooler_output_1, pooler_output_2).cpu().detach().numpy()\n",
        "    preds = []\n",
        "    for i in range(output.shape[0]):\n",
        "      if output[i] > threshold:\n",
        "        preds.append(1)\n",
        "      else:\n",
        "        preds.append(0)\n",
        "    all_predictions = np.concatenate((all_predictions, preds))\n",
        "  return all_predictions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AbmYp4m6EV8A"
      },
      "source": [
        "def evaluate(Y, predictions):\n",
        "  print(\"Accuracy: {}\".format(sklearn.metrics.accuracy_score(Y, predictions)))\n",
        "  print(\"F1 score: {}\".format(sklearn.metrics.f1_score(Y, predictions)))\n",
        "  print(\"Precision: {}\".format(sklearn.metrics.precision_score(Y, predictions)))\n",
        "  print(\"Recall: {}\".format(sklearn.metrics.recall_score(Y, predictions)))\n",
        "  print(\"Confusion matrix: \\n{}\\n\".format(sklearn.metrics.confusion_matrix(Y, predictions)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPhUle3035af"
      },
      "source": [
        "## Data Loader"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxcE7t6w8_RZ",
        "outputId": "7b7cea0f-496e-4923-fa27-5d7361c3c31f"
      },
      "source": [
        "# Raw csv\n",
        "# df = pd.read_csv('train.csv')\n",
        "# data = [list(df[\"question1\"]), list(df[\"question2\"]), list(df[\"is_duplicate\"])]\n",
        "\n",
        "# Preprocessed w/o transitivity\n",
        "# with open('/content/drive/MyDrive/processed_data_wo_transitive.pkl', 'rb') as f:\n",
        "#    data = pickle.load(f)\n",
        "\n",
        "# Preprocessed w/ transitivity\n",
        "# Finalized transitivity based on its superiority\n",
        "with open('/content/drive/MyDrive/processed_data_2.pkl', 'rb') as f:\n",
        "   data = pickle.load(f)\n",
        "\n",
        "print(\"Original data has {} question pairs\".format(len(data[0])))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original data has 577240 question pairs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JOX1LlEcK9gz",
        "outputId": "6fd416dc-7f1d-4f4b-8603-65cf50ce47f3"
      },
      "source": [
        "size = len(data[0])\n",
        "dataset = data[:][:size]\n",
        "train_ratio = 0.8\n",
        "indices = list(range(size))\n",
        "random.shuffle(indices)\n",
        "train_indices = indices[:int(size*train_ratio)]\n",
        "test_indices = indices[int(size*train_ratio):]\n",
        "train_dataset = [[dataset[i][j] for j in train_indices] for i in range(len(dataset))]\n",
        "test_dataset = [[dataset[i][j] for j in test_indices] for i in range(len(dataset))]\n",
        "\n",
        "train_input_1 = [\" \".join(train_dataset[0][i]) for i in range(len(train_dataset[0]))]\n",
        "train_input_2 = [\" \".join(train_dataset[1][i]) for i in range(len(train_dataset[1]))]\n",
        "# train_input_1 = [\"\".join(train_dataset[0][i]) for i in range(len(train_dataset[0]))]\n",
        "# train_input_2 = [\"\".join(train_dataset[1][i]) for i in range(len(train_dataset[1]))]\n",
        "train_Y = train_dataset[2]\n",
        "print(len(train_input_1), len(train_input_2), len(train_Y))\n",
        "num_classes = 2\n",
        "\n",
        "test_input_1 = [\" \".join(test_dataset[0][i]) for i in range(len(test_dataset[0]))]\n",
        "test_input_2 = [\" \".join(test_dataset[1][i]) for i in range(len(test_dataset[1]))]\n",
        "test_Y = test_dataset[2]\n",
        "print(len(test_input_1), len(test_input_2), len(test_Y))\n",
        "num_classes = 2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "461792 461792 461792\n",
            "115448 115448 115448\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_IsB8lP3868"
      },
      "source": [
        "## Fine Tune Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YDT4cJCvU0lW"
      },
      "source": [
        "class SimilarityModelFineTuneBert(nn.Module):\n",
        "    def __init__(self, dropout_rate=0.25):\n",
        "        super(SimilarityModelFineTuneBert, self).__init__()\n",
        "        self.bert = DistilBertModel.from_pretrained(\"distilbert-base-uncased\").cuda()\n",
        "        self.dropout = nn.Dropout(p=dropout_rate)\n",
        "        self.feedforward_1 = nn.Linear(768*2, 300).cuda()\n",
        "        self.non_lin_1 = nn.PReLU().cuda()\n",
        "        self.dropout = nn.Dropout(p=dropout_rate)\n",
        "        self.feedforward_2 = nn.Linear(300, 300).cuda()\n",
        "        self.non_lin_2 = nn.PReLU().cuda()\n",
        "        self.dropout = nn.Dropout(p=dropout_rate)\n",
        "        self.feedforward_3 = nn.Linear(300, 2).cuda()\n",
        "        self.log_softmax = nn.LogSoftmax(dim=0).cuda()\n",
        "\n",
        "    def forward(self, encoded_input_1, encoded_input_2, train=False):\n",
        "        if train:\n",
        "          self.train()\n",
        "        else:\n",
        "          self.eval()\n",
        "        pooler_output_1 = self.bert(encoded_input_1['input_ids'].cuda(), encoded_input_1['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "        pooler_output_2 = self.bert(encoded_input_2['input_ids'].cuda(), encoded_input_2['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "        # print(pooler_output_1)\n",
        "        concatenated_output = torch.cat([pooler_output_1, pooler_output_2], axis=1).cuda()\n",
        "        f1 = self.dropout(self.non_lin_1(self.feedforward_1(concatenated_output)))\n",
        "        f2 = self.dropout(self.non_lin_2(self.feedforward_2(f1)))\n",
        "        return self.log_softmax(self.feedforward_3(f2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qiaT9p-6eE5"
      },
      "source": [
        "## Config"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QahDbz0h5nM9"
      },
      "source": [
        "dropout_rate = 0.25\n",
        "batch_size = 128\n",
        "learning_rate = 0.0001\n",
        "num_epochs = 5"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "etpTXf0B6nCd"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353,
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        },
        "id": "L_3DPe8_XZ4r",
        "outputId": "378ce999-0eaf-418d-b3d2-1413c57e932a"
      },
      "source": [
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "print(\"Training fine tune model\")\n",
        "fine_tune_model = SimilarityModelFineTuneBert()\n",
        "train(fine_tune_model, tokenizer, train_input_1, train_input_2, train_Y, learning_rate=learning_rate, num_epochs=num_epochs, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "52de18169f634678b41c8856d0995cd6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0fee845acfad4b30a62c7a955618ab75",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0a3b11976dad477aacee00cf1f1e6e15",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/455k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a512923db7c14ceb904735ef50991f6f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training fine tune model\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "562156b2ad224ce49cca161121087b22",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/256M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.bias', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_transform.weight', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6f9d744d0d3a40abb455a1e96c56a69b",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 0 = 4.696255683898926\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "00a4c2415c85474ca19bdd9232df613f",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 1 = 4.579102039337158\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d0ee500958141a4a5901ff25f7e35c5",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 2 = 4.508677005767822\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d199fafce4d4a8b86ea5f0f017d0fad",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 3 = 4.456218719482422\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "88dd9a6acc354fab87ff49ef2f72a68b",
              "version_minor": 0,
              "version_major": 2
            },
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 4 = 4.418094635009766\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZa2myzhaUxD"
      },
      "source": [
        "with open(\"bert-ffn-fine-tune-pickle.pkl\", 'wb') as f:\n",
        "  pickle.dump(fine_tune_model, f)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vdBiJ9FPaxKr"
      },
      "source": [
        "torch.save(fine_tune_model.state_dict(), 'bert-ffn-fine-tune-state-dict.pt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4iSAUTM6gEm"
      },
      "source": [
        "## Eval"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329,
          "referenced_widgets": [
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        "id": "IARbpfdwYZQ4",
        "outputId": "349ac9c7-9830-4386-fa7b-26b92a10eea7"
      },
      "source": [
        "print(\"Evaluating fine tune model on train dataset\")\n",
        "predictions = get_predictions(fine_tune_model, train_input_1, train_input_2, batch_size=batch_size)\n",
        "evaluate(train_Y, predictions)\n",
        "\n",
        "print(\"Evaluating fine tune bert model on test dataset\")\n",
        "predictions = get_predictions(fine_tune_model, test_input_1, test_input_2, batch_size=batch_size)\n",
        "evaluate(test_Y, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating fine tune model on train dataset\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_major": 2
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9403844154944218\n",
            "F1 score: 0.9187296676565923\n",
            "Precision: 0.8887264336830563\n",
            "Recall: 0.9508294888637683\n",
            "Confusion matrix: \n",
            "[[278654  19483]\n",
            " [  8047 155608]]\n",
            "\n",
            "Evaluating fine tune bert model on test dataset\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "4a7b9bfc93a64336a0b6ea3a6638454e",
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              "  0%|          | 0/902 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8613141847411822\n",
            "F1 score: 0.8138190867123271\n",
            "Precision: 0.7766040080782973\n",
            "Recall: 0.8547803996287068\n",
            "Confusion matrix: \n",
            "[[64444 10066]\n",
            " [ 5945 34993]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WF9-R1WM3WzR"
      },
      "source": [
        "## Cosine Similarity Baseline (Discontinued) "
      ]
    },
    {
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        "colab": {
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        },
        "id": "31Q-VH1vSg5a",
        "outputId": "06ddaea6-4d8b-47ec-dc52-d0bb19f3e4f6"
      },
      "source": [
        "# Stopped working on this after evaluating a baseline\n",
        "\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "vanilla = DistilBertModel.from_pretrained(\"distilbert-base-uncased\").cuda()\n",
        "print(\"Evaluating vanilla model on train dataset\")\n",
        "print(\"NOTE: For vanilla model with cosine based similarity, train-test split doesn't matter\\n\")\n",
        "predictions = get_predictions_cosine_similarity(vanilla, tokenizer, train_input_1, train_input_2, 0.96)\n",
        "evaluate(train_Y, predictions)\n",
        "\n",
        "print(\"Evaluating vanilla model on test dataset\")\n",
        "print(\"NOTE: For vanilla model with cosine based similarity, train-test split doesn't matter\\n\")\n",
        "predictions = get_predictions_cosine_similarity(vanilla, tokenizer, test_input_1, test_input_2, 0.96)\n",
        "evaluate(test_Y, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating vanilla model on train dataset\n",
            "NOTE: For vanilla model with cosine based similarity, train-test split doesn't matter\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/1000 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.598375\n",
            "F1 score: 0.6064911206368647\n",
            "Precision: 0.4746932515337423\n",
            "Recall: 0.8396066463207867\n",
            "Confusion matrix: \n",
            "[[2311 2740]\n",
            " [ 473 2476]]\n",
            "\n",
            "Evaluating vanilla model on test dataset\n",
            "NOTE: For vanilla model with cosine based similarity, train-test split doesn't matter\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8930463e84194902a75eb180af15c99c",
              "version_minor": 0,
              "version_major": 2
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            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6035\n",
            "F1 score: 0.6170931916948333\n",
            "Precision: 0.4881588999236058\n",
            "Recall: 0.8385826771653543\n",
            "Confusion matrix: \n",
            "[[568 670]\n",
            " [123 639]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bNwp4Paj3evl"
      },
      "source": [
        "## Static BERT Model (Discontinued)"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "GMdw0bL5UfOr",
        "outputId": "e8ddbda4-2ce1-404f-a30a-ed4cf88708ad"
      },
      "source": [
        "# Stopped training this after superiority of finetuning BERT was realized\n",
        "\n",
        "class SimilarityModelStaticBert(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(SimilarityModelStaticBert, self).__init__()\n",
        "        self.bert = DistilBertModel.from_pretrained(\"distilbert-base-uncased\").cuda()\n",
        "        for param in self.bert.parameters():\n",
        "            param.requires_grad = False\n",
        "        self.feedforward_1 = nn.Linear(768*2, 300).cuda()\n",
        "        self.non_lin_1 = nn.PReLU().cuda()\n",
        "        self.feedforward_2 = nn.Linear(300, 300).cuda()\n",
        "        self.non_lin_2 = nn.PReLU().cuda()\n",
        "        self.feedforward_3 = nn.Linear(300, 2).cuda()\n",
        "        self.log_softmax = nn.LogSoftmax(dim=0).cuda()\n",
        "\n",
        "    def forward(self, encoded_input_1, encoded_input_2, train=False):\n",
        "        if train:\n",
        "          self.train()\n",
        "        else:\n",
        "          self.eval()\n",
        "        pooler_output_1 = self.bert(encoded_input_1['input_ids'].cuda(), encoded_input_1['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "        pooler_output_2 = self.bert(encoded_input_2['input_ids'].cuda(), encoded_input_2['attention_mask'].cuda()).last_hidden_state[:, 0].cuda()\n",
        "        # print(pooler_output_1)\n",
        "        concatenated_output = torch.cat([pooler_output_1, pooler_output_2], axis=1).cuda()\n",
        "        f1 = self.non_lin_1(self.feedforward_1(concatenated_output))\n",
        "        f2 = self.non_lin_2(self.feedforward_2(f1))\n",
        "        return self.log_softmax(self.feedforward_3(f2))\n",
        "\n",
        "\n",
        "tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')\n",
        "print(\"Training static bert model\")\n",
        "static_bert_model = SimilarityModelStaticBert()\n",
        "train(static_bert_model, tokenizer, train_input_1, train_input_2, train_Y, learning_rate=0.0001, num_epochs=10, batch_size=32)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training static bert model\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-uncased were not used when initializing DistilBertModel: ['vocab_layer_norm.weight', 'vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_projector.bias', 'vocab_transform.bias']\n",
            "- This IS expected if you are initializing DistilBertModel 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 DistilBertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a284cdf0fc6d4cfdbfeacbaad2b57d40",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 0 = 0.10724116116762161\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb24b6cb89da48cebfafc040914fc53b",
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              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 1 = 0.10655724257230759\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 2 = 0.10627871006727219\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 3 = 0.10622921586036682\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b8ef17ad3cfc41a0a537ab286f344b04",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 4 = 0.10600847005844116\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a9a79967cfc943b789ca069a7dd33144",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 5 = 0.10582157224416733\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "431584b9f55b489f81d75f5bf96be4b1",
              "version_minor": 0,
              "version_major": 2
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              "  0%|          | 0/250 [00:00<?, ?it/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 6 = 0.10573685169219971\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ed086ce0a2d74450846362369a456daf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 7 = 0.10562307387590408\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ae40c56036d94f5d83cacb33011cc079",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 8 = 0.10542453825473785\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8969eac82fdd481499b010ff316129ca",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "avg loss on epoch 9 = 0.10536253452301025\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387,
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        "id": "_HQ4Cd9nXInQ",
        "outputId": "d52ac634-d1ef-44c2-dc03-3fe140979b5c"
      },
      "source": [
        "print(\"Evaluating static bert model on train dataset\")\n",
        "predictions = get_predictions(static_bert_model, train_input_1, train_input_2, batch_size=32)\n",
        "evaluate(train_Y, predictions)\n",
        "\n",
        "print(\"Evaluating static bert model on test dataset\")\n",
        "predictions = get_predictions(static_bert_model, test_input_1, test_input_2, batch_size=32)\n",
        "evaluate(test_Y, predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating static bert model on train dataset\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "95a154391b3e4f23ace4ce2fb5f81b0d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.70625\n",
            "F1 score: 0.6392385630948726\n",
            "Precision: 0.5840112201963534\n",
            "Recall: 0.7060020345879959\n",
            "Confusion matrix: \n",
            "[[3568 1483]\n",
            " [ 867 2082]]\n",
            "\n",
            "Evaluating static bert model on test dataset\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7eee3aa2703c48df845a52100d5a7cc3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "  0%|          | 0/63 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6835\n",
            "F1 score: 0.6165960024227742\n",
            "Precision: 0.5725534308211474\n",
            "Recall: 0.6679790026246719\n",
            "Confusion matrix: \n",
            "[[858 380]\n",
            " [253 509]]\n",
            "\n"
          ]
        }
      ]
    }
  ]
}