SemEval2020_Paraphraser.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "SemEval2020_Paraphraser.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lenyabloko/SemEval2020/blob/master/SemEval2020_Paraphraser.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1VkwsR6Tp2K",
        "colab_type": "text"
      },
      "source": [
        "UPLOAD FILES - Place [train.csv](https://github.com/arielsho/Subtask-1/archive/master.zip) and [test.csv](https://github.com/arielsho/Subtask-1-test/archive/master.zip) files directly under your `gdrive/My Drive/Subtask-1/`, before starting (follow the prompt URL and get authentication token)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p3GfRjjaUfMv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "outputId": "f8e1c999-5c22-47e1-ccd5-8d10ca3c148d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "#!cp /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/train.csv /content\n",
        "#!cp /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/test.csv /content\n",
        "\n",
        "!cp -r /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/train-20180325-001253 /content\n",
        "!cp -r /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/para-nmt-50m /content\n",
        "!cp -r /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/paraphraser /content"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ae2kyrtL-RKk",
        "colab_type": "text"
      },
      "source": [
        "START AGAIN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUAYE7zYdE0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "outputId": "d49012f3-edb1-4b7f-a198-2762c2a4bb86"
      },
      "source": [
        "!pip install tensorflow\n",
        "!pip install numpy\n",
        "\n",
        "import os\n",
        "os.environ['PYTHONPATH'] = \"/usr/local/bin/python\"\n",
        "!echo $PYTHONPATH\n",
        "!which python\n",
        "!python --version\n",
        "\n",
        "import sys\n",
        "sys.path.append('/usr/local/lib/python3.6/site-packages/')\n",
        "\n",
        "#%tensorflow_version 2.x\n",
        "#import tensorflow\n",
        "#print(tensorflow.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow in /usr/local/lib/python3.6/dist-packages (1.15.0)\n",
            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.17.5)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.27.1)\n",
            "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.12.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.34.2)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.1.8)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.0.8)\n",
            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.2.2)\n",
            "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.15.1)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (3.10.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.8.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (1.11.2)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow) (0.9.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (1.0.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow) (45.1.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow) (2.8.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (1.17.5)\n",
            "/usr/local/bin/python\n",
            "/usr/local/bin/python\n",
            "Python 3.6.9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z-U_LDG7_oBe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Restart runtime using 'Runtime' -> 'Restart runtime...'\n",
        "%tensorflow_version 1.x\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "acT8MshjdY6p",
        "colab_type": "text"
      },
      "source": [
        "TEST INTERACTIVE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g5M-rL7BoSo0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1ab86829-f6d0-4aa4-8052-9ded37cc261b"
      },
      "source": [
        "#from paraphraser import inference\n",
        "#paraphrase = inference.greedy_paraphrase(\"Hello Woeldpa\")\n",
        "#print(paraphrase)\n",
        "\n",
        "#!export LC_ALL=en_US.UTF-8\n",
        "#!export LANG=en_US.UTF-8\n",
        "#!sudo python -m spacy download en\n",
        "\n",
        "!python -m spacy download en_core_web_sm\n",
        "!python -m spacy download en\n",
        "\n",
        "!python paraphraser/paraphraser/inference.py --checkpoint=/content/train-20180325-001253/model-171856"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: en_core_web_sm==2.1.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.1.0/en_core_web_sm-2.1.0.tar.gz#egg=en_core_web_sm==2.1.0 in /usr/local/lib/python3.6/dist-packages (2.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "Requirement already satisfied: en_core_web_sm==2.1.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.1.0/en_core_web_sm-2.1.0.tar.gz#egg=en_core_web_sm==2.1.0 in /usr/local/lib/python3.6/dist-packages (2.1.0)\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the model via spacy.load('en_core_web_sm')\n",
            "\u001b[38;5;2m✔ Linking successful\u001b[0m\n",
            "/usr/local/lib/python3.6/dist-packages/en_core_web_sm -->\n",
            "/usr/local/lib/python3.6/dist-packages/spacy/data/en\n",
            "You can now load the model via spacy.load('en')\n",
            "WARNING:tensorflow:From paraphraser/paraphraser/inference.py:25: The name tf.GPUOptions is deprecated. Please use tf.compat.v1.GPUOptions instead.\n",
            "\n",
            "WARNING:tensorflow:From paraphraser/paraphraser/inference.py:26: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From paraphraser/paraphraser/inference.py:26: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "2020-02-29 08:29:51.652439: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2300000000 Hz\n",
            "2020-02-29 08:29:51.654668: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x66f9340 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
            "2020-02-29 08:29:51.654701: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version\n",
            "2020-02-29 08:29:51.659283: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1\n",
            "2020-02-29 08:29:51.845992: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:51.847362: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x66f9500 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
            "2020-02-29 08:29:51.847389: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\n",
            "2020-02-29 08:29:51.848862: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:51.849680: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: \n",
            "name: Tesla P100-PCIE-16GB major: 6 minor: 0 memoryClockRate(GHz): 1.3285\n",
            "pciBusID: 0000:00:04.0\n",
            "2020-02-29 08:29:51.849796: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-02-29 08:29:51.849865: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10\n",
            "2020-02-29 08:29:51.849918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10\n",
            "2020-02-29 08:29:51.849974: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10\n",
            "2020-02-29 08:29:51.850027: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10\n",
            "2020-02-29 08:29:51.850110: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10\n",
            "2020-02-29 08:29:51.850172: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7\n",
            "2020-02-29 08:29:51.850297: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:51.851146: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:51.851932: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0\n",
            "2020-02-29 08:29:51.855842: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
            "2020-02-29 08:29:52.086151: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2020-02-29 08:29:52.086273: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165]      0 \n",
            "2020-02-29 08:29:52.086295: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0:   N \n",
            "2020-02-29 08:29:52.088788: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:52.089955: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:983] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2020-02-29 08:29:52.091123: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2020-02-29 08:29:52.091186: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8140 MB memory) -> physical GPU (device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0)\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:13: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:14: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:20: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:23: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n",
            "\n",
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:46: BasicLSTMCell.__init__ (from tensorflow.python.ops.rnn_cell_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "This class is equivalent as tf.keras.layers.LSTMCell, and will be replaced by that in Tensorflow 2.0.\n",
            "WARNING:tensorflow:From /content/paraphraser/paraphraser/lstm_model.py:52: bidirectional_dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `keras.layers.Bidirectional(keras.layers.RNN(cell))`, which is equivalent to this API\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn.py:464: dynamic_rnn (from tensorflow.python.ops.rnn) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `keras.layers.RNN(cell)`, which is equivalent to this API\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn_cell_impl.py:735: Layer.add_variable (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.add_weight` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn_cell_impl.py:739: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/rnn.py:244: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/contrib/seq2seq/python/ops/helper.py:107: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.random.categorical` instead.\n",
            "WARNING:tensorflow:From paraphraser/paraphraser/inference.py:28: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.\n",
            "\n",
            "Source: "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxHhsazAc6o7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow\n",
        "\n",
        "import os\n",
        "import sys\n",
        "sys.path.append(os.getcwd()+'/paraphraser/paraphraser/')\n",
        "\n",
        "import re\n",
        "\n",
        "source_sentence=\"Folks might not have felt like paying $10 a ticket to see films that have proven to be rather disappointing at the box office, like Cinderella Man or Kingdomof Heaven, but it stands to reason -- as has always been the case -- that many people will probably rent them, or even buy them, on DVD.\"\n",
        "source_sentence= re.sub(r\"[,.;@#?!&]+\\ *\", \" \", source_sentence)\n",
        "print(source_sentence)\n",
        "\n",
        "from inference import *\n",
        "\n",
        "# for re-run - Restart runtime \n",
        "paraphraser = Paraphraser(checkpoint='/content/train-20180325-001253/model-171856')\n",
        "\n",
        "paraphrases = paraphraser.sample_paraphrase(sentence=source_sentence, sampling_temp=0.75, how_many=3) \n",
        "for i, paraphrase in enumerate(paraphrases):\n",
        "  print(\"Paraph #{}: {}\".format(i, paraphrase))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1aTA5OXdvqn",
        "colab_type": "text"
      },
      "source": [
        "FORMAT DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WataucfriZ9S",
        "colab_type": "code",
        "outputId": "4424192e-6d9c-4a5f-b09f-e738d9e54acf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "prefix = '/content/'\n",
        "train_df = pd.read_csv(prefix + 'train.csv', header=None)\n",
        "train_df=train_df.drop(index=0)\n",
        "\n",
        "train_df = pd.DataFrame({\n",
        "    'id':train_df[0],\n",
        "    'labels':train_df[1],\n",
        "    'alpha':['a']*train_df.shape[0],\n",
        "    'text': train_df[2].replace(r'\\n', ' ', regex=True)\n",
        "})\n",
        "train_df.head() "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>labels</th>\n",
              "      <th>alpha</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>100000</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>Goodfellow's theory has been questioned, howev...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>100001</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>However, both campaigners and pro-People's Vot...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>100002</td>\n",
              "      <td>1</td>\n",
              "      <td>a</td>\n",
              "      <td>Things could have been even better if the whol...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>100003</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>The new request, if approved, would keep the m...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>100004</td>\n",
              "      <td>0</td>\n",
              "      <td>a</td>\n",
              "      <td>Companies in financial difficulty can currentl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       id labels alpha                                               text\n",
              "1  100000      1     a  Goodfellow's theory has been questioned, howev...\n",
              "2  100001      1     a  However, both campaigners and pro-People's Vot...\n",
              "3  100002      1     a  Things could have been even better if the whol...\n",
              "4  100003      0     a  The new request, if approved, would keep the m...\n",
              "5  100004      0     a  Companies in financial difficulty can currentl..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F2kZ7FgfjU7I",
        "colab_type": "code",
        "outputId": "b82b74af-29c5-443b-e456-102642bce8ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        }
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "count = train_df['id'].count()\n",
        "print(count)\n",
        "\n",
        "import os\n",
        "import sys\n",
        "sys.path.append(os.getcwd()+'/paraphraser/paraphraser/')\n",
        "\n",
        "from inference import *\n",
        "# if error next - Restart runtime to avoid re-initializing Paraphraser !!!\n",
        "paraphraser = Paraphraser(checkpoint='/content/train-20180325-001253/model-171856')\n",
        "\n",
        "import re\n",
        "import time\n",
        "from tqdm import tqdm\n",
        "\n",
        "from inference import *\n",
        "\n",
        "duplicate_df = train_df.copy()\n",
        "paraphrased = []\n",
        "\n",
        "for ind in tqdm(train_df.index):\n",
        "  #print(\"Original: {}\".format(train_df['text'][ind])) \n",
        "  original_sentence = train_df['text'][ind]\n",
        "\n",
        "  source_sentence= re.sub(r\"[~=+-\\\",:;#!&()0]+\\ *\", \" \", original_sentence)\n",
        "  paraphrases = paraphraser.sample_paraphrase(sentence=source_sentence, sampling_temp=0.75, how_many=3)\n",
        "\n",
        "  for i, paraphrase in enumerate(paraphrases):\n",
        "    duplicate = duplicate_df['id'] == train_df['id'][ind] # get identical row from duplicate dataframe\n",
        "    duplicate['text'] = paraphrase\n",
        "    #print(\"Paraph #{}: {}\".format(i,duplicate['text']))\n",
        "    #paraphrased.append(duplicate) \n",
        "  time.sleep(1)\n",
        "\n",
        "print(\"Done\")\n",
        "train_df.append(paraphrased, ignore_index=True, sort=False) \n",
        "train_df.to_csv(prefix+'train.tsv', sep='\\t', index=False, header=False)  "
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13000\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-8-846d7c18e06e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetcwd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m'/paraphraser/paraphraser/'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0minference\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;31m# if error next - Restart runtime to avoid re-initializing Paraphraser !!!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0mparaphraser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mParaphraser\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'/content/train-20180325-001253/model-171856'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'inference'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-JMlb9ofySNj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cp /content/train.tsv /content/gdrive/My\\ Drive/Colab\\ Notebooks/Subtask-1/"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}