InferSent.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "InferSent.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm",
      "authorship_tag": "ABX9TyPbWM8ok9Vg5HcCB8h+IX5P",
      "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/Ved2000/NLI-datasets-evaluation/blob/master/InferSent.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mKq-U3JfIiux",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/facebookresearch/InferSent.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HvPRbLS1c4VP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cd /content/InferSent/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A4FtgcUadsvV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "!mkdir fastText\n",
        "!curl -Lo fastText/crawl-300d-2M.vec.zip https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip\n",
        "!unzip fastText/crawl-300d-2M.vec.zip -d fastText/"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UrmNq5lvUOKy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir encoder\n",
        "!curl -Lo encoder/infersent2.pkl https://dl.fbaipublicfiles.com/infersent/infersent2.pkl"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gn9iPIZCdeFh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import nltk\n",
        "nltk.download('punkt')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTQ2Dobfc3KJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bcb60215-9ae6-499b-f03d-bbe31a6ec82e"
      },
      "source": [
        "import torch\n",
        "from models import InferSent\n",
        "V = 2\n",
        "MODEL_PATH = 'encoder/infersent%s.pkl' % V\n",
        "params_model = {'bsize': 64, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,\n",
        "                'pool_type': 'max', 'dpout_model': 0.0, 'version': V}\n",
        "infersent = InferSent(params_model)\n",
        "infersent.load_state_dict(torch.load(MODEL_PATH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WWvFZtufkf5t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "W2V_PATH = 'fastText/crawl-300d-2M.vec'\n",
        "infersent.set_w2v_path(W2V_PATH)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zx8GFXaknrC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e579a170-3cb2-44b3-c241-5b3818d5b7a6"
      },
      "source": [
        "infersent.build_vocab_k_words(100000)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Vocab size : 100000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CPGQd7iGmJXY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1cfad9c4-7b7c-4c3b-b473-2bf041098a4a"
      },
      "source": [
        "infersent.visualize('A man plays an instrument.', tokenize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2020-06-24 15:14:29,948 : findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0.\n",
            "2020-06-24 15:14:29,949 : findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,950 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "2020-06-24 15:14:29,950 : findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,951 : findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,952 : findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,952 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,953 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,954 : findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,955 : findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,956 : findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,956 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,958 : findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,959 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,959 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,960 : findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,961 : findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,964 : findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,964 : findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,965 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,967 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "2020-06-24 15:14:29,968 : findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,968 : findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,969 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,970 : findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,971 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,971 : findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,972 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,973 : findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,974 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "2020-06-24 15:14:29,975 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "2020-06-24 15:14:29,977 : findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,978 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,979 : findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,980 : findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,981 : findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,982 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,983 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,983 : findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:29,984 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,985 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,985 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,986 : findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,987 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "2020-06-24 15:14:29,988 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,988 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,989 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,990 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,990 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "2020-06-24 15:14:29,991 : findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:29,992 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,993 : findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:29,994 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:29,995 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "2020-06-24 15:14:29,996 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "2020-06-24 15:14:29,997 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:29,997 : findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=12.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n",
            "2020-06-24 15:14:30,021 : findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0.\n",
            "2020-06-24 15:14:30,022 : findfont: score(<Font 'cmtt10' (cmtt10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,023 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Oblique.ttf) oblique normal 400 normal>) = 1.05\n",
            "2020-06-24 15:14:30,024 : findfont: score(<Font 'STIXGeneral' (STIXGeneralItalic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,025 : findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,027 : findfont: score(<Font 'DejaVu Serif Display' (DejaVuSerifDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,027 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,028 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,029 : findfont: score(<Font 'STIXGeneral' (STIXGeneralBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,030 : findfont: score(<Font 'STIXGeneral' (STIXGeneral.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,031 : findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,032 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUni.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,032 : findfont: score(<Font 'cmsy10' (cmsy10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,033 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,034 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniIta.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,035 : findfont: score(<Font 'cmss10' (cmss10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,036 : findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,036 : findfont: score(<Font 'STIXSizeFiveSym' (STIXSizFiveSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,037 : findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,038 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-BoldOblique.ttf) oblique normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,039 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-Bold.ttf) normal normal 700 normal>) = 0.33499999999999996\n",
            "2020-06-24 15:14:30,040 : findfont: score(<Font 'STIXSizeTwoSym' (STIXSizTwoSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,040 : findfont: score(<Font 'STIXSizeFourSym' (STIXSizFourSymBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,041 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Oblique.ttf) oblique normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,042 : findfont: score(<Font 'DejaVu Sans Display' (DejaVuSansDisplay.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,043 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBolIta.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,045 : findfont: score(<Font 'cmr10' (cmr10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,047 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,048 : findfont: score(<Font 'STIXGeneral' (STIXGeneralBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,049 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans.ttf) normal normal 400 normal>) = 0.05\n",
            "2020-06-24 15:14:30,050 : findfont: score(<Font 'DejaVu Sans' (DejaVuSans-BoldOblique.ttf) oblique normal 700 normal>) = 1.335\n",
            "2020-06-24 15:14:30,054 : findfont: score(<Font 'cmmi10' (cmmi10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,055 : findfont: score(<Font 'DejaVu Serif' (DejaVuSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,056 : findfont: score(<Font 'STIXSizeOneSym' (STIXSizOneSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,057 : findfont: score(<Font 'cmex10' (cmex10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,058 : findfont: score(<Font 'cmb10' (cmb10.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,059 : findfont: score(<Font 'STIXNonUnicode' (STIXNonUniBol.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,060 : findfont: score(<Font 'DejaVu Sans Mono' (DejaVuSansMono.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,060 : findfont: score(<Font 'STIXSizeThreeSym' (STIXSizThreeSymReg.ttf) normal normal regular normal>) = 10.05\n",
            "2020-06-24 15:14:30,061 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,062 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,063 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,064 : findfont: score(<Font 'Liberation Sans' (LiberationSans-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,065 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-BoldItalic.ttf) italic normal 700 condensed>) = 11.535\n",
            "2020-06-24 15:14:30,066 : findfont: score(<Font 'Liberation Sans' (LiberationSans-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,066 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,067 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,068 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,069 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Regular.ttf) normal normal 400 condensed>) = 10.25\n",
            "2020-06-24 15:14:30,070 : findfont: score(<Font 'Liberation Mono' (LiberationMono-BoldItalic.ttf) italic normal 700 normal>) = 11.335\n",
            "2020-06-24 15:14:30,070 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Regular.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,072 : findfont: score(<Font 'Humor Sans' (Humor-Sans.ttf) normal normal 400 normal>) = 10.05\n",
            "2020-06-24 15:14:30,073 : findfont: score(<Font 'Liberation Serif' (LiberationSerif-Italic.ttf) italic normal 400 normal>) = 11.05\n",
            "2020-06-24 15:14:30,074 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Bold.ttf) normal normal 700 condensed>) = 10.535\n",
            "2020-06-24 15:14:30,074 : findfont: score(<Font 'Liberation Sans Narrow' (LiberationSansNarrow-Italic.ttf) italic normal 400 condensed>) = 11.25\n",
            "2020-06-24 15:14:30,075 : findfont: score(<Font 'Liberation Mono' (LiberationMono-Bold.ttf) normal normal 700 normal>) = 10.335\n",
            "2020-06-24 15:14:30,076 : findfont: Matching sans\\-serif:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0 to DejaVu Sans ('/usr/local/lib/python3.6/dist-packages/matplotlib/mpl-data/fonts/ttf/DejaVuSans.ttf') with score of 0.050000.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[ 0.0075,  0.1772,  0.1542,  ..., -0.0334, -0.0174,  0.0108]],\n",
              "        grad_fn=<MaxBackward0>), array([[0, 2, 5, ..., 2, 4, 5]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qcuEiMxWodpt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c837e4d5-6747-4b4c-8de4-2afc7bf17156"
      },
      "source": [
        "cd /content"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivCY8E-soq5Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/facebookresearch/SentEval.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ho8da0qo1k4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "27576a65-d0d1-4f73-d699-cd5bf323426d"
      },
      "source": [
        "cd /content/SentEval/data/downstream/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/SentEval/data/downstream\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NLc85_V2t_1Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!./get_transfer_data.bash"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qzDADZNsvtFt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1568b6e1-e5da-45c7-9243-fab3a90cfd82"
      },
      "source": [
        "cd /content/SentEval/data/downstream/SICK"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/SentEval/data/downstream/SICK\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G5DVd0uHXioH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2df11227-f349-4f7e-eba4-2b0b50938fb0"
      },
      "source": [
        "cd /content/InferSent"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/InferSent\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XT_QrbjZwB9h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Copyright (c) 2017-present, Facebook, Inc.\n",
        "# All rights reserved.\n",
        "#\n",
        "# This source code is licensed under the license found in the\n",
        "# LICENSE file in the root directory of this source tree.\n",
        "#\n",
        "\n",
        "\"\"\"\n",
        "InferSent models. See https://github.com/facebookresearch/InferSent.\n",
        "\"\"\"\n",
        "\n",
        "from __future__ import absolute_import, division, unicode_literals\n",
        "\n",
        "import sys\n",
        "import os\n",
        "import torch\n",
        "import logging\n",
        "\n",
        "# get models.py from InferSent repo\n",
        "\n",
        "from models import InferSent\n",
        "\n",
        "# Set PATHs\n",
        "PATH_SENTEVAL = '/content/SentEval'\n",
        "PATH_TO_DATA = '/content/SentEval/data'\n",
        "PATH_TO_W2V = '/content/InferSent/fastText/crawl-300d-2M.vec'  # or crawl-300d-2M.vec for V2\n",
        "MODEL_PATH = '/content/InferSent/encoder/infersent2.pkl'\n",
        "V = 2 # version of InferSent\n",
        "\n",
        "assert os.path.isfile(MODEL_PATH) and os.path.isfile(PATH_TO_W2V), \\\n",
        "    'Set MODEL and GloVe PATHs'\n",
        "\n",
        "# import senteval\n",
        "sys.path.insert(0, PATH_SENTEVAL)\n",
        "import senteval\n",
        "\n",
        "\n",
        "def prepare(params, samples):\n",
        "    params.infersent.build_vocab([' '.join(s) for s in samples], tokenize=False)\n",
        "\n",
        "\n",
        "def batcher(params, batch):\n",
        "    sentences = [' '.join(s) for s in batch]\n",
        "    embeddings = params.infersent.encode(sentences, bsize=params.batch_size, tokenize=False)\n",
        "    return embeddings\n",
        "\n",
        "\n",
        "\"\"\"\n",
        "Evaluation of trained model on Transfer Tasks (SentEval)\n",
        "\"\"\"\n",
        "\n",
        "# define senteval params\n",
        "params_senteval = {'task_path': PATH_TO_DATA, 'usepytorch': True, 'kfold': 5}\n",
        "params_senteval['classifier'] = {'nhid': 0, 'optim': 'rmsprop', 'batch_size': 8,\n",
        "                                 'tenacity': 3, 'epoch_size': 2}\n",
        "# Set up logger\n",
        "logging.basicConfig(format='%(asctime)s : %(message)s', level=logging.DEBUG)\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    # Load InferSent model\n",
        "    params_model = {'bsize': 8, 'word_emb_dim': 300, 'enc_lstm_dim': 2048,\n",
        "                    'pool_type': 'max', 'dpout_model': 0.0, 'version': V}\n",
        "    model = InferSent(params_model)\n",
        "    model.load_state_dict(torch.load(MODEL_PATH))\n",
        "    model.set_w2v_path(PATH_TO_W2V)\n",
        "\n",
        "    params_senteval['infersent'] = model.cuda()\n",
        "\n",
        "    se = senteval.engine.SE(params_senteval, batcher, prepare)\n",
        "    transfer_tasks = ['SICKEntailment']\n",
        "    results = se.eval(transfer_tasks)\n",
        "    print(results)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RBmt-8h7AqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f87af919-0903-4fd7-c8e8-83f0fb3d2e14"
      },
      "source": [
        "cd /content/"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pQj0SMrfdgnJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9aacab96-d5b7-45b4-cc44-8a50975d2367"
      },
      "source": [
        "sentences1 = []\n",
        "with open('s1.train') as f:\n",
        "    for line in f:\n",
        "        sentences1.append(line.strip())\n",
        "print(len(sentences1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "559191\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o5L1HgHIb_jY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sent=[]\n",
        "for i in range(100000):\n",
        "  sent.append(sentences[i])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jPgrknSX7es9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "e2a2b19a-e1a8-47b4-98b1-13629fe29fc4"
      },
      "source": [
        "infersent=infersent.cuda()\n",
        "a[:,1,:] = infersent.encode(sent, bsize=128, tokenize=False, verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nb words kept : 1585794/1595547 (99.4%)\n",
            "Speed : 4203.1 sentences/s (gpu mode, bsize=128)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zbVIUJBQt9jI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b25678ba-ed6a-4149-c077-11752fbc3c3c"
      },
      "source": [
        "a[99999,1,4]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.041592713445425034"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d4VgrXj3kqGt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "a=np.zeros((100000,2,4096))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLhnIIrflPl8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "b=np.ones((100000,4096))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b3AAICpqAXIV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 162
        },
        "outputId": "28ba4fb1-2897-405f-8905-19c0d2ba2838"
      },
      "source": [
        "type(embeddings)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-1957036a04f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'embeddings' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7H9TFZzlga7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a[:,1,:]=b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "heZ0pwCBlsGL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del b"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brjnjsFVA6z6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "d7cd607d-a0f0-49ff-a3c0-1b04210ca323"
      },
      "source": [
        "with open(\"s1.encodings\", \"w\") as txt_file:\n",
        "    for line in embeddings:\n",
        "        txt_file.write(\" \".join(line) + \"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-00176b762a8a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"s1.encodings\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"w\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtxt_file\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0membeddings\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      3\u001b[0m         \u001b[0mtxt_file\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'embeddings' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxyYh-AFBub2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "55df8692-09cd-4ea4-f572-33b2c502eddf"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "np.savetxt(\"s1.encodings\",embeddings, fmt=\"%s\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-14-ebbd3d769cac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msavetxt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"s1.encodings\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"%s\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py\u001b[0m in \u001b[0;36msavetxt\u001b[0;34m(fname, X, fmt, delimiter, newline, header, footer, comments, encoding)\u001b[0m\n\u001b[1;32m   1445\u001b[0m             \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1446\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1447\u001b[0;31m                     \u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnewline\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1448\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1449\u001b[0m                     raise TypeError(\"Mismatch between array dtype ('%s') and \"\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cKJmf-aFCf_y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e9fa5a16-cfb3-42bc-9e6c-396e79c30ab8"
      },
      "source": [
        "sentences2 = []\n",
        "with open('s2.train') as f:\n",
        "    for line in f:\n",
        "        sentences2.append(line.strip())\n",
        "print(len(sentences2))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "559191\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aYhKJTUluKbY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sent1=[]\n",
        "for i in range(100000):\n",
        "  sent1.append(sentences1[i])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rfVu8zUuYS8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "8043edac-698b-45b7-f296-7b9b2c8ba8ea"
      },
      "source": [
        "a[:,0,:] = infersent.encode(sent1, bsize=128, tokenize=False, verbose=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nb words kept : 1016055/1021276 (99.5%)\n",
            "Speed : 5874.7 sentences/s (gpu mode, bsize=128)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nptLrE0Vw1Ad",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "426e54e9-59c0-4240-d68b-9bf0ce72d14b"
      },
      "source": [
        "labels= []\n",
        "with open('labels.train') as f:\n",
        "    for line in f:\n",
        "        labels.append(line.strip())\n",
        "print(len(labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "559191\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x5hvJDPuxH5c",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lab=[]\n",
        "for i in range(100000):\n",
        "  lab.append(labels[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2eG9G-zxdEo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da5f82d6-1580-4a56-8e93-3f8d4fa4f79d"
      },
      "source": [
        "len(lab)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vc_bVOFnxeY7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d04d6e11-2aab-4384-dc71-8b1d5e4fb6d6"
      },
      "source": [
        "type(lab[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "elZ46NzNxiN8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lab=np.asfarray(lab,float)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j6XGHV-wyCwZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "15bb16ec-2b7b-4262-8290-3b5cd72005f3"
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "clf = LogisticRegression(random_state=0).fit(a, lab)\n",
        "clf.score(X, y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-8b5667196d43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatasets\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_iris\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_model\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlab\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      4\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m   1525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1526\u001b[0m         X, y = check_X_y(X, y, accept_sparse='csr', dtype=_dtype, order=\"C\",\n\u001b[0;32m-> 1527\u001b[0;31m                          accept_large_sparse=solver != 'liblinear')\n\u001b[0m\u001b[1;32m   1528\u001b[0m         \u001b[0mcheck_classification_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1529\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    753\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    754\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 755\u001b[0;31m                     estimator=estimator)\n\u001b[0m\u001b[1;32m    756\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    757\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    572\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    573\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[0;32m--> 574\u001b[0;31m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Found array with dim 3. Estimator expected <= 2."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LCOMHt_UyDvI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        },
        "outputId": "f491859f-5df9-40d0-f9b3-77d2b207e22f"
      },
      "source": [
        " import numpy as np\n",
        " enc_input=[]\n",
        " enc_input.append(np.hstack((a[:,1,:], a[:,0,:], a[:,1,:]*a[:,0,:],np.abs(a[:,1,:] - a[:,0,:]))))\n",
        " print(enc_input)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
            "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3yuo5T0PFwpU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "infersent=infersent.cuda()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CpEEHzGK9KMC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_0rcCXSCy3C",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 857
        },
        "outputId": "5e066463-c947-4605-8661-756eb0f7303f"
      },
      "source": [
        "result=[]\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import preprocessing\n",
        "for i in range(10):\n",
        "  sent1=[]\n",
        "  sent2=[]\n",
        "  lab=[]\n",
        "  for j in range(i*50000,(i+1)*50000):\n",
        "    sent1.append(sentences1[j])\n",
        "    sent2.append(sentences2[j])\n",
        "    lab.append(labels[j])\n",
        "  embed1=infersent.encode(sent1, bsize=128, tokenize=False, verbose=True)\n",
        "  embed2=infersent.encode(sent2, bsize=128, tokenize=False, verbose=True)\n",
        "  del sent1\n",
        "  del sent2\n",
        "  enc=[]\n",
        "  enc.append(np.hstack((embed1, embed2, embed1 * embed2 ,np.abs(embed1 - embed2))))\n",
        "  del embed1\n",
        "  del embed2\n",
        "  enc_input=np.vstack(enc)\n",
        "  del enc\n",
        "  scaler = preprocessing.StandardScaler().fit(enc_input)\n",
        "  enc_input=scaler.transform(enc_input)\n",
        "  clf = LogisticRegression(random_state=0,max_iter=10000).fit(enc_input, lab)\n",
        "  #np.append(result,clf.predict(enc_input))\n",
        "  A=clf.predict(enc_input)\n",
        "  result.append(A)\n",
        "  A=np.array(A)\n",
        "  np.savetxt(\"result.txt\",A, fmt=\"%s\")\n",
        "  del enc_input\n",
        "  del A\n",
        "  print(i)\n",
        "\n",
        "\n",
        "  \n",
        "  \n",
        "  \n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nb words kept : 796386/801243 (99.4%)\n",
            "Speed : 4314.4 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 509529/512071 (99.5%)\n",
            "Speed : 6460.2 sentences/s (gpu mode, bsize=128)\n",
            "0\n",
            "Nb words kept : 789408/794304 (99.4%)\n",
            "Speed : 4355.2 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 506526/509205 (99.5%)\n",
            "Speed : 6272.2 sentences/s (gpu mode, bsize=128)\n",
            "1\n",
            "Nb words kept : 800487/805327 (99.4%)\n",
            "Speed : 4332.6 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 508782/511382 (99.5%)\n",
            "Speed : 6202.2 sentences/s (gpu mode, bsize=128)\n",
            "2\n",
            "Nb words kept : 791890/796886 (99.4%)\n",
            "Speed : 4396.1 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 510786/513183 (99.5%)\n",
            "Speed : 6218.4 sentences/s (gpu mode, bsize=128)\n",
            "3\n",
            "Nb words kept : 795308/800212 (99.4%)\n",
            "Speed : 4303.5 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 510289/512723 (99.5%)\n",
            "Speed : 6267.8 sentences/s (gpu mode, bsize=128)\n",
            "4\n",
            "Nb words kept : 801012/806258 (99.3%)\n",
            "Speed : 4289.4 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 510862/513413 (99.5%)\n",
            "Speed : 6261.1 sentences/s (gpu mode, bsize=128)\n",
            "5\n",
            "Nb words kept : 795782/800951 (99.4%)\n",
            "Speed : 4351.0 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 511322/513855 (99.5%)\n",
            "Speed : 6180.9 sentences/s (gpu mode, bsize=128)\n",
            "6\n",
            "Nb words kept : 796263/801714 (99.3%)\n",
            "Speed : 4346.0 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 510850/513406 (99.5%)\n",
            "Speed : 6154.1 sentences/s (gpu mode, bsize=128)\n",
            "7\n",
            "Nb words kept : 798371/803080 (99.4%)\n",
            "Speed : 4260.1 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 511207/513826 (99.5%)\n",
            "Speed : 6183.6 sentences/s (gpu mode, bsize=128)\n",
            "8\n",
            "Nb words kept : 797389/802408 (99.4%)\n",
            "Speed : 4276.3 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 509404/512038 (99.5%)\n",
            "Speed : 6211.7 sentences/s (gpu mode, bsize=128)\n",
            "9\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vlld_HNvWqAE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "f49d6740-1aef-4f3c-d0be-9e5f5abb5995"
      },
      "source": [
        "sent1=[]\n",
        "sent2=[]\n",
        "lab=[]\n",
        "for j in range(500000,559191):\n",
        "  sent1.append(sentences1[j])\n",
        "  sent2.append(sentences2[j])\n",
        "  lab.append(labels[j])\n",
        "embed1=infersent.encode(sent1, bsize=128, tokenize=False, verbose=True)\n",
        "embed2=infersent.encode(sent2, bsize=128, tokenize=False, verbose=True)\n",
        "del sent1\n",
        "del sent2\n",
        "enc=[]\n",
        "enc.append(np.hstack((embed1, embed2, embed1 * embed2 ,np.abs(embed1 - embed2))))\n",
        "del embed1\n",
        "del embed2\n",
        "enc_input=np.vstack(enc)\n",
        "del enc\n",
        "scaler = preprocessing.StandardScaler().fit(enc_input)\n",
        "enc_input=scaler.transform(enc_input)\n",
        "clf = LogisticRegression(random_state=0,max_iter=10000).fit(enc_input, lab)\n",
        "A=clf.predict(enc_input)\n",
        "result.append(A)\n",
        "A=np.array(A)\n",
        "np.savetxt(\"result.txt\",A, fmt=\"%s\")\n",
        "del enc_input\n",
        "del A\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Nb words kept : 955995/961812 (99.4%)\n",
            "Speed : 4261.6 sentences/s (gpu mode, bsize=128)\n",
            "Nb words kept : 605007/607970 (99.5%)\n",
            "Speed : 6368.1 sentences/s (gpu mode, bsize=128)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idwssrtwWuy2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "976ac7e7-6e02-4b7f-99f8-6b10d815c55e"
      },
      "source": [
        "len(result)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "11"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJNotJpwXgse",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97fa83e5-67cd-46ae-9a02-07576295f983"
      },
      "source": [
        "!wc -w result.txt"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "59191 result.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xL7Ca8IUEHkk",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "4d9048c1-1123-4c17-dea2-b04ab4c76c12"
      },
      "source": [
        "result[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['0', '0', '1', ..., '1', '0', '1'], dtype='<U1')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EIB6H2_FEr9V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "g=[]\n",
        "for i in range(11):\n",
        "  for j in result[i]:\n",
        "    g.append(j)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U3kfAGJeFBWG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f25ea07a-b60e-48f3-8353-f82b4d5f9285"
      },
      "source": [
        "len(g)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "559191"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4ahMKUcFDEq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "g=np.array(g)\n",
        "np.savetxt(\"result_infersent.txt\",g, fmt=\"%s\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JCjziPi-FOt3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}