Tokenizer.ipynb

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        "<a href=\"https://colab.research.google.com/github/Ishan-Kotian/Tokenizer_NLP/blob/main/Tokenizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yOXicuuz-NCw"
      },
      "source": [
        "# Tokenization in NLP\n",
        "Tokenization is essentially splitting a phrase, sentence, paragraph, or an entire text document into smaller units, such as individual words or terms. Each of these smaller units are called tokens.\n",
        "\n",
        "\n",
        "# Why is Tokenization required in NLP?\n",
        "Before processing a natural language, we need to identify the words that constitute a string of characters. That’s why tokenization is the most basic step to proceed with NLP (text data). This is important because the meaning of the text could easily be interpreted by analyzing the words present in the text.\n",
        "\n",
        "Let’s take an example. Consider the below string: “This is a cat.”\n",
        "\n",
        "What do you think will happen after we perform tokenization on this string? We get [‘This’, ‘is’, ‘a’, cat’].\n",
        "\n",
        "There are numerous uses of doing this. We can use this tokenized form to: Count the number of words in the text Count the frequency of the word, that is, the number of times a particular word is present And so on. We can extract a lot more information which we’ll discuss in detail in future articles. For now, it’s time to dive into the meat of this article – the different methods of performing tokenization in NLP."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVSEe2ppmzVy"
      },
      "source": [
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "\n",
        "sentence_list = [\n",
        "                 \"I Love machine learning\",\n",
        "                 \"I love deep learning\",\n",
        "                 \"We are learning deep learning\"\n",
        "]"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
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        },
        "id": "RRZ2uQW2odA7",
        "outputId": "ecf477b0-1468-4ff8-eafc-b59b3348e380"
      },
      "source": [
        "tokenizer = Tokenizer(num_words = 50)\n",
        "tokenizer.fit_on_texts(sentence_list)\n",
        "word_index = tokenizer.word_index\n",
        "print(word_index)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'learning': 1, 'i': 2, 'love': 3, 'deep': 4, 'machine': 5, 'we': 6, 'are': 7}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "56in6yWpo-Bk",
        "outputId": "0bba991d-9d66-4e72-afe4-5ef55c8d81c0"
      },
      "source": [
        "sentence_list = [\n",
        "                 \"I Love machine learning\",\n",
        "                 \"I love deep learning\",\n",
        "                 \"We are learning deep learning\",\n",
        "                 \"We are learning from best trainer\"\n",
        "]\n",
        "\n",
        "tokenizer = Tokenizer(num_words = 50)\n",
        "tokenizer.fit_on_texts(sentence_list)\n",
        "word_index = tokenizer.word_index\n",
        "print(word_index)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentence_list)\n",
        "for s in sequences : print(sequences)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'learning': 1, 'i': 2, 'love': 3, 'deep': 4, 'we': 5, 'are': 6, 'machine': 7, 'from': 8, 'best': 9, 'trainer': 10}\n",
            "[[2, 3, 7, 1], [2, 3, 4, 1], [5, 6, 1, 4, 1], [5, 6, 1, 8, 9, 10]]\n",
            "[[2, 3, 7, 1], [2, 3, 4, 1], [5, 6, 1, 4, 1], [5, 6, 1, 8, 9, 10]]\n",
            "[[2, 3, 7, 1], [2, 3, 4, 1], [5, 6, 1, 4, 1], [5, 6, 1, 8, 9, 10]]\n",
            "[[2, 3, 7, 1], [2, 3, 4, 1], [5, 6, 1, 4, 1], [5, 6, 1, 8, 9, 10]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ucA8lS21p6zT",
        "outputId": "f5dc6b1a-b8f2-43eb-f6ea-f38100c2b23b"
      },
      "source": [
        "new = [\"The world is screwed, so are we\"]\n",
        "seq = tokenizer.texts_to_sequences(new)\n",
        "print(seq)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[6, 5]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4nPNkE8rAZs",
        "outputId": "727babb0-e07f-4dc9-f0d3-257a6ceeaa17"
      },
      "source": [
        "sentence_list = [\n",
        "                 \"I Love machine learning\",\n",
        "                 \"I love deep learning\",\n",
        "                 \"We are learning deep learning\",\n",
        "                 \"We are learning from best trainer\"\n",
        "]\n",
        "\n",
        "tokenizer = Tokenizer(num_words = 50,oov_token = \"#OOv\")\n",
        "tokenizer.fit_on_texts(sentence_list)\n",
        "word_index = tokenizer.word_index\n",
        "print(word_index)\n",
        "\n",
        "sequences = tokenizer.texts_to_sequences(sentence_list)\n",
        "for s in sequences : print(s)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'#OOv': 1, 'learning': 2, 'i': 3, 'love': 4, 'deep': 5, 'we': 6, 'are': 7, 'machine': 8, 'from': 9, 'best': 10, 'trainer': 11}\n",
            "[3, 4, 8, 2]\n",
            "[3, 4, 5, 2]\n",
            "[6, 7, 2, 5, 2]\n",
            "[6, 7, 2, 9, 10, 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZy5tcWusOvL",
        "outputId": "f224e085-1072-4218-c342-84fb2fe6a7f4"
      },
      "source": [
        "new = [\"The world is screwed, so are we\"]\n",
        "seq = tokenizer.texts_to_sequences(new)\n",
        "print(s)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6, 7, 2, 9, 10, 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Etz5J2RsUhc",
        "outputId": "ba13e8d1-15c6-412a-9fe4-4d11cf9bfeb4"
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "padded_seq = pad_sequences(sequences, padding = 'post')\n",
        "for s in padded_seq : print(s)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3 4 8 2 0 0]\n",
            "[3 4 5 2 0 0]\n",
            "[6 7 2 5 2 0]\n",
            "[ 6  7  2  9 10 11]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Me5yqBriwIyz"
      },
      "source": [
        "# Training an RNN with IMDB dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365,
          "referenced_widgets": [
            "30aeec09e13f47e5b7d2bdde09741cf3",
            "21e2a99a6bef43cc9d0037e3e12cbd7c",
            "affdad39ec394f17b6fdd8ea32294a1d",
            "1cc24abbbbd44d14b1e3b22e0ee8c7b7",
            "fa131d5d7d784f3282724789a19bab7b",
            "ba7aa42df04542dd928bcd0da43ad5f0",
            "d519b608eb93486083a2d0b0af8fa8d5",
            "ef0eb5a01b324ee7b33d03d099d98d92",
            "d6d3f3e71e0242b385bc4b9b28228884",
            "8ef2788078194f8a84d08efd7c692ddc",
            "71ddff42f0194f089e8b5f4c1559d2d9",
            "d10dfb057e6c4b44bc75bd7f483b3c7f",
            "37c035dd8d7040e3aef2ea5cef5f7e40",
            "3d8edb46285f449eb3805c64b8f12e0d",
            "0fc69197035644e6ad9cdb7dfc9f2849",
            "1e1bfc6f121445e08c93a6c7cf7cb4d0",
            "635d300faf104d45a5ea7ea8de90ff19",
            "f77af9a7a68449499d8b3041328e8add",
            "4409a9c706f5444885bacd19b305a600",
            "c140a02f96564376ba0159b6c06a2409",
            "0122ca32b07f4a28885cd40ae4acae89",
            "14c02f517fa94b30a9783a5346cfa817",
            "04900b34badd4b18a02499d941e5558f",
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            "38866b03c2644c9dbe616c68f6129c2b",
            "c33c111d4f224e1eaaffbff45da7b594",
            "625f385a103c4f59b6a2d2ff0105f716",
            "4752ef0b7fe44d86bcd14f449697b24e",
            "05a2cb8e6d62423283eccaff7df4c4e1",
            "4b9aaa6d86dd4ac9962782c5668342f7",
            "4ceedfef261948caae7fd6dcb48c7501",
            "61905d8dad9544ddae743017eb9ac154",
            "cb23b32102af490fa608da67d9b4ff5f",
            "4a51a5fb2a5247abbc46ea0a20fca405",
            "bbcf360d191046298530b050a4512e39",
            "40445c0f7a4948f5add3769b7fa20d9d",
            "e9daf4a226eb4170a2c0a128265dcbb9",
            "8eadfb3f65b2411491e92eea1d3e9108",
            "1be1615eb1d7404997afe2c01074924a",
            "132900bdbecb4a34a40d3c141bd7dd64",
            "11d657d9c64440029f6cf1702c861adf",
            "aa68fce818b348bda94eaf64fbd22be4",
            "b66376c95471497aa2d28bfdc11b832e",
            "451f398f7f484a548b3d6a477e6ac16b",
            "f70d9827fc214a13811e65effb5242ad",
            "b9261f8f3fd54df2853c8c0f1cf0239c",
            "fd3edb303bf74ff48c27266aedc22020",
            "29cba70c0f344840881ac7ceea83ecd0",
            "7e54bac50b3a4202984cae8a1d6f8e3d",
            "d666f45186254a559e3c3cc5fe58e0cb",
            "d27be39d43b44c1a8cc52c0450e035a1",
            "cc298024809a4112b0a03f8d039c6c14",
            "3c0e11946b114a0ba8868147b5413b74",
            "52e5b8af52784b15989df0d97bfa0dde",
            "8d81a897830841d99c1eda7ca8ca4d5d",
            "a0e89d01832b4542b385feeb885431fb",
            "c9838d56eac84e81bef6e517c2ccdccc",
            "51fba4aceeab41d5b3565449748e4ea9",
            "f54bad0c5fde4d18b742263fb03003e6",
            "c78dbc8bfe3b42ea8c51a623a8e9ba91",
            "d14c770c978d4c5183c9c54b4cb1b7a8",
            "d06aaa0a1b244767a3e9f951c13060f2",
            "670c91e3c67a4759a6eb4f4cb2fa6318",
            "f216b547c1794e49b1b06c05d2980088"
          ]
        },
        "id": "68K320tduCkD",
        "outputId": "535a270d-de40-4d1a-fc78-1fe07b0d804d"
      },
      "source": [
        "import tensorflow_datasets as tfds\n",
        "imdb, info = tfds.load(\"imdb_reviews\", with_info=True, as_supervised=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDownloading and preparing dataset imdb_reviews/plain_text/1.0.0 (download: 80.23 MiB, generated: Unknown size, total: 80.23 MiB) to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0...\u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "30aeec09e13f47e5b7d2bdde09741cf3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Completed...', max=1.0, style=Progre…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d6d3f3e71e0242b385bc4b9b28228884",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', description='Dl Size...', max=1.0, style=ProgressSty…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "635d300faf104d45a5ea7ea8de90ff19",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJSRWNV/imdb_reviews-train.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "38866b03c2644c9dbe616c68f6129c2b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cb23b32102af490fa608da67d9b4ff5f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJSRWNV/imdb_reviews-test.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "11d657d9c64440029f6cf1702c861adf",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=25000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7e54bac50b3a4202984cae8a1d6f8e3d",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=1.0, bar_style='info', max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\rShuffling and writing examples to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0.incompleteJSRWNV/imdb_reviews-unsupervised.tfrecord\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c9838d56eac84e81bef6e517c2ccdccc",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=50000.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:absl:Dataset is using deprecated text encoder API which will be removed soon. Please use the plain_text version of the dataset and migrate to `tensorflow_text`.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\u001b[1mDataset imdb_reviews downloaded and prepared to /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0. Subsequent calls will reuse this data.\u001b[0m\n",
            "\r"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WZiQ6G8NvstC"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "train_data, test_data = imdb['train'], imdb['test']\n",
        "\n",
        "training_sentences = []\n",
        "training_labels = []\n",
        "\n",
        "testing_sentences = []\n",
        "testing_labels = []\n",
        "\n",
        "for s,l in train_data:\n",
        "  training_sentences.append(s.numpy().decode('utf8'))\n",
        "  training_labels.append(l.numpy())\n",
        "\n",
        "for s,l in test_data:\n",
        "  testing_sentences.append(s.numpy().decode('utf8'))\n",
        "  testing_labels.append(l.numpy())\n",
        "\n",
        "training_labels_final = np.array(training_labels)\n",
        "testing_labels_final = np.array(testing_labels)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZb-0NHIyQwN",
        "outputId": "65604f92-f234-491e-f5b3-f82289d4dcec"
      },
      "source": [
        "count = 1\n",
        "for s in training_sentences:\n",
        "  if count>5 : break\n",
        "  print(s,end = '\\n\\n\\n')\n",
        "  count +=1"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "This was an absolutely terrible movie. Don't be lured in by Christopher Walken or Michael Ironside. Both are great actors, but this must simply be their worst role in history. Even their great acting could not redeem this movie's ridiculous storyline. This movie is an early nineties US propaganda piece. The most pathetic scenes were those when the Columbian rebels were making their cases for revolutions. Maria Conchita Alonso appeared phony, and her pseudo-love affair with Walken was nothing but a pathetic emotional plug in a movie that was devoid of any real meaning. I am disappointed that there are movies like this, ruining actor's like Christopher Walken's good name. I could barely sit through it.\n",
            "\n",
            "\n",
            "I have been known to fall asleep during films, but this is usually due to a combination of things including, really tired, being warm and comfortable on the sette and having just eaten a lot. However on this occasion I fell asleep because the film was rubbish. The plot development was constant. Constantly slow and boring. Things seemed to happen, but with no explanation of what was causing them or why. I admit, I may have missed part of the film, but i watched the majority of it and everything just seemed to happen of its own accord without any real concern for anything else. I cant recommend this film at all.\n",
            "\n",
            "\n",
            "Mann photographs the Alberta Rocky Mountains in a superb fashion, and Jimmy Stewart and Walter Brennan give enjoyable performances as they always seem to do. <br /><br />But come on Hollywood - a Mountie telling the people of Dawson City, Yukon to elect themselves a marshal (yes a marshal!) and to enforce the law themselves, then gunfighters battling it out on the streets for control of the town? <br /><br />Nothing even remotely resembling that happened on the Canadian side of the border during the Klondike gold rush. Mr. Mann and company appear to have mistaken Dawson City for Deadwood, the Canadian North for the American Wild West.<br /><br />Canadian viewers be prepared for a Reefer Madness type of enjoyable howl with this ludicrous plot, or, to shake your head in disgust.\n",
            "\n",
            "\n",
            "This is the kind of film for a snowy Sunday afternoon when the rest of the world can go ahead with its own business as you descend into a big arm-chair and mellow for a couple of hours. Wonderful performances from Cher and Nicolas Cage (as always) gently row the plot along. There are no rapids to cross, no dangerous waters, just a warm and witty paddle through New York life at its best. A family film in every sense and one that deserves the praise it received.\n",
            "\n",
            "\n",
            "As others have mentioned, all the women that go nude in this film are mostly absolutely gorgeous. The plot very ably shows the hypocrisy of the female libido. When men are around they want to be pursued, but when no \"men\" are around, they become the pursuers of a 14 year old boy. And the boy becomes a man really fast (we should all be so lucky at this age!). He then gets up the courage to pursue his true love.\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "70gkigcty8zf",
        "outputId": "74e253cb-d809-4ae0-f6ce-3aeb95a0700f"
      },
      "source": [
        "count = 1\n",
        "for l in training_labels:\n",
        "  if count>5 : break\n",
        "  print(l,end = '\\n\\n')\n",
        "  count +=1"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "\n",
            "0\n",
            "\n",
            "0\n",
            "\n",
            "1\n",
            "\n",
            "1\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j6M5lTj-zenn",
        "outputId": "59fba009-721f-40bc-ca87-590c163db870"
      },
      "source": [
        "print(len(training_sentences))\n",
        "print(len(training_labels))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "25000\n",
            "25000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0ZDihf9z3Wk"
      },
      "source": [
        "vocab_size = 10000\n",
        "embedding_dim = 16\n",
        "max_length = 120\n",
        "truc_type = 'post'\n",
        "oov_token = \"#OOV\""
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKMLAQ3R0gDP"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zo_9Yq-l0i2Q"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "tokenizer = Tokenizer(num_words=vocab_size, oov_token=oov_token)\n",
        "tokenizer.fit_on_texts(training_sentences)\n",
        "word_index = tokenizer.word_index\n",
        "sequences = tokenizer.texts_to_sequences(training_sentences)\n",
        "#print(sequences)\n",
        "padded = pad_sequences(sequences,maxlen=max_length,truncating=truc_type)\n",
        "\n",
        "testing_sequences = tokenizer.texts_to_sequences(testing_sentences)\n",
        "testing_padded = pad_sequences(testing_sequences,maxlen = max_length)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mP97iRnD4a-t"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Embedding(vocab_size,embedding_dim,input_length = max_length),\n",
        "                             tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "                             tf.keras.layers.Dense(16,activation='relu'),\n",
        "                             tf.keras.layers.Dense(1,activation='sigmoid')\n",
        "\n",
        "])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DhG_buiq52W1",
        "outputId": "7d46319b-6082-48c1-f1c8-f3a04c5c7d4a"
      },
      "source": [
        "model.compile(loss = 'binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 120, 16)           160000    \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (None, 64)                12544     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 16)                1040      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 173,601\n",
            "Trainable params: 173,601\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2NzNh_j6RbL",
        "outputId": "06fdeaf7-b73c-416f-f98e-99ccf77f42ef"
      },
      "source": [
        "history = model.fit(padded,training_labels_final,epochs = 6,validation_data = (testing_padded,testing_labels_final))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/6\n",
            "782/782 [==============================] - 48s 19ms/step - loss: 0.5723 - accuracy: 0.6581 - val_loss: 0.3478 - val_accuracy: 0.8470\n",
            "Epoch 2/6\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 0.2815 - accuracy: 0.8863 - val_loss: 0.3593 - val_accuracy: 0.8417\n",
            "Epoch 3/6\n",
            "782/782 [==============================] - 14s 17ms/step - loss: 0.2066 - accuracy: 0.9222 - val_loss: 0.3728 - val_accuracy: 0.8370\n",
            "Epoch 4/6\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.1515 - accuracy: 0.9450 - val_loss: 0.5101 - val_accuracy: 0.8264\n",
            "Epoch 5/6\n",
            "782/782 [==============================] - 13s 17ms/step - loss: 0.0958 - accuracy: 0.9675 - val_loss: 0.5625 - val_accuracy: 0.8214\n",
            "Epoch 6/6\n",
            "782/782 [==============================] - 14s 18ms/step - loss: 0.0697 - accuracy: 0.9773 - val_loss: 0.6056 - val_accuracy: 0.8032\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "8sJtun7b6_8N",
        "outputId": "efbe8fa8-6b55-4f1c-a74c-ef3589665dda"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel('accuracy')\n",
        "plt.legend(['accuracy', 'val_accuracy'])\n",
        "plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "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"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "SzxiBLAO_uhe",
        "outputId": "5558996f-03eb-45df-a955-76082a0a9476"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['loss', 'val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "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"
          }
        }
      ]
    }
  ]
}