7 Text Augmentation Using nlpaug.ipynb

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      "cell_type": "markdown",
      "source": [
        "# Text Augmentation\n",
        "\n",
        "\\\n",
        "The more data we have, the better model performance we can achieve. However, it is often too costly and time-consuming to annotate a large amount of training data. Therefore, proper data augmentation is useful to boost up your model performance.\n",
        "\n",
        "\\\n",
        "In the natural language processing (NLP) field, it is hard to augment text due to the high complexity of language.\n",
        "\n",
        "\\\n",
        "The [nlpaug](https://github.com/makcedward/nlpaug) module implements a number of high-performance text augmentation algorithms that may significantly boost performance of NLP models. This tutorial introduces some cool text augmentation functions in the nlpaug module.\n",
        "\n",
        "\\\n",
        "References and recommended readings:\n",
        "* [nlpaug Git Hub site](https://github.com/makcedward/nlpaug)\n",
        "* [Data Augmentation in NLP: Introduction to Text Augmentation](https://towardsdatascience.com/data-augmentation-in-nlp-2801a34dfc28)"
      ],
      "metadata": {
        "id": "Ru1eoHfA_mmx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install and import modules"
      ],
      "metadata": {
        "id": "SYfBOfe4XP6e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the most recent version of gensim.\n",
        "# Otherwise, you may get the following error when running naw.WordEmbsAug():\n",
        "# 'Word2VecKeyedVectors' object has no attribute 'index_to_key'\n",
        "# see: https://stackoverflow.com/questions/71032760/word2veckeyedvectors-object-has-no-attribute-index-to-key\n",
        "!pip install --upgrade gensim --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GcR6g_O_J7GK",
        "outputId": "1e22a7e1-7f40-468f-87e1-e718849f015f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 24.1 MB 1.1 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import gensim.\n",
        "# Note: You will need to retart runtime in order to import the most recent version of gensim \n",
        "import gensim\n",
        "print(gensim.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7gsPk4gKXl7",
        "outputId": "88823f14-54af-4d7d-ccbd-673030e3e6ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the transformers module in order to use their base models (e.g., BERT)\n",
        "!pip install transformers --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZuL6ywxTevt",
        "outputId": "c49b5daf-b4f1-479e-c325-3222ba84eec7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[K     |████████████████████████████████| 4.9 MB 15.3 MB/s \n",
            "\u001b[K     |████████████████████████████████| 120 kB 57.0 MB/s \n",
            "\u001b[K     |████████████████████████████████| 6.6 MB 65.3 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import transformers\n",
        "import transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105,
          "referenced_widgets": [
            "111469b058754c70a9312a75b9c86cce",
            "07ce224630604b6bb81a9543f8d3a787",
            "600f8481f3044e4fabc7aae2c052bc04",
            "91796f33ba95495ba716381cfe61d99b",
            "0e81ccdbf9a740a1806f5b8a49b6ab9c",
            "6180d90c3f4342b18cb504173ad480fb",
            "e34ee9bf77664cf0a8adf156822ce0b3",
            "f7035859497c44f1aece63e294cc3913",
            "f003dfa1ef9745b9b770939caf174c56",
            "3b6b2be64e0d4b36b911695ee1a5f955",
            "99abaf23976a4bebb893bbe1b2f0388c"
          ]
        },
        "id": "cU50-qZ-ToCZ",
        "outputId": "a997325f-85da-45a4-b397-e4ab7e2b22fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Moving 0 files to the new cache system\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "111469b058754c70a9312a75b9c86cce"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the tokenizer needed by the back translation model\n",
        "!pip install sacremoses --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i82tiRXrYgqF",
        "outputId": "71ab5cf2-82f4-48bf-a4ee-e77c2bc37324"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the tokenizer\n",
        "import sacremoses"
      ],
      "metadata": {
        "id": "gUP0ZnhwYkV1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Install the nlpaug module\n",
        "!pip install nlpaug --quiet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pu8MvH3qBQ6q",
        "outputId": "8060edb7-b5b0-46d2-da1a-2f471b47d3a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l\r\u001b[K     |▉                               | 10 kB 28.5 MB/s eta 0:00:01\r\u001b[K     |█▋                              | 20 kB 22.9 MB/s eta 0:00:01\r\u001b[K     |██▍                             | 30 kB 30.2 MB/s eta 0:00:01\r\u001b[K     |███▏                            | 40 kB 15.3 MB/s eta 0:00:01\r\u001b[K     |████                            | 51 kB 14.0 MB/s eta 0:00:01\r\u001b[K     |████▉                           | 61 kB 16.3 MB/s eta 0:00:01\r\u001b[K     |█████▋                          | 71 kB 15.2 MB/s eta 0:00:01\r\u001b[K     |██████▍                         | 81 kB 14.9 MB/s eta 0:00:01\r\u001b[K     |███████▏                        | 92 kB 16.2 MB/s eta 0:00:01\r\u001b[K     |████████                        | 102 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████▉                       | 112 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████▋                      | 122 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 133 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████▏                    | 143 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████                    | 153 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 163 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████████▋                  | 174 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████████▍                 | 184 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████████▏                | 194 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████                | 204 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 215 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████▋              | 225 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████▍             | 235 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████▏            | 245 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████            | 256 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████▊           | 266 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████▌          | 276 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████▍         | 286 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████▏        | 296 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 307 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 317 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▌      | 327 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 337 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 348 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 358 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▊   | 368 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▌  | 378 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▎ | 389 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▏| 399 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 409 kB 14.7 MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 410 kB 14.7 MB/s \n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RtqsnG_Q-8KO"
      },
      "outputs": [],
      "source": [
        "# Import the nlpaug module and its methods\n",
        "import nlpaug.augmenter.char as nac\n",
        "import nlpaug.augmenter.word as naw\n",
        "import nlpaug.augmenter.sentence as nas\n",
        "import nlpaug.flow as nafc\n",
        "from nlpaug.util import Action"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download models"
      ],
      "metadata": {
        "id": "bTAoThu-XVDy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Download models to a temporary path\n",
        "from nlpaug.util.file.download import DownloadUtil\n",
        "DownloadUtil.download_word2vec(dest_dir = '.')\n",
        "# Possible values are ‘wiki-news-300d-1M’, ‘wiki-news-300d-1M-subword’, ‘crawl-300d-2M’ and ‘crawl-300d-2M-subword’\n",
        "DownloadUtil.download_fasttext(dest_dir = '.', model_name = 'crawl-300d-2M')\n",
        "# Possible values are ‘glove.6B’, ‘glove.42B.300d’, ‘glove.840B.300d’ and ‘glove.twitter.27B’\n",
        "DownloadUtil.download_glove(dest_dir = '.', model_name = 'glove.6B')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TxL8dqJDH4Lt",
        "outputId": "e905de94-5b56-4db0-9fc1-cd307db63c5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?export=download&id=0B7XkCwpI5KDYNlNUTTlSS21pQmM\n",
            "To: /content/GoogleNews-vectors-negative300.bin.gz\n",
            "100%|██████████| 1.65G/1.65G [00:16<00:00, 97.8MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Example text"
      ],
      "metadata": {
        "id": "yAWfkkWLXdMF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's define some texts\n",
        "text = \"\"\"\n",
        "  Is daily coffee consumption good for our health? \n",
        "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
        "  \"\"\""
      ],
      "metadata": {
        "id": "MhAlqyypBeGi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Option 1: Substitute or insert word randomly using word embeddings similarity"
      ],
      "metadata": {
        "id": "peGEPKIGXhg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the augmenter with model \"word2vec\"\n",
        "aug = naw.WordEmbsAug(\n",
        "  # You can choose from \"word2vec\", \"glove\", or \"fasttext\" \n",
        "  model_type = 'word2vec', \n",
        "  model_path = 'GoogleNews-vectors-negative300.bin',\n",
        "  # You may also choose \"insert\"\n",
        "  action = \"substitute\")\n",
        "\n",
        "# Augment the text\n",
        "augmented_text = aug.augment(text)\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Btf7-QPfCY7C",
        "outputId": "e14c15e7-6278-4c65-a089-0c26b24f988d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "[\"Is daily coffee unprocessed_grains good depriving our Ajmal_Pardes? hadn'tI guess it'sa revolves_around particularized_suspicion to believe AMÉLIE_MAURESMO, but it may also adversely_affects on how much you drink.\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the augmenter with model \"fasttext\"\n",
        "aug = naw.WordEmbsAug(\n",
        "  # You can choose from \"word2vec\", \"glove\", or \"fasttext\" \n",
        "  model_type = 'fasttext', \n",
        "  # Note: check your \"content\" path to find out specific model names\n",
        "  model_path = 'crawl-300d-2M.vec',\n",
        "  # You may also choose \"insert\"\n",
        "  action = \"substitute\")\n",
        "\n",
        "# Augment the text\n",
        "augmented_text = aug.augment(text)\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BhEH3uD0QkUy",
        "outputId": "f7ee9ee2-f8c2-48b8-ea16-9a82d50d7c24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "['1.Is nearly-daily coffee consumption good for our hеаlth? I surmize it is equitable to believe so, but it mght acutally depending on you--how much you drink.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initialize the augmenter with model \"glove\"\n",
        "aug = naw.WordEmbsAug(\n",
        "  # You can choose from \"word2vec\", \"glove\", or \"fasttext\" \n",
        "  model_type = 'glove', \n",
        "  # Note: check your \"content\" path to find out specific model names\n",
        "  model_path = 'glove.6B.300d.txt',\n",
        "  # You may also choose \"insert\"\n",
        "  action = \"substitute\")\n",
        "\n",
        "# Augment the text\n",
        "augmented_text = aug.augment(text)\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sP8SzmOVSWtm",
        "outputId": "47d50d5f-c430-4931-8131-5b8f5f878029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "['Is daily coffee goods good for our health? I guess it can reasonable continue believe so, go it if use depend on how some anybody ate.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Option 2: Substitute or insert word by contextual word embeddings"
      ],
      "metadata": {
        "id": "AUY8CVwoX3xF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Substitute word by contextual word embeddings (BERT, DistilBERT, RoBERTA or XLNet)\n",
        "aug = naw.ContextualWordEmbsAug(\n",
        "  # Other models include 'distilbert-base-uncased', 'roberta-base', etc.\n",
        "  model_path = 'bert-base-uncased', \n",
        "  # You can also choose \"insert\"\n",
        "  action = \"substitute\")\n",
        "augmented_text = aug.augment(text)\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304,
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        "id": "upPJoyIeS7kY",
        "outputId": "4d92c0e5-9c48-476b-8603-1f85a52e1d99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/570 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "version_minor": 0,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "949c5ee6e9534ea49b1ed74bea825c1c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ac690690c08477cac86975eb32cbd1f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "['is daily coffee as good for that waitress? i guess it appears reasonable to use her, but it may certainly depend on have much you serve.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Option 3: Substitute or insert word by synonym"
      ],
      "metadata": {
        "id": "-WXCwEsdYLGt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Substitute word by WordNet's synonym\n",
        "aug = naw.SynonymAug(aug_src = 'wordnet')\n",
        "augmented_text = aug.augment(text)\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zdO-g5ugUIUu",
        "outputId": "428fae79-550a-49d5-b3c0-10d636450fbf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]     /root/nltk_data...\n",
            "[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "['Is casual coffee consumption skillful for our health? I guess it is reasonable to think so, simply information technology may also calculate on how much you drink.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Substitute word by WordNet's synonym.\n",
        "# You can optionally set the max number of words to replace with synonym.\n",
        "aug = naw.SynonymAug(aug_src = 'wordnet', aug_max = 3)\n",
        "augmented_text = aug.augment(text, )\n",
        "print(\"Original:\")\n",
        "print(text)\n",
        "print(\"Augmented Text:\")\n",
        "print(augmented_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oTAo0nKDeQwF",
        "outputId": "b85d49a6-7113-4d55-8a8c-fd318d7eeaa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original:\n",
            "\n",
            "  Is daily coffee consumption good for our health? \n",
            "  I guess it is reasonable to believe so, but it may also depend on how much you drink.\n",
            "  \n",
            "Augmented Text:\n",
            "['Is daily coffee consumption good for our health? I guess information technology is reasonable to believe so, but it may also reckon on how much you drink.']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Option 4: Substitute or insert word using back translation"
      ],
      "metadata": {
        "id": "YpA32bf3YSod"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Use back translation augmenter\n",
        "back_translation_aug = naw.BackTranslationAug(\n",
        "    from_model_name = 'facebook/wmt19-en-de', \n",
        "    to_model_name = 'facebook/wmt19-de-en'\n",
        ")\n",
        "back_translation_aug.augment(text)"
      ],
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}