Data_Augmentation_in_NLP_Using_Back_Translation.ipynb

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      },
      "source": [
        "# Data Augmentation in NLP Using Back Translation With MarianMT       \n",
        "> Author: https://zoumanakeita.medium.com/"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n",
        "!pip install sentencepiece\n",
        "from transformers import MarianMTModel, MarianTokenizer"
      ],
      "metadata": {
        "id": "5ZUY_FALGjIN"
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      "id": "5ZUY_FALGjIN",
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "**Note**: \n",
        "Make sure to restart the \"**Runtime > Restart runtime**\" so that the changes are taken into consideration."
      ],
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      "source": [
        "# Models Configuration"
      ],
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      "cell_type": "markdown",
      "source": [
        "## Configuration of the first model \n",
        "This model translates from English to French"
      ],
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        "# Get the name of the first model\n",
        "first_model_name = 'Helsinki-NLP/opus-mt-en-fr'\n",
        "\n",
        "# Get the tokenizer\n",
        "first_model_tkn = MarianTokenizer.from_pretrained(first_model_name)\n",
        "\n",
        "# Load the pretrained model based on the name\n",
        "first_model = MarianMTModel.from_pretrained(first_model_name)"
      ],
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        "## Configuration of the second model \n",
        "This model translates from French to English"
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        "# Get the name of the second model\n",
        "second_model_name = 'Helsinki-NLP/opus-mt-fr-en'\n",
        "\n",
        "# Get the tokenizer\n",
        "second_model_tkn = MarianTokenizer.from_pretrained(second_model_name)\n",
        "\n",
        "# Load the pretrained model based on the name\n",
        "second_model = MarianMTModel.from_pretrained(second_model_name)"
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      "cell_type": "code",
      "source": [
        "original_texts = [\"This article aims to perform the back translation for text data augmentation\",\n",
        "          \"It is the 25th article by Zoumana on Medium. He loves to give back to the community\",\n",
        "          \"The first model translates from English to French, which is a temporary process\", \n",
        "          \"The second model finally translates back all the temporary french text into English\"]\n",
        "\n",
        "original_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VI_wXoaFGjBY",
        "outputId": "14fb7a31-2551-414f-a309-36d1e2b05e74"
      },
      "id": "VI_wXoaFGjBY",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This article aims to perform the back translation for text data augmentation',\n",
              " 'It is the 25th article by Zoumana on Medium. He loves to give back to the community',\n",
              " 'The first model translates from English to French, which is a temporary process',\n",
              " 'The second model finally translates back all the temporary french text into English']"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def format_batch_texts(language_code, batch_texts):\n",
        "  \n",
        "  formated_bach = [\">>{}<< {}\".format(language_code, text) for text in batch_texts]\n",
        "\n",
        "  return formated_bach\n"
      ],
      "metadata": {
        "id": "O3kxabs9QVt1"
      },
      "id": "O3kxabs9QVt1",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test of the function\n",
        "format_batch_texts(\"fr\", original_texts)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vh_Ilqo_SfTc",
        "outputId": "cc21d6a7-8328-467a-c2d3-3f91824c16f1"
      },
      "id": "vh_Ilqo_SfTc",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['>>fr<< This article aims to perform the back translation for text data augmentation',\n",
              " '>>fr<< It is the 25th article by Zoumana on Medium. He loves to give back to the community',\n",
              " '>>fr<< The first model translates from English to French, which is a temporary process',\n",
              " '>>fr<< The second model finally translates back all the temporary french text into English']"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "O2bN3HrWSfGx"
      },
      "id": "O2bN3HrWSfGx",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_translation(batch_texts, model, tokenizer, language=\"fr\"):\n",
        "    # Prepare the text data into appropriate format for the model\n",
        "    formated_batch_texts = format_batch_texts(language, batch_texts)\n",
        "    \n",
        "    # Generate translation using model\n",
        "    translated = model.generate(**tokenizer(formated_batch_texts, return_tensors=\"pt\", padding=True))\n",
        "\n",
        "    # Convert the generated tokens indices back into text\n",
        "    translated_texts = [tokenizer.decode(t, skip_special_tokens=True) for t in translated]\n",
        "    \n",
        "    return translated_texts\n"
      ],
      "metadata": {
        "id": "AY40z0OOL2UQ"
      },
      "id": "AY40z0OOL2UQ",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "translated_texts = perform_translation(original_texts, first_model, first_model_tkn)"
      ],
      "metadata": {
        "id": "ngXroplYGi96"
      },
      "id": "ngXroplYGi96",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "translated_texts"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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          "data": {
            "text/plain": [
              "[\"Cet article vise à effectuer la traduction arrière pour l'augmentation de données texte\",\n",
              " \"C'est le 25ème article de Zoumana sur Medium. Il aime rendre à la communauté\",\n",
              " \"Le premier modèle traduit de l'anglais au français, qui est un processus temporaire\",\n",
              " 'Le deuxième modèle traduit enfin tout le texte temporaire français en anglais']"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we can translate back the following texts into English by providing the proper parameters."
      ],
      "metadata": {
        "id": "c1xggx_gQLIX"
      },
      "id": "c1xggx_gQLIX"
    },
    {
      "cell_type": "code",
      "source": [
        "back_translated_texts = perform_translation(translated_texts, second_model, second_model_tkn)"
      ],
      "metadata": {
        "id": "GNEM2wcAOzLU"
      },
      "id": "GNEM2wcAOzLU",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "back_translated_texts"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssC3tYsqUvF4",
        "outputId": "cf92f6fe-f865-4abb-99d7-bd0d8fa589e1"
      },
      "id": "ssC3tYsqUvF4",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This article aims to perform back translation for text data increase',\n",
              " \"This is Zoumana's 25th article on Medium. He likes to give back to the community.\",\n",
              " 'The first model translates from English to French, which is a temporary process',\n",
              " 'The second model finally translates all the temporary French text into English']"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Instead of performing all the tasks in a standalone manner, it might be interesting to create a function that generates the back translation rights away. "
      ],
      "metadata": {
        "id": "s-GL4nCWVOqs"
      },
      "id": "s-GL4nCWVOqs"
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_back_translation(batch_texts, original_language=\"en\", temporary_language=\"fr\"):\n",
        "\n",
        "  # Translate from Original to Temporary Language\n",
        "  tmp_translated_batch = perform_translation(batch_texts, first_model, first_model_tkn, temporary_language)\n",
        "\n",
        "  # Translate Back to English\n",
        "  back_translated_batch = perform_translation(tmp_translated_batch, second_model, second_model_tkn, original_language)\n",
        "\n",
        "  # Return The Final Result\n",
        "  return back_translated_batch\n"
      ],
      "metadata": {
        "id": "jdle3dYBUyE_"
      },
      "id": "jdle3dYBUyE_",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def combine_texts(original_texts, back_translated_batch):\n",
        "  \n",
        "  return set(original_texts + back_translated_batch) "
      ],
      "metadata": {
        "id": "wqrtExgMXS_L"
      },
      "id": "wqrtExgMXS_L",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NeWMnBOKlQPj"
      },
      "id": "NeWMnBOKlQPj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "back_translated_batch = perform_back_translation(original_texts)\n",
        "back_translated_batch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DyAax0sIWsm0",
        "outputId": "9fcb6407-1724-4480-d6d9-6ff7736f9ba5"
      },
      "id": "DyAax0sIWsm0",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['This article aims to perform back translation for text data increase',\n",
              " \"This is Zoumana's 25th article on Medium. He likes to give back to the community.\",\n",
              " 'The first model translates from English to French, which is a temporary process',\n",
              " 'The second model finally translates all the temporary French text into English']"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can notice that the third sentence in the original batch is exactly the same as the one in the back translated batch. Here is where the duplicate suppression occurs. "
      ],
      "metadata": {
        "id": "7Uo-fKC9X-33"
      },
      "id": "7Uo-fKC9X-33"
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Final Augmented Text data. \n",
        "I modified the back translation function by creating the augmentation. "
      ],
      "metadata": {
        "id": "QLaKLmh6Ywdo"
      },
      "id": "QLaKLmh6Ywdo"
    },
    {
      "cell_type": "code",
      "source": [
        "def perform_back_translation_with_augmentation(batch_texts, original_language=\"en\", temporary_language=\"fr\"):\n",
        "\n",
        "  # Translate from Original to Temporary Language\n",
        "  tmp_translated_batch = perform_translation(batch_texts, first_model, first_model_tkn, temporary_language)\n",
        "\n",
        "  # Translate Back to English\n",
        "  back_translated_batch = perform_translation(tmp_translated_batch, second_model, second_model_tkn, original_language)\n",
        "\n",
        "  # Return The Final Result\n",
        "  return combine_texts(original_texts, back_translated_batch)"
      ],
      "metadata": {
        "id": "3klpsR9-mA3v"
      },
      "id": "3klpsR9-mA3v",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_augmented = perform_back_translation_with_augmentation(original_texts)\n",
        "final_augmented"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQiu7p1MYtmO",
        "outputId": "f6b896cf-9a96-469c-8078-678435c35455"
      },
      "id": "uQiu7p1MYtmO",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'It is the 25th article by Zoumana on Medium. He loves to give back to the community',\n",
              " 'The first model translates from English to French, which is a temporary process',\n",
              " 'The second model finally translates all the temporary French text into English',\n",
              " 'The second model finally translates back all the temporary french text into English',\n",
              " 'This article aims to perform back translation for text data increase',\n",
              " 'This article aims to perform the back translation for text data augmentation',\n",
              " \"This is Zoumana's 25th article on Medium. He likes to give back to the community.\"}"
            ]
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          "execution_count": 51
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