Sequence-to-Sequence Model for Machine Translation.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdX3ToMsvvYC"
      },
      "source": [
        "# Sequence-to-Sequence Model for Machine Translation\n",
        "---"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nxUWSY0psgOB",
        "outputId": "5fe02257-b556-4ea5-d8b8-176dbe731d67"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-3.3.2 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "JlFWXxJnsaUP"
      },
      "outputs": [],
      "source": [
        "import datasets\n",
        "import nltk\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "\n",
        "from nltk.tokenize import word_tokenize\n",
        "from torch.utils.data import DataLoader, TensorDataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Igjxc0yasYHb"
      },
      "source": [
        "---\n",
        "## Preparting Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HN-X47OnsYHc"
      },
      "source": [
        "---\n",
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "237226dd18db4d6b802b7b7e6f8ff66d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "train.jsonl:   0%|          | 0.00/4.60M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f31975108f0141f7bb02e85626891efa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "val.jsonl:   0%|          | 0.00/164k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "709769256f044254ac7dc6c791d405f6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "test.jsonl:   0%|          | 0.00/156k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0126bee5e434ffd8c95f79a7dd38f76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/29000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1dac98ee9243460e95a958beb19d2286"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/1014 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba4ff6e611f84e01a575417caf37a70d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "03c25d7014584ef194f62d6b603a9ad3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Load the English-German subset of the Multi30k dataset\n",
        "dataset = datasets.load_dataset(\"bentrevett/multi30k\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rx8clcj8wwvE",
        "outputId": "c20e6487-4ca9-40e4-afc8-25a511ce110b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['en', 'de'],\n",
            "        num_rows: 29000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['en', 'de'],\n",
            "        num_rows: 1014\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['en', 'de'],\n",
            "        num_rows: 1000\n",
            "    })\n",
            "})\n"
          ]
        }
      ],
      "source": [
        "print(dataset)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "iSGH11Y9syo8"
      },
      "outputs": [],
      "source": [
        "# Split the dataset into train, validation, and test sets\n",
        "train_data, valid_data, test_data = (dataset[\"train\"],\n",
        "                                     dataset[\"validation\"],\n",
        "                                     dataset[\"test\"],)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TYhyLsi4s8j9",
        "outputId": "f31d9e4d-e6ef-4a62-a950-d5062d51789f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'en': 'Two young, White males are outside near many bushes.', 'de': 'Zwei junge weiße Männer sind im Freien in der Nähe vieler Büsche.'}\n",
            "{'en': 'A group of men are loading cotton onto a truck', 'de': 'Eine Gruppe von Männern lädt Baumwolle auf einen Lastwagen'}\n",
            "{'en': 'A man in an orange hat starring at something.', 'de': 'Ein Mann mit einem orangefarbenen Hut, der etwas anstarrt.'}\n"
          ]
        }
      ],
      "source": [
        "print(train_data[0])\n",
        "print(valid_data[0])\n",
        "print(test_data[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGdnf8f8tDuO"
      },
      "source": [
        "---\n",
        "### Tokenizers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVNIENeS7Ne1",
        "outputId": "1e6b45ec-40ee-4f99-a0d5-74da65182cff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt_tab.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Download necessary resources for tokenization\n",
        "nltk.download('punkt')\n",
        "nltk.download('punkt_tab')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "2BCKvX2T6qNr"
      },
      "outputs": [],
      "source": [
        "# Example sentence in English for tokenization\n",
        "text_english = \"A cat chases a mouse into a small hole in the wall.\"\n",
        "tokens = word_tokenize(text_english)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdOXrT2qsYHj",
        "outputId": "720495c1-2ba0-4862-aef0-ed9d9ceff8ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokens: ['A', 'cat', 'chases', 'a', 'mouse', 'into', 'a', 'small', 'hole', 'in', 'the', 'wall', '.']\n"
          ]
        }
      ],
      "source": [
        "print(\"Tokens:\", tokens)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pqMOQTFW3vSO"
      },
      "source": [
        "**Exercise 1 [1/1]**:\n",
        "1. Implement `tokenize_example()` to apply these transformations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Vsj8QUtot41Z"
      },
      "outputs": [],
      "source": [
        "# Function to tokenize a sentence\n",
        "def tokenize_example(example, max_length = 100, sos_token = \"<sos>\", eos_token = \"<eos>\"):\n",
        "\n",
        "    tokenized = {}\n",
        "\n",
        "    # Iterate through the languages\n",
        "    for lang_key in example.keys():\n",
        "        sentence = example[lang_key]\n",
        "        tokens = word_tokenize(sentence.lower()) # Tokenize and convert to lowercase\n",
        "        tokens = tokens[:max_length] # Trim or pad to max_length\n",
        "        tokenized[lang_key] = [sos_token] + tokens + [eos_token] # Add <sos> and <eos> tokens\n",
        "\n",
        "    return tokenized"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "kTalTDlp_G7N"
      },
      "outputs": [],
      "source": [
        "# Tokenizing the first example in the training data\n",
        "tokenized = tokenize_example(train_data[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59yxCBAzsYHk",
        "outputId": "56c50adb-a56f-4b1c-d49b-adad7c9f1d52"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Tokenized English:  ['<sos>', 'two', 'young', ',', 'white', 'males', 'are', 'outside', 'near', 'many', 'bushes', '.', '<eos>']\n",
            "Tokenized German:  ['<sos>', 'zwei', 'junge', 'weiße', 'männer', 'sind', 'im', 'freien', 'in', 'der', 'nähe', 'vieler', 'büsche', '.', '<eos>']\n"
          ]
        }
      ],
      "source": [
        "print(\"Tokenized English: \", tokenized[\"en\"])\n",
        "print(\"Tokenized German: \", tokenized[\"de\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "OGsA2anfsYHk"
      },
      "outputs": [],
      "source": [
        "# Define parameters for tokenization\n",
        "max_length = 100\n",
        "sos_token = \"<sos>\"\n",
        "eos_token = \"<eos>\"\n",
        "fn_kwargs = {\"max_length\": max_length,\n",
        "             \"sos_token\": sos_token,\n",
        "             \"eos_token\": eos_token}"
      ]
    },
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      "execution_count": 14,
      "metadata": {
        "colab": {
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        },
        "id": "M0avAdx5sYHl",
        "outputId": "557364ff-ea8f-4e93-d80c-94f71338d929"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/29000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85950ad266c546ee9b147c6a66571aa5"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1014 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cfc2fa23d87f44eabf3ee3050a57abcd"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "aca190d669744a939d65ed34a95a964d"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Apply the tokenization to the entire train, validation, and test datasets\n",
        "train_data_tokenized = train_data.map(tokenize_example, fn_kwargs = fn_kwargs)\n",
        "valid_data_tokenized = valid_data.map(tokenize_example, fn_kwargs = fn_kwargs)\n",
        "test_data_tokenized = test_data.map(tokenize_example, fn_kwargs = fn_kwargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KxT67Xp2sYHl",
        "outputId": "ffeefe14-dbd9-4fec-b953-82c38bf061dd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['<sos>', 'toddler', 'boy', 'in', 'a', 'red', 'hat', 'holding', 'on', 'to', 'some', 'railings', '.', '<eos>']\n",
            "['<sos>', 'männliches', 'kleinkind', 'in', 'einem', 'roten', 'hut', ',', 'das', 'sich', 'an', 'einem', 'geländer', 'festhält', '.', '<eos>']\n"
          ]
        }
      ],
      "source": [
        "print(train_data_tokenized[100][\"en\"])\n",
        "print(train_data_tokenized[100][\"de\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4Bpe1uGff57"
      },
      "source": [
        "---\n",
        "### Vocabularies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QLTzenPEsYHm"
      },
      "source": [
        "**Exercise 2 [1/1]**:\n",
        "1. Implement build_vocab()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "tvGtO0IgdQf0"
      },
      "outputs": [],
      "source": [
        "# Function to build a vocabulary from tokenized sentences\n",
        "def build_vocab(tokenized, lang_key, specials = [\"<pad>\", \"<sos>\", \"<eos>\", \"<unk>\"]):\n",
        "\n",
        "    # Initialize with special tokens\n",
        "    vocab = {token: idx for idx, token in enumerate(specials)}\n",
        "\n",
        "    # Iterate over tokenized sentences and add words to the vocabulary\n",
        "    for example in tokenized:\n",
        "        for word in example[lang_key]:\n",
        "            if word not in vocab:\n",
        "                vocab[word] = len(vocab)\n",
        "\n",
        "    return vocab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "SG5iYnYLsYHm"
      },
      "outputs": [],
      "source": [
        "# Build vocabularies for English and German languages\n",
        "special_tokens = [\"<pad>\", \"<sos>\", \"<eos>\", \"<unk>\"]\n",
        "en_vocab = build_vocab(train_data_tokenized, \"en\", specials = special_tokens)\n",
        "de_vocab = build_vocab(train_data_tokenized, \"de\", specials = special_tokens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UhXzWsH4sYHn",
        "outputId": "126fe018-a7d0-4d19-dd1a-a757ea11851f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English Vocabulary size: 10218\n",
            "German Vocabulary size: 18680\n"
          ]
        }
      ],
      "source": [
        "print(\"English Vocabulary size:\", len(en_vocab))\n",
        "print(\"German Vocabulary size:\", len(de_vocab))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFWxFnwpMuvL",
        "outputId": "d0b42b9d-6d99-4bf1-9fe2-5fc62270b923"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "English Vocabulary: {'<pad>': 0, '<sos>': 1, '<eos>': 2, '<unk>': 3, 'two': 4, 'young': 5, ',': 6, 'white': 7, 'males': 8, 'are': 9}\n",
            "German Vocabulary: {'<pad>': 0, '<sos>': 1, '<eos>': 2, '<unk>': 3, 'zwei': 4, 'junge': 5, 'weiße': 6, 'männer': 7, 'sind': 8, 'im': 9}\n"
          ]
        }
      ],
      "source": [
        "print(\"English Vocabulary:\", dict(list(en_vocab.items())[:10]))\n",
        "print(\"German Vocabulary:\", dict(list(de_vocab.items())[:10]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hcdXrm0jsYHo"
      },
      "source": [
        "---\n",
        "### Convert to IDs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3ufJTK7Z4ThB"
      },
      "source": [
        "**Exercise 3 [1/1]**:\n",
        "1. Implement convert_tokens_to_ids()."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "kVumOsGZVNLj"
      },
      "outputs": [],
      "source": [
        "# Function to convert tokens to their corresponding IDs from the vocabulary\n",
        "def convert_tokens_to_ids(example, en_vocab, de_vocab, max_length = 10):\n",
        "\n",
        "    data_ids = {}\n",
        "\n",
        "    # Convert tokens in both English and German to IDs\n",
        "    for lang_key in example.keys():\n",
        "        vocab = en_vocab if lang_key == \"en\" else de_vocab\n",
        "        ids = [vocab.get(token, vocab[\"<unk>\"]) for token in example[lang_key]] # Use <unk> for unknown tokens\n",
        "        data_ids[lang_key] = ids[:max_length] # Trim to max_length\n",
        "\n",
        "    return data_ids"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nVsZSGPJ-yPT"
      },
      "source": [
        "**Exercise 4 [1/1]**:\n",
        "1. Use the map() function to convert tokenized examples in the train, validation, and test datasets into lists of IDs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "ETIFs-545jgC"
      },
      "outputs": [],
      "source": [
        "fn_kwargs = {\"en_vocab\": en_vocab,\n",
        "             \"de_vocab\": de_vocab,\n",
        "             \"max_length\": max_length}"
      ]
    },
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      "cell_type": "code",
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        "colab": {
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          "height": 113,
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        "outputId": "3d04907a-5de0-4bcf-bc27-9467c94dc884"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/29000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1014 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "78ed14ef41d7457ba30806bf6de5336c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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      ],
      "source": [
        "# Apply the conversion of tokens to IDs to the entire train, validation, and test datasets\n",
        "train_data_ids = train_data_tokenized.map(convert_tokens_to_ids, fn_kwargs = fn_kwargs)\n",
        "valid_data_ids = valid_data_tokenized.map(convert_tokens_to_ids, fn_kwargs = fn_kwargs)\n",
        "test_data_ids = test_data_tokenized.map(convert_tokens_to_ids, fn_kwargs = fn_kwargs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAZmQ2KvsYHr",
        "outputId": "06eb02b3-35f6-4026-fb7e-977778dae78d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'en': ['<sos>', 'two', 'young', ',', 'white', 'males', 'are', 'outside', 'near', 'many', 'bushes', '.', '<eos>'], 'de': ['<sos>', 'zwei', 'junge', 'weiße', 'männer', 'sind', 'im', 'freien', 'in', 'der', 'nähe', 'vieler', 'büsche', '.', '<eos>']}\n",
            "{'en': [1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 2], 'de': [1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 2]}\n"
          ]
        }
      ],
      "source": [
        "print(train_data_tokenized[0])\n",
        "print(train_data_ids[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSRfBOJZsYHr"
      },
      "source": [
        "---\n",
        "### Padding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PlwPLuIZQyY"
      },
      "source": [
        "**Exercise 5 [1/1]**:\n",
        "1. Similar to `convert_tokens_to_ids()` and `tokenize_example()`, implement `pad_example()` to pad each sequence in an English-French pair within an `example`.\n",
        "2. Use the pad token’s ID (0 in our case) for padding, and ensure every sequence is either padded or trimmed to match `max_length`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "2hPYSAlpYRyR"
      },
      "outputs": [],
      "source": [
        "# Function to pad sequences to a fixed length using padding token ID\n",
        "def pad_example(example, pad_token_id = 0, max_length = 10):\n",
        "\n",
        "    padded = {}\n",
        "\n",
        "    # Pad or trim sequences to max_length\n",
        "    for lan_key in example.keys():\n",
        "        sequence = example[lan_key]\n",
        "\n",
        "        # Trim if sequence is too long\n",
        "        if len(sequence) > max_length:\n",
        "            padded_sequence = sequence[:max_length]\n",
        "\n",
        "        # Pad if sequence is too short\n",
        "        else:\n",
        "            padded_sequence = sequence + [pad_token_id] * (max_length - len(sequence))\n",
        "\n",
        "        padded[lan_key] = padded_sequence\n",
        "\n",
        "    return padded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "BJ60ajQvCaJu"
      },
      "outputs": [],
      "source": [
        "fn_kwargs = {\"pad_token_id\": 0,\n",
        "             \"max_length\": max_length}"
      ]
    },
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            "e4474bf0f62b421a9f0baf6628eb3a70",
            "97fb457ff03a4c1ab1259abcf5b27d8f",
            "b6a48728415a44548f9f257a831c8c39",
            "ca5c00f716aa413a9e6669ef08e6f7d3",
            "81ad5312fca749f9b33c21483581aead",
            "06dced119c634036bdc0b37b2a2f5995",
            "def449fd07bc43e49a0f1943cd82bd1a",
            "e60ea1b674cf419fa3f35d889dacc732",
            "007f8e275a1d46778f7b9338afa1b032",
            "865b01088eee403aaa39732b66cb4b2b",
            "c46a412b277142939793af79de6732c6",
            "c81601a0e89c4dba8c9d828aee18c6e4",
            "cbc790a917b14cc5a81a429124788b30",
            "fd347c0f63254220836ec3414cd02f65",
            "676f9150a0fd4e9c8ecb305e7677a1d8",
            "79925f3e0b4b4daa8c104020c23ee881",
            "7b3579c3f1714fd283531fe948909671"
          ]
        },
        "id": "5uTNvPR0sYHs",
        "outputId": "1868548c-bd14-474a-c2c2-538f50d4fe61"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/29000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d1c1ed4161dd432889babf94f335bc91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1014 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bcfe726c18eb457e8ee47e097e052f0d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "def449fd07bc43e49a0f1943cd82bd1a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Apply padding to the entire train, validation, and test datasets\n",
        "train_data_padded = train_data_ids.map(pad_example, fn_kwargs = fn_kwargs)\n",
        "valid_data_padded = valid_data_ids.map(pad_example, fn_kwargs = fn_kwargs)\n",
        "test_data_padded = test_data_ids.map(pad_example, fn_kwargs = fn_kwargs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJ5EZfD1ZSTO"
      },
      "source": [
        "---\n",
        "### Data Loaders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "CWQcayCbsYHt"
      },
      "outputs": [],
      "source": [
        "# Set the data format to PyTorch tensors\n",
        "train_data_padded.set_format(type = \"torch\")\n",
        "valid_data_padded.set_format(type = \"torch\")\n",
        "test_data_padded.set_format(type = \"torch\")\n",
        "batch_size = 64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "ipqQ-ZsuZFVD"
      },
      "outputs": [],
      "source": [
        "# Create data loaders for batching during training\n",
        "train_dataloader = DataLoader(train_data_padded, batch_size = batch_size, shuffle = True)\n",
        "valid_dataloader = DataLoader(valid_data_padded, batch_size = batch_size, shuffle = False)\n",
        "test_dataloader = DataLoader(test_data_padded, batch_size = batch_size, shuffle = False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EzvjfbWHsYH3"
      },
      "source": [
        "---\n",
        "## Define the Machine Translation Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t4CuMhNWZ696"
      },
      "source": [
        "---\n",
        "### Encoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6KLpeUUOsYH3"
      },
      "source": [
        "**Exercise 6 [1/1]**:\n",
        "1. Implement the `Encoder` class to process the input sequence `src` in the `forward()` method and return `(hidden, cell)` as the context vector. The input arguments include:\n",
        "- `input_dim`: the source vocabulary size\n",
        "- `emb_dim`: the dimension of the embedding space\n",
        "- `hidden_dim`: the number of neurons in each RNN unit\n",
        "- `num_layers`: the depth of the RNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "ewFLT-DeZz8l"
      },
      "outputs": [],
      "source": [
        "# Encoder class processes the input sequence and returns hidden and cell states\n",
        "class Encoder(nn.Module):\n",
        "\n",
        "    def __init__(self, input_dim, emb_dim, hidden_dim, num_layers = 1):\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        self.input_dim = input_dim\n",
        "        self.embedding = nn.Embedding(input_dim, emb_dim) # Embedding layer for word vectors\n",
        "        self.lstm = nn.LSTM(emb_dim, hidden_dim, num_layers = num_layers, batch_first = True) # LSTM layer\n",
        "\n",
        "    def forward(self, src):\n",
        "\n",
        "        embedded = self.embedding(src) # Convert input words to embeddings\n",
        "        outputs, (hidden, cell) = self.lstm(embedded) # Pass through the LSTM\n",
        "\n",
        "        return hidden, cell"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXH3ydYAug6O"
      },
      "source": [
        "---\n",
        "### Decoder"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3shDcNesYH4"
      },
      "source": [
        "**Exercise 7 [1/1]**:\n",
        "1. Implement the `Decoder` class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "okopBItRM2Pm"
      },
      "outputs": [],
      "source": [
        "# Decoder class generates output based on the previous output and hidden states\n",
        "class Decoder(nn.Module):\n",
        "\n",
        "    def __init__(self, output_dim, emb_dim, hidden_dim, num_layers = 1):\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        self.output_dim = output_dim\n",
        "        self.embedding = nn.Embedding(output_dim, emb_dim) # Embedding layer for target words\n",
        "        self.lstm = nn.LSTM(emb_dim, hidden_dim, num_layers = num_layers, batch_first = True) # LSTM layer\n",
        "        self.fc_out = nn.Linear(hidden_dim, output_dim) #  Linear layer to output vocabulary-sized predictions\n",
        "\n",
        "    def forward(self, input, hidden, cell):\n",
        "\n",
        "        input = input.unsqueeze(1) # Add a dimension for the sequence length\n",
        "        embedded = self.embedding(input) # Convert input word to embedding\n",
        "        output, (hidden, cell) = self.lstm(embedded, (hidden, cell)) # Pass through the LSTM\n",
        "        prediction = self.fc_out(output) # Get the output prediction\n",
        "\n",
        "        return prediction, hidden, cell"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iNdGqCkrsYH4"
      },
      "source": [
        "---\n",
        "### Seq2Seq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ixj4Lkn7uvce"
      },
      "source": [
        "**Exercise 8 [1/1]**:\n",
        "1. Implement the `Seq2Seq` class."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "t2wi_GKMXmSo"
      },
      "outputs": [],
      "source": [
        "# Seq2Seq class combines encoder and decoder, using teacher forcing during training\n",
        "class Seq2Seq(nn.Module):\n",
        "\n",
        "    def __init__(self, encoder, decoder, device):\n",
        "\n",
        "        super().__init__()\n",
        "\n",
        "        self.encoder = encoder\n",
        "        self.decoder = decoder\n",
        "        self.device = device\n",
        "\n",
        "    def forward(self, src, tgt, teacher_forcing_ratio = 0.5):\n",
        "\n",
        "        batch_size = src.size(0)  # Get the batch size from the source tensor\n",
        "        tgt_len = tgt.size(1)     # Get the target sequence length\n",
        "        tgt_vocab_size = self.decoder.output_dim  # Get the vocabulary size from the decoder's output\n",
        "\n",
        "        # Get initial hidden and cell states from the encoder\n",
        "        hidden, cell = self.encoder(src)\n",
        "\n",
        "        # Initialize an output tensor with zeros to store the predictions\n",
        "        outputs = torch.zeros(batch_size, tgt_len, tgt_vocab_size).to(self.device)\n",
        "\n",
        "        # Initialize the input for the decoder\n",
        "        input = tgt[:, 0]\n",
        "\n",
        "        # Loop through each time step of the target sequence\n",
        "        for t in range(1, tgt_len):\n",
        "\n",
        "            # Make a prediction for the current time step\n",
        "            prediction, hidden, cell = self.decoder(input, hidden, cell)\n",
        "            outputs[:, t, :] = prediction.squeeze(1)\n",
        "\n",
        "            # Get the token with the highest probability for the next input\n",
        "            top1 = prediction.squeeze(1).argmax(1)\n",
        "\n",
        "            # Decide whether to use the ground truth token or the model's prediction\n",
        "            input = tgt[:, t] if torch.rand(1).item() < teacher_forcing_ratio else top1\n",
        "\n",
        "        return outputs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "voj6y-hQsYH5"
      },
      "outputs": [],
      "source": [
        "# Set device for computation (GPU if available, otherwise CPU)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "DwMWQRoUsYH5"
      },
      "outputs": [],
      "source": [
        "# Initialize the encoder, decoder, and Seq2Seq model\n",
        "encoder = Encoder(input_dim = len(de_vocab), emb_dim = 256, hidden_dim = 512, num_layers = 2)\n",
        "decoder = Decoder(output_dim = len(en_vocab), emb_dim = 256, hidden_dim = 512, num_layers = 2)\n",
        "model = Seq2Seq(encoder, decoder, device).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "x8K6RD6Qm481"
      },
      "outputs": [],
      "source": [
        "# Get a batch of data from the training dataloader\n",
        "batch = next(iter(train_dataloader))\n",
        "(src, tgt) = batch[\"de\"], batch[\"en\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S7Azpk_JsYH5",
        "outputId": "1d799822-4e6e-4ad6-d535-a9f9e6bc871c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max index in src: 18648\n",
            "Max index in tgt: 10204\n"
          ]
        }
      ],
      "source": [
        "print(\"Max index in src:\", src.max().item())\n",
        "print(\"Max index in tgt:\", tgt.max().item())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2hkE_MtmsYH5",
        "outputId": "649751f5-ebef-494f-da49-48dd64c916de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Input_dim for encoder: 18680\n",
            "Output_dim for decoder: 10218\n"
          ]
        }
      ],
      "source": [
        "print(\"Input_dim for encoder:\", encoder.input_dim)\n",
        "print(\"Output_dim for decoder:\", decoder.output_dim)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "FRu1aOVzUAFG"
      },
      "outputs": [],
      "source": [
        "# Get another batch of data and move it to the model's device\n",
        "batch = next(iter(train_dataloader))\n",
        "(src, tgt) = batch[\"de\"].to(model.device), batch[\"en\"].to(model.device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "MFm0JONrsYH6"
      },
      "outputs": [],
      "source": [
        "# Perform a forward pass through the model with the batch\n",
        "outputs = model(src, tgt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWfQzVjsXECU",
        "outputId": "48607c1b-452a-40e5-873e-ee758f2b11f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([64, 100, 10218])\n",
            "tensor([ 0.0206,  0.0159,  0.0054,  ...,  0.0003, -0.0343,  0.0091],\n",
            "       device='cuda:0', grad_fn=<SliceBackward0>)\n"
          ]
        }
      ],
      "source": [
        "print(outputs.size())\n",
        "print(outputs[0, 10, :])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dayiBJLSsYH6"
      },
      "source": [
        "---\n",
        "## Training Seq2Seq Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-eKsTtZDoE_C"
      },
      "source": [
        "**Exercise 9 [1/1]**:\n",
        "1. Implement `train()` and `evaluate()` functions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "dHG-DCPXoLPh"
      },
      "outputs": [],
      "source": [
        "def train(model, data_loader, optimizer, criterion):\n",
        "\n",
        "    # Set the model to training mode\n",
        "    model.train()\n",
        "\n",
        "    epoch_loss = 0\n",
        "\n",
        "    # Iterate over the data loader\n",
        "    for i, batch in enumerate(data_loader):\n",
        "\n",
        "        # Move the source and target tensors to the device\n",
        "        src, tgt = batch[\"de\"].to(model.device), batch[\"en\"].to(model.device)\n",
        "\n",
        "        # Zero the gradients from the previous step\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Perform a forward pass through the model\n",
        "        output = model(src, tgt)\n",
        "\n",
        "        # Reshape output and target to calculate loss\n",
        "        output = output.view(-1, output.shape[2])\n",
        "        tgt = tgt.view(-1)\n",
        "\n",
        "        # Calculate the loss using the criterion\n",
        "        loss = criterion(output, tgt)\n",
        "\n",
        "        # Backpropagate the loss\n",
        "        loss.backward()\n",
        "\n",
        "        # Update model parameters using the optimizer\n",
        "        optimizer.step()\n",
        "\n",
        "        # Add the current batch's loss to the running total\n",
        "        epoch_loss += loss.item()\n",
        "\n",
        "    return epoch_loss / len(data_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "7o504HM8u4qa"
      },
      "outputs": [],
      "source": [
        "def evaluate(model, data_loader, criterion):\n",
        "\n",
        "    # Set the model to evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    epoch_loss = 0\n",
        "\n",
        "    # No need to track gradients during evaluation\n",
        "    with torch.no_grad():\n",
        "\n",
        "        # Iterate over the validation data loader\n",
        "        for i, batch in enumerate(data_loader):\n",
        "\n",
        "            # Move the source and target tensors to the device\n",
        "            src, tgt = batch[\"de\"].to(model.device), batch[\"en\"].to(model.device)\n",
        "\n",
        "            # Perform a forward pass through the model\n",
        "            output = model(src, tgt)\n",
        "\n",
        "            # Reshape output and target to calculate loss\n",
        "            output = output.view(-1, output.shape[2])\n",
        "            tgt = tgt.view(-1)\n",
        "\n",
        "            # Calculate the loss\n",
        "            loss = criterion(output, tgt)\n",
        "            epoch_loss += loss.item()\n",
        "\n",
        "    return epoch_loss / len(data_loader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "LeX50HBysYH7"
      },
      "outputs": [],
      "source": [
        "# Set the random seed for reproducibility\n",
        "torch.manual_seed(1)\n",
        "torch.cuda.manual_seed(1)\n",
        "torch.backends.cudnn.deterministic = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "bP-rbusRsYH7"
      },
      "outputs": [],
      "source": [
        "# Define the device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "4aEIHmRCsYH8"
      },
      "outputs": [],
      "source": [
        "# Initialize the encoder, decoder, and Seq2Seq model\n",
        "encoder = Encoder(input_dim = len(de_vocab), emb_dim = 256, hidden_dim = 512, num_layers = 2)\n",
        "decoder = Decoder(output_dim = len(en_vocab), emb_dim = 256, hidden_dim = 512, num_layers = 2)\n",
        "model = Seq2Seq(encoder, decoder, device).to(device)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "k1rRhxwTsYH8"
      },
      "outputs": [],
      "source": [
        "# Define PAD_IDX to ignore padding during loss calculation\n",
        "PAD_IDX = de_vocab[\"<pad>\"]\n",
        "criterion = nn.CrossEntropyLoss(ignore_index = PAD_IDX) # CrossEntropyLoss with padding ignored\n",
        "optimizer = optim.Adam(model.parameters(), lr = 0.01) # Adam optimizer for model training"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tshJMCSXqei2",
        "outputId": "e71eab37-33de-4e4e-d358-0e5994135097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1, Train Loss: 5.1670, Valid Loss: 4.8050\n",
            "Epoch: 2, Train Loss: 4.6609, Valid Loss: 4.6104\n",
            "Epoch: 3, Train Loss: 4.4606, Valid Loss: 4.4845\n",
            "Epoch: 4, Train Loss: 4.3381, Valid Loss: 4.4119\n",
            "Epoch: 5, Train Loss: 4.2492, Valid Loss: 4.4085\n",
            "Epoch: 6, Train Loss: 4.2133, Valid Loss: 4.4354\n",
            "Epoch: 7, Train Loss: 4.1519, Valid Loss: 4.2969\n",
            "Epoch: 8, Train Loss: 4.1095, Valid Loss: 4.4653\n",
            "Epoch: 9, Train Loss: 4.0849, Valid Loss: 4.3757\n",
            "Epoch: 10, Train Loss: 4.0402, Valid Loss: 4.3039\n"
          ]
        }
      ],
      "source": [
        "# Lists to store the training and validation losses for each epoch\n",
        "train_losses = []\n",
        "valid_losses = []\n",
        "\n",
        "# Training loop\n",
        "for epoch in range(10):\n",
        "\n",
        "    # Train the model and calculate training loss\n",
        "    train_loss = train(model, train_dataloader, optimizer, criterion)\n",
        "    train_losses.append(train_loss)\n",
        "\n",
        "    # Evaluate the model on the validation data\n",
        "    valid_loss = evaluate(model, valid_dataloader, criterion)\n",
        "    valid_losses.append(valid_loss)\n",
        "\n",
        "    print(f\"Epoch: {epoch + 1}, Train Loss: {train_loss:.4f}, Valid Loss: {valid_loss:.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cL-1Kiw0zm76"
      },
      "source": [
        "---\n",
        "## Evaluation and Translation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OYnt01u6sYH8"
      },
      "source": [
        "**Exercise 10 [1/1]**:\n",
        "1. Implement `translate_sentence()` to perform machine translation using the trained model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "qyy2cUeSzoCP"
      },
      "outputs": [],
      "source": [
        "def translate_sentence(sentence, model, src_vocab, tgt_vocab, max_len = 10):\n",
        "\n",
        "    # Set the model to evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    # Split the input sentence into tokens and convert them to indices using the source vocabulary\n",
        "    tokens = sentence.split()\n",
        "    src_indices = [src_vocab.get(token, src_vocab[\"<unk>\"]) for token in tokens]\n",
        "    src_tensor = torch.tensor(src_indices).unsqueeze(0).to(model.device)\n",
        "\n",
        "    # Pass the source tensor through the encoder to obtain hidden and cell states\n",
        "    with torch.no_grad():\n",
        "        hidden, cell = model.encoder(src_tensor)\n",
        "\n",
        "    # Initialize the target sentence with the <sos> token\n",
        "    tgt_ids = [tgt_vocab[\"<sos>\"]]\n",
        "    tgt_tensor = torch.tensor([tgt_ids[-1]]).to(model.device)\n",
        "\n",
        "    # Generate the translated sentence\n",
        "    for _ in range(max_len):\n",
        "\n",
        "        output, hidden, cell = model.decoder(tgt_tensor, hidden, cell)\n",
        "\n",
        "        # Get the predicted token\n",
        "        pred_token_id = output.argmax(2).item()\n",
        "\n",
        "        # Append the predicted token to the target sentence\n",
        "        tgt_ids.append(pred_token_id)\n",
        "\n",
        "        # Stop translation if the <eos> token is predicted\n",
        "        if pred_token_id == tgt_vocab[\"<eos>\"]:\n",
        "            break\n",
        "\n",
        "        # Prepare the target tensor for the next iteration\n",
        "        tgt_tensor = torch.tensor([pred_token_id]).to(model.device)\n",
        "\n",
        "    # Convert the target token indices back to words using the target vocabulary\n",
        "    translated_sentence = [list(tgt_vocab.keys())[i] for i in tgt_ids[1:]]\n",
        "\n",
        "    return translated_sentence"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "V54mQoFV4Hae"
      },
      "outputs": [],
      "source": [
        "# Example translation using the trained model\n",
        "sentence = test_data[0][\"de\"]\n",
        "expected_translation = test_data[0][\"en\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
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        },
        "id": "dZZcZHMjsYH9",
        "outputId": "8f36e02b-2d63-4db9-9c9d-964a764a3774"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ein Mann mit einem orangefarbenen Hut, der etwas anstarrt.\n",
            "A man in an orange hat starring at something.\n"
          ]
        }
      ],
      "source": [
        "print(sentence)\n",
        "print(expected_translation)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "1p1sWE4zsYH9"
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      "source": [
        "# Translate the sentence and output the translation\n",
        "translation = translate_sentence(sentence, model, de_vocab, en_vocab)"
      ]
    },
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      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
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        },
        "id": "VGP4kzRu4Qql",
        "outputId": "cbf79e6e-883c-4df5-99f6-f2cf9a4093b4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['a', 'and', 'a', 'and', 'and', 'a', 'a', 'are', 'playing', 'the']\n"
          ]
        }
      ],
      "source": [
        "print(translation)"
      ]
    }
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