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            "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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            "\u001b[?25hInstalling collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-3.0.2 dill-0.3.8 multiprocess-0.70.16 xxhash-3.5.0\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers datasets\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/ml-olympiad-machine-translation-french-wolof.zip -d /content/\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9EMOBKrRjkun",
        "outputId": "a272288b-e2c1-4589-804b-d3ee1ef526fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/ml-olympiad-machine-translation-french-wolof.zip\n",
            "  inflating: /content/test.csv       \n",
            "  inflating: /content/train.csv      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install evaluate\n"
      ],
      "metadata": {
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C4X1yTLmuVc",
        "outputId": "b75d281d-5ae2-4860-f22e-d7c23fe6370a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting evaluate\n",
            "  Downloading evaluate-0.4.3-py3-none-any.whl.metadata (9.2 kB)\n",
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            "Downloading evaluate-0.4.3-py3-none-any.whl (84 kB)\n",
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            "\u001b[?25hInstalling collected packages: evaluate\n",
            "Successfully installed evaluate-0.4.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset\n"
      ],
      "metadata": {
        "id": "Psj6mUEenaZX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import evaluate\n",
        "\n",
        "# Load BLEU metric\n",
        "bleu_metric = evaluate.load(\"bleu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237,
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            "babaea6ba2ce40ed90c783f375c5e172",
            "ef31ec74f4c44801b70e310840e6a4b7"
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        },
        "collapsed": true,
        "id": "TZHv0w1tmNzy",
        "outputId": "e4814abe-d1fb-4432-cd3f-841838bd760a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        {
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from transformers import MarianMTModel, MarianTokenizer, Trainer, TrainingArguments\n",
        "import evaluate\n",
        "\n",
        "# Load your train and test datasets\n",
        "train_data = pd.read_csv(\"train.csv\")\n",
        "test_data = pd.read_csv(\"test.csv\")\n",
        "\n",
        "# Initialize the tokenizer and model\n",
        "model_name = \"Helsinki-NLP/opus-mt-fr-en\"  # You might need a custom or retrained French-Wolof model\n",
        "tokenizer = MarianTokenizer.from_pretrained(model_name)\n",
        "model = MarianMTModel.from_pretrained(model_name)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330,
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        "id": "O49ACbG1lCSc",
        "outputId": "fb302e01-1fca-4206-9216-d005b4d27c50"
      },
      "execution_count": null,
      "outputs": [
        {
          "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": "ae64b9adb829483ba1c31731d158e840"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb25076a738c49d7a0c20df864ca0236"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "02d8032d55e441da8baec1e028bca3b3"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fae084cfa00f4eedac6934136ded680a"
            }
          },
          "metadata": {}
        },
        {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cf5274f7447486298c7e2e7fa240bef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
            "  warnings.warn(\n"
          ]
        },
        {
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      "source": [
        "train_data.head()"
      ],
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          "height": 275
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        "id": "gqWq-eaAoj1x",
        "outputId": "c85a8f60-1e88-42e3-e179-a85850c033fc"
      },
      "execution_count": null,
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        {
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            "text/plain": [
              "   ID                                             FRENCH  \\\n",
              "0   0                                Bonne continuation.   \n",
              "1   1  Vérifiez que votre main soit détendue le plus ...   \n",
              "2   2  Ce renouveau politique, véritable alternative,...   \n",
              "3   3  Mais je ne doute point, vu la mobilisation exc...   \n",
              "4   4  Il faut que tu tiennes ta promesse, me dit dou...   \n",
              "\n",
              "                                               WOLOF  \n",
              "0             Yal na la jig te yeneeni ndam topp ci.  \n",
              "1  Wóo ni sa loxo bi téguna bu baax booy bindee l...  \n",
              "2  Beesal gi am ci pólótig bi dana tax, ci lu wér...  \n",
              "3  Waaye wéddiwuma ko, soo gisee ndaje mu am dool...  \n",
              "4  Ndoomu buur si toogaat ci sama wet, wax ma ndà...  "
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "train_data",
              "summary": "{\n  \"name\": \"train_data\",\n  \"rows\": 7263,\n  \"fields\": [\n    {\n      \"column\": \"ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2096,\n        \"min\": 0,\n        \"max\": 7262,\n        \"num_unique_values\": 7263,\n        \"samples\": [\n          1414,\n          6459,\n          5960\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FRENCH\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7244,\n        \"samples\": [\n          \"Il avait eu peur, bien s\\u00fbr !\",\n          \"Sheffield menait 2 buts \\u00e0 z\\u00e9ro avant la sortie de celui ci.\",\n          \"Mais c\\u2019est bien sur sa sortie que cette \\u00e9quipe, souvent favorite mais jamais titr\\u00e9e, sera jug\\u00e9e.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WOLOF\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7233,\n        \"samples\": [\n          \"Bi \\u00f1u ci tegee 250 at, Guinness magg na doon ab ligg\\u00e9eyukaay bu mag buy jur 10 miliyaari oro (US$14,7 miliyaar) at mu nekk.\\n\",\n          \"Lekkleen, defaraatleen seen toogaay ci gannaaw \\u00f1iy dajale seen xaalis, nun dinanu taxaw xeexal \\u00f1i boole dajale seen xaalis.\",\n          \"L\\u00e9egi ndoomu buur si daldi mer bay futt.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preprocess the data\n",
        "def preprocess_data(examples):\n",
        "    # Tokenize French input\n",
        "    inputs = tokenizer(examples[\"FRENCH\"], max_length=128, truncation=True, padding=\"max_length\")\n",
        "    # Tokenize Wolof output (label)\n",
        "    targets = tokenizer(examples[\"Wolof\"], max_length=128, truncation=True, padding=\"max_length\")\n",
        "\n",
        "    inputs[\"labels\"] = targets[\"input_ids\"]\n",
        "    return inputs\n",
        "\n",
        "# Convert the train and test data into Hugging Face datasets\n",
        "train_dataset = Dataset.from_pandas(train_data)\n",
        "test_dataset = Dataset.from_pandas(test_data)\n",
        "\n",
        "# Apply the preprocessing function\n",
        "train_dataset = train_dataset.map(preprocess_data, batched=True)\n",
        "test_dataset = test_dataset.map(preprocess_data, batched=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388,
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7263 [00:00<?, ? examples/s]"
            ],
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        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'Wolof'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-29d77220b11a>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;31m# Apply the preprocessing function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mtest_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    558\u001b[0m         }\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3053\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3054\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3055\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3056\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3057\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3456\u001b[0m                         )  # Something simpler?\n\u001b[1;32m   3457\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m                             batch = apply_function_on_filtered_inputs(\n\u001b[0m\u001b[1;32m   3459\u001b[0m                                 \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                                 \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mapply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3318\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwith_rank\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3319\u001b[0m                 \u001b[0madditional_args\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3320\u001b[0;31m             \u001b[0mprocessed_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madditional_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyDict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3322\u001b[0m                 processed_inputs = {\n",
            "\u001b[0;32m<ipython-input-15-29d77220b11a>\u001b[0m in \u001b[0;36mpreprocess_data\u001b[0;34m(examples)\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FRENCH\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"max_length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# Tokenize Wolof output (label)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mtargets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Wolof\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"max_length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0minputs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"labels\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"input_ids\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys_to_format\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'Wolof'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def preprocess_data(examples):\n",
        "    # Adjust these keys based on your actual column names\n",
        "    french_texts = examples[\"FRENCH\"]\n",
        "    wolof_texts = examples[\"WOLOF\"]\n",
        "\n",
        "    model_inputs = tokenizer(french_texts, max_length=128, truncation=True, padding=\"max_length\")\n",
        "\n",
        "    with tokenizer.as_target_tokenizer():\n",
        "        labels = tokenizer(wolof_texts, max_length=128, truncation=True, padding=\"max_length\")\n",
        "        model_inputs[\"labels\"] = labels[\"input_ids\"]\n",
        "\n",
        "    return model_inputs\n"
      ],
      "metadata": {
        "id": "8nScLbWNnDOx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the preprocessing function\n",
        "train_dataset = train_dataset.map(preprocess_data, batched=True)\n",
        "test_dataset = test_dataset.map(preprocess_data, batched=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403,
          "referenced_widgets": [
            "234ef15172854a73843de1f75f008ec9",
            "8a8ddfb9153f480d9c34607f36a76a89",
            "c128b11d71444309aa266050a8db4d92",
            "0e4ad52a48ad4f298b422c4fa31854c8",
            "525355341f5d4ba29e9a6c36dc2eb655",
            "3e4e2243bef64dfe90e5fc76d6a0a460",
            "489532395a2b4070ae6aaafcab545dbb",
            "001224096a1b495794390eff42e802a3",
            "8fd3eded88d54eb29236e21cb9f23a87",
            "785910077f4c42d3b0d1cfd44dfaee47",
            "1d32828894244155bca34129525067bd",
            "f702a15355b4453d80640b066ab07b2a",
            "bfcce8816e6a473d80dff351ede5dcfc",
            "1265d9bb828c49cc93980618a5b247a4",
            "76cfbf8148b84eae8719177639b26113",
            "ad2e8a53cff64f0abaf4c65aca67c052",
            "ad3ef82b197f43a59bcaf33ea51efbb5",
            "99b2361a343d4be2915e7d145c10a372",
            "0e1e8fb373d24849ad3c0ee6e35a2182",
            "298d05ac27764ada8ff3e9220acf226e",
            "28a95295304644d8b84aa0bb0bf38a25",
            "b11a5b77d6ac40ce8497026f9d016fd6"
          ]
        },
        "id": "lnjJn9_NpOLA",
        "outputId": "19e26b9d-d152-4e4f-8dbd-970469c8e40f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/7263 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "234ef15172854a73843de1f75f008ec9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2270 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f702a15355b4453d80640b066ab07b2a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'WOLOF'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-20-7c14528251ea>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Apply the preprocessing function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mtrain_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mtest_dataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpreprocess_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatched\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    558\u001b[0m         }\n\u001b[1;32m    559\u001b[0m         \u001b[0;31m# apply actual function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 560\u001b[0;31m         \u001b[0mout\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mUnion\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"DatasetDict\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    561\u001b[0m         \u001b[0mdatasets\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Dataset\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    562\u001b[0m         \u001b[0;31m# re-apply format to the output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, load_from_cache_file, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, num_proc, suffix_template, new_fingerprint, desc)\u001b[0m\n\u001b[1;32m   3053\u001b[0m                     \u001b[0mdesc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdesc\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m\"Map\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3054\u001b[0m                 ) as pbar:\n\u001b[0;32m-> 3055\u001b[0;31m                     \u001b[0;32mfor\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_map_single\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mdataset_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3056\u001b[0m                         \u001b[0;32mif\u001b[0m \u001b[0mdone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3057\u001b[0m                             \u001b[0mshards_done\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36m_map_single\u001b[0;34m(shard, function, with_indices, with_rank, input_columns, batched, batch_size, drop_last_batch, remove_columns, keep_in_memory, cache_file_name, writer_batch_size, features, disable_nullable, fn_kwargs, new_fingerprint, rank, offset)\u001b[0m\n\u001b[1;32m   3456\u001b[0m                         )  # Something simpler?\n\u001b[1;32m   3457\u001b[0m                         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3458\u001b[0;31m                             batch = apply_function_on_filtered_inputs(\n\u001b[0m\u001b[1;32m   3459\u001b[0m                                 \u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3460\u001b[0m                                 \u001b[0mindices\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/arrow_dataset.py\u001b[0m in \u001b[0;36mapply_function_on_filtered_inputs\u001b[0;34m(pa_inputs, indices, check_same_num_examples, offset)\u001b[0m\n\u001b[1;32m   3318\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mwith_rank\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3319\u001b[0m                 \u001b[0madditional_args\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3320\u001b[0;31m             \u001b[0mprocessed_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mfn_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madditional_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfn_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3321\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLazyDict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3322\u001b[0m                 processed_inputs = {\n",
            "\u001b[0;32m<ipython-input-19-7c74d6d35987>\u001b[0m in \u001b[0;36mpreprocess_data\u001b[0;34m(examples)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;31m# Adjust these keys based on your actual column names\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mfrench_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"FRENCH\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mwolof_texts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexamples\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"WOLOF\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mmodel_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrench_texts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtruncation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"max_length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/datasets/formatting/formatting.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    276\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 277\u001b[0;31m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    278\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys_to_format\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    279\u001b[0m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'WOLOF'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "import pandas as pd\n",
        "from transformers import MarianMTModel, MarianTokenizer, Trainer, TrainingArguments\n",
        "import evaluate\n",
        "\n",
        "# Load and inspect the training and test datasets\n",
        "train_data = pd.read_csv(\"train.csv\")  # Adjust with your train CSV path\n",
        "test_data = pd.read_csv(\"test.csv\")    # Adjust with your test CSV path\n",
        "\n",
        "# Ensure the train dataset has FRENCH and WOLOF columns, while the test dataset only has FRENCH\n",
        "print(\"Training data columns:\", train_data.columns)\n",
        "print(\"Testing data columns:\", test_data.columns)\n",
        "\n",
        "# Convert pandas dataframes to Hugging Face Dataset format\n",
        "train_dataset = Dataset.from_pandas(train_data)\n",
        "test_dataset = Dataset.from_pandas(test_data)\n",
        "\n",
        "# Load the MarianMT tokenizer and model for French to Wolof translation\n",
        "model_name = \"Helsinki-NLP/opus-mt-fr-en\"  # Starting with French-to-English, to be fine-tuned\n",
        "tokenizer = MarianTokenizer.from_pretrained(model_name)\n",
        "model = MarianMTModel.from_pretrained(model_name)\n",
        "\n",
        "# Preprocessing function for the train dataset, which includes target labels (WOLOF)\n",
        "def preprocess_train_data(examples):\n",
        "    # Tokenize the French input\n",
        "    model_inputs = tokenizer(examples[\"FRENCH\"], max_length=128, truncation=True, padding=\"max_length\")\n",
        "    # Tokenize the Wolof target labels\n",
        "    model_inputs[\"labels\"] = tokenizer(text_target=examples[\"WOLOF\"], max_length=128, truncation=True, padding=\"max_length\")[\"input_ids\"]\n",
        "    return model_inputs\n",
        "\n",
        "# Preprocessing function for the test dataset, which only includes source text (FRENCH)\n",
        "def preprocess_test_data(examples):\n",
        "    # Tokenize only the French input for testing\n",
        "    return tokenizer(examples[\"FRENCH\"], max_length=128, truncation=True, padding=\"max_length\")\n",
        "\n",
        "# Apply preprocessing to train and test datasets\n",
        "train_dataset = train_dataset.map(preprocess_train_data, batched=True)\n",
        "test_dataset = test_dataset.map(preprocess_test_data, batched=True)\n",
        "\n",
        "# Define training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"./results\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    per_device_train_batch_size=16,\n",
        "    per_device_eval_batch_size=16,\n",
        "    num_train_epochs=3,\n",
        "    weight_decay=0.01,\n",
        "    save_total_limit=2,\n",
        ")\n",
        "\n",
        "# Initialize the Trainer\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        ")\n",
        "\n",
        "# Start training\n",
        "trainer.train()\n",
        "\n",
        "# Evaluate the model\n",
        "trainer.evaluate()\n",
        "\n",
        "# Define a function to translate new French sentences into Wolof\n",
        "def translate_text(text):\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\", padding=True, truncation=True)\n",
        "    translated_tokens = model.generate(**inputs)\n",
        "    return tokenizer.decode(translated_tokens[0], skip_special_tokens=True)\n",
        "\n",
        "# Example: Test translation with a sample French sentence\n",
        "french_text = \"Bonne continuation.\"\n",
        "wolof_translation = translate_text(french_text)\n",
        "print(f\"French: {french_text}\")\n",
        "print(f\"Wolof: {wolof_translation}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547,
          "referenced_widgets": [
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            "a272b5e59f9949edb98db53f50973267",
            "89d605ee1374481ca09d66ac42dc82fb"
          ]
        },
        "id": "vOq8sLnTpQTN",
        "outputId": "d3faa733-2478-490d-910d-cdcfde09fe51"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training data columns: Index(['ID', 'FRENCH', 'WOLOF'], dtype='object')\n",
            "Testing data columns: Index(['ID', 'FRENCH'], dtype='object')\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/tokenization_utils_base.py:1601: FutureWarning: `clean_up_tokenization_spaces` was not set. It will be set to `True` by default. This behavior will be depracted in transformers v4.45, and will be then set to `False` by default. For more details check this issue: https://github.com/huggingface/transformers/issues/31884\n",
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