Easy_multi_task_ModernBERT_fine_tuning_with_tasknet.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/sileod/tasknet/blob/main/Easy_multi_task_ModernBERT_fine_tuning_with_tasknet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nBqMVdixWaqC"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.system('pip install triton accelerate tasknet tasksource')\n",
        "os.environ[\"WANDB_DISABLED\"] = \"true\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install git+https://github.com/huggingface/transformers.git -q"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2GMQyy4772kV",
        "outputId": "7c6cb43d-6604-452c-c68f-74f3172c3c9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tasknet as tn\n",
        "from datasets import load_dataset\n",
        "import datasets\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=UserWarning, message=\".*seems not to be NE tag.*\") #seqeval issue"
      ],
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/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"
          ]
        },
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              "Downloading builder script:   0%|          | 0.00/6.34k [00:00<?, ?B/s]"
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        "outputId": "7330b791-ae78-4bc8-ded5-907e13751faf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/variables/functions.py:725: UserWarning: Graph break due to unsupported builtin None.Random.random. This function is either a Python builtin (e.g. _warnings.warn) or a third-party C/C++ Python extension (perhaps created with pybind). If it is a Python builtin, please file an issue on GitHub so the PyTorch team can add support for it and see the next case for a workaround. If it is a third-party C/C++ Python extension, please either wrap it into a PyTorch-understood custom operator (see https://pytorch.org/tutorials/advanced/custom_ops_landing_page.html for more details) or, if it is traceable, use torch.compiler.allow_in_graph.\n",
            "  torch._dynamo.utils.warn_once(msg)\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1078' max='1078' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1078/1078 03:46, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>F1</th>\n",
              "      <th>Name</th>\n",
              "      <th>Size</th>\n",
              "      <th>Index</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.371400</td>\n",
              "      <td>0.839300</td>\n",
              "      <td>0.514286</td>\n",
              "      <td>0.499579</td>\n",
              "      <td>glue/wnli</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.371400</td>\n",
              "      <td>0.351788</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.864527</td>\n",
              "      <td>glue/mrpc</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.102600</td>\n",
              "      <td>1.308422</td>\n",
              "      <td>0.514286</td>\n",
              "      <td>0.512695</td>\n",
              "      <td>glue/wnli</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.102600</td>\n",
              "      <td>0.701844</td>\n",
              "      <td>0.892157</td>\n",
              "      <td>0.870139</td>\n",
              "      <td>glue/mrpc</td>\n",
              "      <td>408</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/9.64k [00:00<?, ?B/s]"
            ],
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          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/3.72k [00:00<?, ?B/s]"
            ],
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=1078, training_loss=0.23698728911730707, metrics={'train_runtime': 237.4655, 'train_samples_per_second': 36.241, 'train_steps_per_second': 4.54, 'total_flos': 2948257146052608.0, 'train_loss': 0.23698728911730707, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "class args:\n",
        "    model_name = \"sileod/deberta-v3-small-tasksource-nli\"\n",
        "    model_name = \"tasksource/ModernBERT-base-nli\"\n",
        "    # remaining arguments are from https://huggingface.co/docs/transformers/v4.24.0/en/main_classes/trainer#transformers.TrainingArguments\n",
        "    learning_rate = 3e-5\n",
        "    #max_steps = 500\n",
        "    num_train_epochs =2\n",
        "\n",
        "\n",
        "\n",
        "mrpc = tn.Classification(\n",
        "    dataset=load_dataset(\"glue\", \"mrpc\"), s1=\"sentence1\", s2=\"sentence2\", y=\"label\"\n",
        ")\n",
        "\n",
        "wnli = tn.AutoTask('glue/wnli',main_split=\"validation\")\n",
        "\n",
        "\n",
        "conll_pos = tn.TokenClassification(\n",
        "    dataset=\"conll2003\", # A dataset string/tuple can also be passed and loaded internally with datasets.load_dataset\n",
        "    name=\"conll_pos\", tokens=\"tokens\", y=\"pos_tags\"\n",
        ")\n",
        "\n",
        "\n",
        "reco = tn.MultipleChoice(\n",
        "    tn.utils.train_validation_test_split(\n",
        "        tn.load_dataset(\"sileod/movie_recommendation\")[\"test\"]\n",
        "    ),\n",
        "    s1=\"question\",\n",
        "    y=\"label\",\n",
        "    choices=[\"answer_0\", \"answer_1\", \"answer_2\", \"answer_3\"],\n",
        ")\n",
        "\n",
        "stsb = tn.AutoTask(\"glue/stsb\") # https://github.com/sileod/tasksource/blob/main/tasks.md\n",
        "\n",
        "tasks = [wnli,mrpc]\n",
        "models = tn.Model(tasks, args) # list of models; by default, shared encoder, task-specific CLS token task-specific head\n",
        "trainer = tn.Trainer(models, tasks, args) # tasks are uniformly sampled by default\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.evaluate()"
      ],
      "metadata": {
        "id": "1rfhKqP71U9o",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "outputId": "a950ffce-08ae-4946-e8fb-09cb0170b9b4"
      },
      "execution_count": null,
      "outputs": [
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              "      <progress value='56' max='5' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5/5 00:03]\n",
              "    </div>\n",
              "    "
            ]
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'eval_loss': 0.7018182873725891,\n",
              " 'eval_accuracy': 0.8921568627450981,\n",
              " 'eval_f1': 0.8701388888888889,\n",
              " 'eval_name': 'glue/mrpc',\n",
              " 'eval_size': 408,\n",
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