Twitter_Roberta_Sentiment_Pretrained_EmotionalPolarity.ipynb

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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0IYl0s_yfux",
        "outputId": "190e5e20-9711-4eb0-e65b-8c8017302ba1"
      },
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.28.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (2023.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "--2023-05-08 03:12:19--  https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4321231 (4.1M) [application/octet-stream]\n",
            "Saving to: ‘wassa2023-shared-task.tar.gz’\n",
            "\n",
            "wassa2023-shared-ta 100%[===================>]   4.12M  --.-KB/s    in 0.08s   \n",
            "\n",
            "2023-05-08 03:12:19 (48.7 MB/s) - ‘wassa2023-shared-task.tar.gz’ saved [4321231/4321231]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!pip install torch transformers\n",
        "!rm -f wassa2023-shared-task.tar.gz\n",
        "!wget 'https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz'\n",
        "!tar -xf wassa2023-shared-task.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqeNheNENmsa",
        "outputId": "b4d5ebdb-69b2-4a28-e9a7-f7043b4c09e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "\n",
        "class OrdinalLabelEncoder:\n",
        "    def get_feature_names_out(self, prefix=\"X\"):\n",
        "      return list(map(lambda x: f\"{prefix}_{x}\", self.classes_))\n",
        "    \n",
        "    def fit(self, y):\n",
        "      self.classes_ = sorted(list(set(y)))\n",
        "      self.sorter = np.argsort(self.classes_)\n",
        "      \n",
        "    def fit_transform(self, y):\n",
        "        y = y.to_numpy()\n",
        "        self.classes_ = sorted(list(set(y.reshape(-1))))\n",
        "        self.sorter = np.argsort(self.classes_)\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)))\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row,:i[0]+1] = 1\n",
        "        return ordinal_labels\n",
        "\n",
        "    def transform(self, y):\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)), dtype=\"int32\")\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row][:i[0]+1] = 1\n",
        "        ordinal_labels = ordinal_labels.astype(\"int32\")\n",
        "        return ordinal_labels\n",
        "\n",
        "    def inverse_transform(self, y_ordinal):\n",
        "        idx = y_ordinal.sum(axis=1, keepdims=True)\n",
        "        idx = idx.numpy()\n",
        "        idx = idx.astype(\"int32\")\n",
        "        classes = np.array(self.classes_)\n",
        "        return classes[idx - 1]\n",
        "\n",
        "\n",
        "class Encoder:\n",
        "  def __init__(self, enc_type=None):\n",
        "    self.enc_type = enc_type\n",
        "    if enc_type is None:\n",
        "      self.enc = None\n",
        "    elif enc_type == \"ohe\":\n",
        "      self.enc = OneHotEncoder()\n",
        "    elif enc_type == \"label\":\n",
        "      self.enc = LabelEncoder()\n",
        "    elif enc_type == \"ordinal\":\n",
        "      self.enc = OrdinalLabelEncoder()\n",
        "\n",
        "  def fit(self, df, target_column):\n",
        "    if self.enc_type is None:\n",
        "      self.target_column = target_column\n",
        "      self.classes = []\n",
        "      self.num_classes = 0 \n",
        "    elif self.enc_type == \"ohe\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[[target_column]])\n",
        "      self.classes = self.enc.categories_[0]\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"label\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def transform(self, df):\n",
        "    if self.enc_type is None:\n",
        "      return df[self.target_column]\n",
        "    elif self.enc_type == \"ohe\":\n",
        "      return self.enc.transform(df[[self.target_column]]).toarray() \n",
        "    elif self.enc_type == \"label\":\n",
        "      return self.enc.transform(df[self.target_column])\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      return self.enc.transform(df[[self.target_column]])\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def inverse_transform(self, val):\n",
        "    if self.enc_type in [\"ohe\", \"label\", \"ordinal\"]:\n",
        "      return self.enc.inverse_transform(val) \n",
        "    else:\n",
        "      raise Exception(f\"Cannot inverse transform with encoding type {self.enc_type}\")\n",
        "\n",
        "  def get_column_names(self):\n",
        "    if self.enc_type == \"ohe\":\n",
        "      return self.enc.get_feature_names_out()\n",
        "    if self.enc_type == \"ordinal\":\n",
        "      return self.enc.get_feature_names_out(prefix=self.target_column)\n",
        "    else:\n",
        "      return np.array([self.target_column])\n",
        "    \n",
        "class Preprocessor:\n",
        "  def __init__(self, path, tokenizer, text_col, encoder, max_token_len=512):\n",
        "    self.path = path\n",
        "    self.tokenizer = tokenizer\n",
        "    self.text_col = text_col\n",
        "    self.max_token_len = max_token_len \n",
        "    self.encoder = encoder\n",
        "    self._prepare_data()\n",
        "\n",
        "\n",
        "  def _prepare_data(self):\n",
        "    self.df = pd.read_table(self.path[\"convs\"])\n",
        "    if \"conv_labels\" in self.path:\n",
        "      conv_labels_df = pd.read_table(self.path[\"conv_labels\"], names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "      self.df = pd.concat([self.df, conv_labels_df], axis=1)\n",
        "    \n",
        "    def tokenize(target_col):\n",
        "      def _tokenize(row):\n",
        "        text = row[target_col]\n",
        "        tokens = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=\"pt\",\n",
        "            truncation=True,\n",
        "            max_length=self.max_token_len,\n",
        "            padding=\"max_length\",\n",
        "            return_attention_mask=True\n",
        "        )\n",
        "        return tokens[\"input_ids\"].numpy(), tokens[\"attention_mask\"].numpy()\n",
        "      return _tokenize\n",
        "\n",
        "    self.tokenized_input = self.df.apply(tokenize(self.text_col), axis=1)\n",
        "    self.labels = self.encoder.transform(self.df)\n",
        "\n",
        "  def get_tokenized_data(self):\n",
        "    tokenized_series = self.tokenized_input.apply(pd.Series)\n",
        "    input_ids = tokenized_series[0].to_numpy().reshape(-1, 1)\n",
        "    attention_mask = tokenized_series[1].to_numpy().reshape(-1, 1)\n",
        "    labels = self.labels\n",
        "    return input_ids, attention_mask, labels"
      ],
      "metadata": {
        "id": "dkAnaZZ5_w-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = \"cardiffnlp/twitter-roberta-base-sentiment\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "train_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_with_labels_train.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_with_labels_train.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "val_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_dev.tsv\",\n",
        "    \"conv_labels\": \"/content/goldstandard_CONV_dev.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_dev.tsv\",\n",
        "    \"essay_labels\": \"/content/goldstandard_dev.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "\n",
        "max_token_len = 96\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "label_col = \"EmotionalPolarity\"\n",
        "encoder = Encoder(\"label\")\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "encoder.fit(_df, label_col)\n",
        "\n",
        "preprocessor_train = Preprocessor(train_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)\n",
        "preprocessor_val = Preprocessor(val_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)"
      ],
      "metadata": {
        "id": "nO4o5NrtGu_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "\n",
        "class ConversationDataset(Dataset):\n",
        "  def __init__(self, input_ids, attention_mask, labels):\n",
        "    self.input_ids = input_ids\n",
        "    self.attention_mask = attention_mask \n",
        "    if len(labels.shape) > 1:\n",
        "      self.labels = torch.FloatTensor(labels)\n",
        "    else:\n",
        "      self.labels = labels if isinstance(labels, np.ndarray) else torch.tensor(labels.to_numpy()).float() \n",
        "  def __len__(self):\n",
        "    return len(self.input_ids)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    input_ids = torch.tensor(self.input_ids[idx][0][0])\n",
        "    attention_mask = torch.tensor(self.attention_mask[idx][0][0])\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"attention_mask\": attention_mask,\n",
        "        \"labels\": self.labels[idx]\n",
        "    }"
      ],
      "metadata": {
        "id": "FyQmvx427rWt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "train_input, train_attn_mask, train_labels = preprocessor_train.get_tokenized_data()\n",
        "val_input, val_attn_mask, val_labels = preprocessor_val.get_tokenized_data()\n",
        "\n",
        "train_ds = ConversationDataset(train_input, train_attn_mask, train_labels)\n",
        "val_ds = ConversationDataset(val_input, val_attn_mask, val_labels)\n",
        "\n",
        "\n",
        "val_dl = DataLoader(\n",
        "        val_ds,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=False\n",
        ")"
      ],
      "metadata": {
        "id": "efgU6YPHWYP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=encoder.num_classes, ignore_mismatched_sizes=True).to(device)\n",
        "# for param in model.roberta.parameters():\n",
        "#   param.requires_grad = False"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sM7hedHOIYb",
        "outputId": "0154c1ea-427f-459e-be09-b4802bc24a08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at cardiffnlp/twitter-roberta-base-sentiment and are newly initialized because the shapes did not match:\n",
            "- classifier.out_proj.weight: found shape torch.Size([3, 768]) in the checkpoint and torch.Size([9, 768]) in the model instantiated\n",
            "- classifier.out_proj.bias: found shape torch.Size([3]) in the checkpoint and torch.Size([9]) in the model instantiated\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(train_ds)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4W1d1gfIeEbx",
        "outputId": "1f5c48b6-6a76-427a-b356-09eedbf0b700"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8776"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments, set_seed\n",
        "import numpy as np\n",
        "\n",
        "set_seed(42)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/MSML-641-empathy/models/roberta-sentiment-pretrained-emotional-polarity/',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=64,\n",
        "    warmup_steps=500,\n",
        "    learning_rate=1e-5,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=300,\n",
        "    logging_steps=300,\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=1\n",
        ")\n"
      ],
      "metadata": {
        "id": "fdPvTy6zQYdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=val_ds\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "id": "39YLZieuQkvn",
        "outputId": "2863c61b-f9d2-4c6e-dc82-10706f08fe0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3291' max='3291' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3291/3291 11:20, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>1.887200</td>\n",
              "      <td>1.479300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.449800</td>\n",
              "      <td>1.418317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.389500</td>\n",
              "      <td>1.389866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.368400</td>\n",
              "      <td>1.396476</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.312900</td>\n",
              "      <td>1.393517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.248200</td>\n",
              "      <td>1.393392</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.234400</td>\n",
              "      <td>1.409759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.157800</td>\n",
              "      <td>1.446530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>1.108900</td>\n",
              "      <td>1.450925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.148500</td>\n",
              "      <td>1.452644</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3291, training_loss=1.3150620114220126, metrics={'train_runtime': 680.9191, 'train_samples_per_second': 38.665, 'train_steps_per_second': 4.833, 'total_flos': 1298929357592064.0, 'train_loss': 1.3150620114220126, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "\n",
        "predictions = np.array([])\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    val_loop = tqdm(val_dl)\n",
        "    for idx, val_batch in enumerate(val_loop):\n",
        "      input_ids = val_batch[\"input_ids\"].to(device)\n",
        "      attention_mask = val_batch[\"attention_mask\"].to(device)\n",
        "      labels = val_batch[\"labels\"].to(device)\n",
        "      output = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "      pr = torch.sigmoid(output.logits)\n",
        "      pr_idx = pr.argmax(axis=-1)\n",
        "      labels = encoder.classes[pr_idx.cpu()]\n",
        "      predictions = np.concatenate([predictions, labels], axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "cb6ee442be794999a7568c50fa48ec91",
            "ff26b1a0cf4e47e3a5c1c03732aaf752",
            "ddc87fb0e9744e5597a94e2b7feec8b2",
            "80d808b01973405a9698398348bfb2c3",
            "7605814653a045f7a087d7f9c85a5e6b",
            "c9e4ec26806b4ea59f452801c771076f",
            "ff8c42d926174296b575379db8260b56",
            "d4cfd7140606483ba4eb46d1899c57c4",
            "890158afd9304083b75d3aa889cfee80",
            "17ecb88d4f0d4baa965e2eaf2f7f0ad2",
            "93e5ea9dd56a4538930e2d1d4bd597a9"
          ]
        },
        "id": "OG5z4fCKBK8K",
        "outputId": "e5e3727c-61e3-472b-cdab-e7be1c3fea6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb6ee442be794999a7568c50fa48ec91"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe = OneHotEncoder()\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "ohe.fit(_df[[label_col]])\n",
        "num_classes = len(ohe.categories_[0])\n",
        "\n",
        "predictions_tr = pd.DataFrame(predictions, columns=[label_col])\n",
        "\n",
        "_transformed = ohe.transform(predictions_tr[[label_col]])\n",
        "_ohe_df = pd.DataFrame(_transformed.toarray(), columns=ohe.get_feature_names_out())\n",
        "predictions_tr = pd.concat([predictions_tr, _ohe_df], axis=1).drop([label_col], axis=1)\n",
        "final_pr = predictions_tr.to_numpy()\n",
        "\n",
        "val_data = pd.read_table('/content/goldstandard_CONV_dev.tsv', names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "transformed = ohe.transform(val_data[[label_col]]).toarray()\n",
        "ohe_df = pd.DataFrame(transformed, columns=ohe.get_feature_names_out())\n",
        "val_data = pd.concat([val_data, ohe_df], axis=1).drop([\"Empathy\", \"EmotionalPolarity\", \"Emotion\"], axis=1)\n",
        "labels = val_data.to_numpy()\n",
        "\n",
        "final_pr.shape == labels.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6J68SROgQrCb",
        "outputId": "7a239809-a60a-485a-e54e-95dee508d159"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "for i, label in enumerate(ohe.get_feature_names_out()):\n",
        "  fpr, tpr, _ = metrics.roc_curve(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  auc = metrics.roc_auc_score(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  plt.plot(fpr, tpr, label=\"%s %g\" %(label, auc))\n",
        "  plt.xlabel(\"False positive rate\")\n",
        "  plt.ylabel(\"True positive rate\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"Fine-tuned cardiffnlp/twitter-roberta-base-sentiment model for Emotional Polarity\")"
      ],
      "metadata": {
        "id": "pSf26dMCF-v0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "18488c1c-5fc2-477b-96ca-ca2795be5c5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_2aun4L26qQG"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}