RoBERTa_Last_4_Layers_EmotionalPolarity.ipynb

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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S0IYl0s_yfux",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e91eb7c1-2634-4941-9216-e4abeb6703db"
      },
      "outputs": [
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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: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\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: 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: 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: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\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: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\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: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\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: 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: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\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 06:13:49--  https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|: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 06:13:49 (51.4 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": {
        "id": "KqeNheNENmsa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f4c626b-46b3-4c02-d83c-70d7957b2ff5"
      },
      "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 = \"roberta-base\"\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": [
        "from tensorflow.python.ops.numpy_ops import ndarray\n",
        "from tensorflow.python.ops.gen_logging_ops import Print\n",
        "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": [
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "from transformers import AutoModel\n",
        "from transformers.modeling_outputs import SequenceClassifierOutput\n",
        "\n",
        "\n",
        "class CustomClassificationHead(nn.Module):\n",
        "    def __init__(self, hidden_size, hidden_layers_to_use, num_labels, classifier_dropout):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size \n",
        "        self.num_labels = num_labels\n",
        "        self.classifier_dropout = classifier_dropout\n",
        "        self.hidden_layers_to_use = hidden_layers_to_use\n",
        "\n",
        "        self.dense = nn.Linear(hidden_size * hidden_layers_to_use, hidden_size)\n",
        "        self.dropout = nn.Dropout(classifier_dropout)\n",
        "        self.out_proj = nn.Linear(hidden_size, num_labels)\n",
        "\n",
        "    def forward(self, hidden_states, **kwargs):\n",
        "        hidden_state_cls_vectors = []\n",
        "        for i in range(1, self.hidden_layers_to_use + 1):\n",
        "          hidden_state_cls_vectors.append(hidden_states[-1 * i][:, 0, :])\n",
        "        x = torch.cat(hidden_state_cls_vectors, dim=1)\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "class CustomClassifier(nn.Module):\n",
        "    def __init__(self, model_name, num_labels, hidden_layers_to_use=4):\n",
        "        super().__init__()\n",
        "        self.model_name = model_name \n",
        "        self.num_labels = num_labels\n",
        "        self.hidden_layers_to_use = hidden_layers_to_use\n",
        "        self.pretrained = AutoModel.from_pretrained(model_name, output_hidden_states=True)\n",
        "        classifier_dropout = self.pretrained.config.classifier_dropout if self.pretrained.config.classifier_dropout is not None else self.pretrained.config.hidden_dropout_prob\n",
        "        self.classifier = CustomClassificationHead(self.pretrained.config.hidden_size, hidden_layers_to_use, num_labels, classifier_dropout)\n",
        "\n",
        "        # for param in self.parameters():\n",
        "        #   param.requires_grad = False\n",
        "\n",
        "        # # Pretrained last n layers; ON\n",
        "        # for i in range(1, hidden_layers_to_use + 1):\n",
        "        #   for param in self.pretrained.encoder.layer[-i].parameters():\n",
        "        #       param.requires_grad = True\n",
        "\n",
        "        # # Linear; ON\n",
        "        # for param in self.classifier.parameters():\n",
        "        #     param.requires_grad = True\n",
        "        for param in self.pretrained.parameters():\n",
        "            param.requires_grad = False\n",
        "\n",
        "    def forward(self,input_ids=None, attention_mask=None,labels=None):\n",
        "        outputs = self.pretrained(input_ids, attention_mask=attention_mask)\n",
        "        sequence_output = outputs.hidden_states\n",
        "        logits = self.classifier(sequence_output)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            # move labels to correct device to enable model parallelism\n",
        "            labels = labels.to(logits.device)\n",
        "            loss_fct = CrossEntropyLoss()\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # if self.config.problem_type is None:\n",
        "            #     if self.num_labels == 1:\n",
        "            #         self.config.problem_type = \"regression\"\n",
        "            #     elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "            #         self.config.problem_type = \"single_label_classification\"\n",
        "            #     else:\n",
        "            #         self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            # if self.config.problem_type == \"regression\":\n",
        "            #     loss_fct = MSELoss()\n",
        "            #     if self.num_labels == 1:\n",
        "            #         loss = loss_fct(logits.squeeze(), labels.squeeze())\n",
        "            #     else:\n",
        "            #         loss = loss_fct(logits, labels)\n",
        "            # elif self.config.problem_type == \"single_label_classification\":\n",
        "            #     loss_fct = CrossEntropyLoss()\n",
        "            #     loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # elif self.config.problem_type == \"multi_label_classification\":\n",
        "            #     loss_fct = BCEWithLogitsLoss()\n",
        "            #     loss = loss_fct(logits, labels)\n",
        "\n",
        "        return SequenceClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n",
        "\n"
      ],
      "metadata": {
        "id": "_Wg8QSKnbszU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomClassifier(model_name, num_labels=encoder.num_classes, hidden_layers_to_use=12).to(device)"
      ],
      "metadata": {
        "id": "0sM7hedHOIYb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa2c97a9-476d-420c-d5e6-e79d43dd1235"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "import numpy as np\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/MSML-641-empathy/models/roberta-last-4-layers-emotional-polarity/',\n",
        "    num_train_epochs=20,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=64,\n",
        "    lr_scheduler_type=\"cosine_with_restarts\",\n",
        "    warmup_ratio=0.1,\n",
        "    learning_rate=5e-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": {
        "id": "39YLZieuQkvn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9da39adf-47eb-4da5-dedd-a81f3acfb9df"
      },
      "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='21940' max='21940' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [21940/21940 31:13, Epoch 20/20]\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>1.399100</td>\n",
              "      <td>1.475644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>1.213300</td>\n",
              "      <td>1.488381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>1.176000</td>\n",
              "      <td>1.511763</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>1.239800</td>\n",
              "      <td>1.509344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>1.236200</td>\n",
              "      <td>1.524880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>1.249000</td>\n",
              "      <td>1.481702</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>1.343700</td>\n",
              "      <td>1.514179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>1.325700</td>\n",
              "      <td>1.495172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>1.377400</td>\n",
              "      <td>1.577321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>1.436100</td>\n",
              "      <td>1.471331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3300</td>\n",
              "      <td>1.465000</td>\n",
              "      <td>1.621643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3600</td>\n",
              "      <td>1.511000</td>\n",
              "      <td>1.486896</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3900</td>\n",
              "      <td>1.501600</td>\n",
              "      <td>1.570182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4200</td>\n",
              "      <td>1.525500</td>\n",
              "      <td>1.497690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4500</td>\n",
              "      <td>1.530800</td>\n",
              "      <td>1.492187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4800</td>\n",
              "      <td>1.569600</td>\n",
              "      <td>1.571789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5100</td>\n",
              "      <td>1.518900</td>\n",
              "      <td>1.531871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5400</td>\n",
              "      <td>1.551200</td>\n",
              "      <td>1.514145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5700</td>\n",
              "      <td>1.574200</td>\n",
              "      <td>1.485967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6000</td>\n",
              "      <td>1.571400</td>\n",
              "      <td>1.545223</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6300</td>\n",
              "      <td>1.522700</td>\n",
              "      <td>1.546218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6600</td>\n",
              "      <td>1.531500</td>\n",
              "      <td>1.508041</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6900</td>\n",
              "      <td>1.526400</td>\n",
              "      <td>1.581890</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7200</td>\n",
              "      <td>1.532000</td>\n",
              "      <td>1.485463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7500</td>\n",
              "      <td>1.534000</td>\n",
              "      <td>1.499336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7800</td>\n",
              "      <td>1.554000</td>\n",
              "      <td>1.492938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8100</td>\n",
              "      <td>1.550600</td>\n",
              "      <td>1.536073</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8400</td>\n",
              "      <td>1.511900</td>\n",
              "      <td>1.507194</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8700</td>\n",
              "      <td>1.529100</td>\n",
              "      <td>1.519480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9000</td>\n",
              "      <td>1.511200</td>\n",
              "      <td>1.480200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9300</td>\n",
              "      <td>1.522200</td>\n",
              "      <td>1.492405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9600</td>\n",
              "      <td>1.519800</td>\n",
              "      <td>1.490869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9900</td>\n",
              "      <td>1.501700</td>\n",
              "      <td>1.462305</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10200</td>\n",
              "      <td>1.552000</td>\n",
              "      <td>1.478040</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10500</td>\n",
              "      <td>1.494000</td>\n",
              "      <td>1.471550</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10800</td>\n",
              "      <td>1.502400</td>\n",
              "      <td>1.480690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11100</td>\n",
              "      <td>1.508900</td>\n",
              "      <td>1.478285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11400</td>\n",
              "      <td>1.509000</td>\n",
              "      <td>1.463824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11700</td>\n",
              "      <td>1.503700</td>\n",
              "      <td>1.490347</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12000</td>\n",
              "      <td>1.484000</td>\n",
              "      <td>1.484960</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12300</td>\n",
              "      <td>1.468400</td>\n",
              "      <td>1.599813</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12600</td>\n",
              "      <td>1.490000</td>\n",
              "      <td>1.462705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12900</td>\n",
              "      <td>1.531000</td>\n",
              "      <td>1.507841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13200</td>\n",
              "      <td>1.475000</td>\n",
              "      <td>1.488307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13500</td>\n",
              "      <td>1.533900</td>\n",
              "      <td>1.462773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13800</td>\n",
              "      <td>1.493900</td>\n",
              "      <td>1.476532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14100</td>\n",
              "      <td>1.483400</td>\n",
              "      <td>1.461900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14400</td>\n",
              "      <td>1.474300</td>\n",
              "      <td>1.457453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14700</td>\n",
              "      <td>1.484500</td>\n",
              "      <td>1.461610</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15000</td>\n",
              "      <td>1.485500</td>\n",
              "      <td>1.462494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15300</td>\n",
              "      <td>1.473500</td>\n",
              "      <td>1.455278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15600</td>\n",
              "      <td>1.465200</td>\n",
              "      <td>1.464568</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15900</td>\n",
              "      <td>1.473800</td>\n",
              "      <td>1.460936</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16200</td>\n",
              "      <td>1.483400</td>\n",
              "      <td>1.460994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16500</td>\n",
              "      <td>1.476300</td>\n",
              "      <td>1.457120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16800</td>\n",
              "      <td>1.497500</td>\n",
              "      <td>1.451110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17100</td>\n",
              "      <td>1.480400</td>\n",
              "      <td>1.450623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17400</td>\n",
              "      <td>1.467600</td>\n",
              "      <td>1.449290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17700</td>\n",
              "      <td>1.442300</td>\n",
              "      <td>1.456393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18000</td>\n",
              "      <td>1.466200</td>\n",
              "      <td>1.461075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18300</td>\n",
              "      <td>1.461400</td>\n",
              "      <td>1.451658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18600</td>\n",
              "      <td>1.465100</td>\n",
              "      <td>1.460372</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18900</td>\n",
              "      <td>1.473700</td>\n",
              "      <td>1.451018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19200</td>\n",
              "      <td>1.440600</td>\n",
              "      <td>1.456651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19500</td>\n",
              "      <td>1.473400</td>\n",
              "      <td>1.451386</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19800</td>\n",
              "      <td>1.459500</td>\n",
              "      <td>1.455288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20100</td>\n",
              "      <td>1.489900</td>\n",
              "      <td>1.448675</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20400</td>\n",
              "      <td>1.456200</td>\n",
              "      <td>1.450120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20700</td>\n",
              "      <td>1.456200</td>\n",
              "      <td>1.448048</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21000</td>\n",
              "      <td>1.462700</td>\n",
              "      <td>1.448866</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21300</td>\n",
              "      <td>1.465500</td>\n",
              "      <td>1.448375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21600</td>\n",
              "      <td>1.472900</td>\n",
              "      <td>1.448688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21900</td>\n",
              "      <td>1.448800</td>\n",
              "      <td>1.448720</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=21940, training_loss=1.4710041652947203, metrics={'train_runtime': 1873.3217, 'train_samples_per_second': 93.695, 'train_steps_per_second': 11.712, 'total_flos': 0.0, 'train_loss': 1.4710041652947203, 'epoch': 20.0})"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "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": {
        "id": "OG5z4fCKBK8K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b9a19656d4344c3a9b87e0e2693f7bc6",
            "44ab5fc53bf245538c56079bd1eeb71b",
            "92362e3b713f4cbca52fba7dd2d563df",
            "55ede1ae3c9f4ff9a697393b39a444b3",
            "f49d7bf2345b43a8875f90367e61651d",
            "0d627885496949f6b01b0888a849511d",
            "e43745d8f14c44d5995400281f67b643",
            "aef1db0dea5d41e59cad8203a99f4f4e",
            "61e8826e0ec24cf28d4330b6bb039700",
            "7f46a9ce6b0f4de195aabdfc464b6c62",
            "30f3e96922bd4bbabc08bdfdc55004d5"
          ]
        },
        "outputId": "64e1cdbb-9d51-47f7-bcac-3de0c5d4761a"
      },
      "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": "b9a19656d4344c3a9b87e0e2693f7bc6"
            }
          },
          "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": {
        "id": "6J68SROgQrCb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd4440b9-459f-49ec-9b32-4fed90931c4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "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(\"RoBERTa (Using last 4 hidden layers) for Emotional Polarity\")"
      ],
      "metadata": {
        "id": "4tX5Jgg1SIiF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "outputId": "a03f2365-d58f-4c79-a4cb-756b0acec563"
      },
      "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": "WevJInw4Fwuf"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}