Subtask_B.ipynb

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            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d8/b2/57495b5309f09fa501866e225c84532d1fd89536ea62406b2181933fb418/transformers-4.5.1-py3-none-any.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 7.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
            "\u001b[K     |████████████████████████████████| 901kB 50.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.10.1)\n",
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            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
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            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.45 tokenizers-0.10.2 transformers-4.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zP9332IA28x1"
      },
      "source": [
        "import random\n",
        "import torch\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from tqdm import tqdm\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from transformers import BertTokenizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 180,
          "referenced_widgets": [
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            "02cb82e6c0564bf08dc90361ba48d5a2",
            "e74d0fb21dfd4210903257bdb287913f",
            "dab8b4c5c2c74e27a3dfa8204e7d1a6f",
            "c3333f5de99d48a9b7ec56e0c39b206e",
            "cb536477eada4ce5a160009557c082a3",
            "0524af68376a495d865d8f3f0d10e6fb"
          ]
        },
        "id": "h4ozqu3A3A-v",
        "outputId": "e4aaf224-7541-42fb-a097-b7108831ac82"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Load the BERT tokenizer.\n",
        "print('Loading BERT tokenizer...')\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading BERT tokenizer...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PEvAj3J3FbL"
      },
      "source": [
        "train_data_df = pd.read_csv(\"data/twitter-2016train-BD.txt\", delimiter='\\t', header=None, names=['id','topic','label', 'tweet'])\n",
        "val_data_df = pd.read_csv(\"data/twitter-2016devtest-BD.txt\", delimiter='\\t', header=None, names=['id','topic','label', 'tweet'])\n",
        "\n",
        "train_tweet = train_data_df.tweet.values\n",
        "train_topic = train_data_df.topic.values\n",
        "y_train = train_data_df.label.values\n",
        "\n",
        "val_tweet = val_data_df.tweet.values\n",
        "val_topic = val_data_df.topic.values\n",
        "y_val = val_data_df.label.values"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5F_a4uWJAxjN",
        "outputId": "93440c85-8e41-4399-f09d-5907579a97b5"
      },
      "source": [
        "print(train_data_df.sample(5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "                      id  ...                                              tweet\n",
            "464   641575778304557057  ...  @ApplWatcher I only changed to Apple iPhone 6 ...\n",
            "784   641164550318989312  ...  @LLNTutor Like the BBC, when they've just ment...\n",
            "1946  636232024223784960  ...  Federer overtakes Lendl for second longest str...\n",
            "1451  637282857002659841  ...  One of the Worlds biggest icons David Beckham ...\n",
            "577   637538852996050944  ...  I wanna get an Apple Watch for my 18th, and I ...\n",
            "\n",
            "[5 rows x 4 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FNLQlJe-36W1"
      },
      "source": [
        "train_labels=[]\n",
        "val_labels=[]\n",
        "label_dict = {'negative':0,'positive':1}\n",
        "\n",
        "for label in y_train:\n",
        "  train_labels.append(label_dict[label])\n",
        "\n",
        "for label in y_val:\n",
        "  val_labels.append(label_dict[label])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m1yRSs2J4ImK",
        "outputId": "ec50bf18-84c5-42ac-c034-b15e378a35e9"
      },
      "source": [
        "print(len(train_labels))\n",
        "print(len(val_labels))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4309\n",
            "1417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hbv5Om1l4ii9"
      },
      "source": [
        "def processdata(topics,tweets,labels):\n",
        "  input_ids = []\n",
        "  attention_masks = []\n",
        "  token_type_ids = []\n",
        "  for topic, tweet in zip(topics,tweets):\n",
        "    encoded_dict = tokenizer.encode_plus(topic,\n",
        "                        tweet,                      # Sentence to encode.\n",
        "                        add_special_tokens = True, # Add '[CLS]' and '[SEP]'\n",
        "                        max_length = 64,           # Pad & truncate all sentences.\n",
        "                        pad_to_max_length = True,\n",
        "                        return_attention_mask = True,   # Construct attn. masks.\n",
        "                        return_tensors = 'pt',     # Return pytorch tensors.\n",
        "                        return_token_type_ids = True,\n",
        "                   )\n",
        "    input_ids.append(encoded_dict['input_ids'])\n",
        "    attention_masks.append(encoded_dict['attention_mask'])\n",
        "    token_type_ids.append(encoded_dict['token_type_ids'])\n",
        "\n",
        "  input_ids = torch.cat(input_ids, dim=0)\n",
        "  attention_masks = torch.cat(attention_masks, dim=0)\n",
        "  token_type_ids = torch.cat(token_type_ids,dim=0)\n",
        "  labels = torch.tensor(labels)\n",
        "  return input_ids,attention_masks,token_type_ids,labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0G-XQqGr4B_9",
        "outputId": "41a3a552-6244-4e71-becd-c2fedcc68504"
      },
      "source": [
        "train_input_ids,train_attention_masks,train_token_type_ids,train_labels = processdata(train_topic,train_tweet,train_labels)\n",
        "val_input_ids,val_attention_masks,val_token_type_ids,val_labels = processdata(val_topic,val_tweet,val_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8D3ZocO5F18"
      },
      "source": [
        "train_dataset = TensorDataset(train_input_ids, train_attention_masks,train_token_type_ids,train_labels)\n",
        "val_dataset = TensorDataset(val_input_ids, val_attention_masks,val_token_type_ids,val_labels)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pCsxt5RD5zsc"
      },
      "source": [
        "from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n",
        "batch_size = 32\n",
        "\n",
        "# Create the DataLoaders for our training and validation sets.\n",
        "train_dataloader = DataLoader(\n",
        "            train_dataset,  # The training samples.\n",
        "            sampler = RandomSampler(train_dataset), # Select batches randomly\n",
        "            batch_size = batch_size # Trains with this batch size.\n",
        "        )\n",
        "\n",
        "# For validation the order doesn't matter, so we'll just read them sequentially.\n",
        "validation_dataloader = DataLoader(\n",
        "            val_dataset, # The validation samples.\n",
        "            sampler = SequentialSampler(val_dataset), # Pull out batches sequentially.\n",
        "            batch_size = batch_size # Evaluate with this batch size.\n",
        "        )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "id": "SvquBIqT6Vo3",
        "outputId": "608e69ae-33eb-4a7b-d23d-17e6fbbcc57a"
      },
      "source": [
        "from transformers import BertForSequenceClassification, AdamW, BertConfig\n",
        "\n",
        "# Load BertForSequenceClassification, the pretrained BERT model with a single \n",
        "# linear classification layer on top. \n",
        "model = BertForSequenceClassification.from_pretrained(\n",
        "    \"bert-base-uncased\", \n",
        "    num_labels = 2, # The number of output labels = 2\n",
        "                    \n",
        "    output_attentions = False, # Whether the model returns attentions weights.\n",
        "    output_hidden_states = False, # Whether the model returns all hidden-states.\n",
        ")\n",
        "\n",
        "# Tell pytorch to run this model on the GPU.\n",
        "model.cuda()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2fd5d5353cbc4930b0fa6ae78a34429a",
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        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias']\n",
            "- This IS expected if you are initializing BertForSequenceClassification 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 BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.weight', 'classifier.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BertForSequenceClassification(\n",
              "  (bert): BertModel(\n",
              "    (embeddings): BertEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 768, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 768)\n",
              "      (token_type_embeddings): Embedding(2, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): BertEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): BertLayer(\n",
              "          (attention): BertAttention(\n",
              "            (self): BertSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): BertSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): BertIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "          )\n",
              "          (output): BertOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): BertPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\n",
              "    )\n",
              "  )\n",
              "  (dropout): Dropout(p=0.1, inplace=False)\n",
              "  (classifier): Linear(in_features=768, out_features=2, bias=True)\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y7HBNHDA6f0Q"
      },
      "source": [
        "# Note: AdamW is a class from the huggingface library\n",
        "optimizer = AdamW(model.parameters(),\n",
        "                  lr = 2e-5, # args.learning_rate - default is 5e-5, our notebook had 2e-5\n",
        "                  eps = 1e-8 # args.adam_epsilon  - default is 1e-8.\n",
        "                )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZn2ET0H6svi"
      },
      "source": [
        "from transformers import get_linear_schedule_with_warmup\n",
        "\n",
        "# Number of training epochs. The BERT authors recommend between 2 and 4. \n",
        "EPOCHS = 4\n",
        "\n",
        "# Total number of training steps is [number of batches] x [number of epochs]. \n",
        "# (Note that this is not the same as the number of training samples).\n",
        "total_steps = len(train_dataloader) * EPOCHS\n",
        "\n",
        "# Create the learning rate scheduler.\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, \n",
        "                                            num_warmup_steps = 0, # Default value in run_glue.py\n",
        "                                            num_training_steps = total_steps)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0gGFc-pd-76N"
      },
      "source": [
        "def accuracy(y_pred, y_test):\n",
        "  acc = (torch.log_softmax(y_pred, dim=1).argmax(dim=1) == y_test).sum().float() / float(y_test.size(0))\n",
        "  return acc"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DUiTu2ST_RGK"
      },
      "source": [
        "import time\n",
        "import datetime\n",
        "\n",
        "def format_time(elapsed):\n",
        "    '''\n",
        "    Takes a time in seconds and returns a string hh:mm:ss\n",
        "    '''\n",
        "    # Round to the nearest second.\n",
        "    elapsed_rounded = int(round((elapsed)))\n",
        "    \n",
        "    # Format as hh:mm:ss\n",
        "    return str(datetime.timedelta(seconds=elapsed_rounded))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hdBFHqPeCrCz"
      },
      "source": [
        "training_stats=[]\n",
        "def train(model, train_loader, val_loader, optimizer,scheduler):  \n",
        "  total_step = len(train_loader)\n",
        "\n",
        "  for epoch in range(EPOCHS):\n",
        "    # Measure how long the training epoch takes.\n",
        "    train_start = time.time()\n",
        "    model.train()\n",
        "\n",
        "    # Reset the total loss and accuracy for this epoch.\n",
        "    total_train_loss = 0\n",
        "    total_train_acc  = 0\n",
        "    for batch_idx, (pair_token_ids, mask_ids,seg_ids, y) in enumerate(train_loader):\n",
        "\n",
        "      # Unpack this training batch from our dataloader. \n",
        "      pair_token_ids = pair_token_ids.to(device)\n",
        "      mask_ids = mask_ids.to(device)\n",
        "      seg_ids = seg_ids.to(device)\n",
        "      labels = y.to(device)\n",
        "\n",
        "      #clear any previously calculated gradients before performing a backward pass\n",
        "      optimizer.zero_grad()\n",
        "\n",
        "      #Get the loss and prediction\n",
        "      loss, prediction = model(pair_token_ids, \n",
        "                             token_type_ids=seg_ids, \n",
        "                             attention_mask=mask_ids, \n",
        "                             labels=labels).values()\n",
        "\n",
        "      acc = accuracy(prediction, labels)\n",
        "      \n",
        "      # Accumulate the training loss and accuracy over all of the batches so that we can\n",
        "      # calculate the average loss at the end\n",
        "      total_train_loss += loss.item()\n",
        "      total_train_acc  += acc.item()\n",
        "\n",
        "      # Perform a backward pass to calculate the gradients.\n",
        "      loss.backward()\n",
        "\n",
        "      # Clip the norm of the gradients to 1.0.\n",
        "      # This is to help prevent the \"exploding gradients\" problem.\n",
        "      torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n",
        "\n",
        "      # Update parameters and take a step using the computed gradient.\n",
        "      optimizer.step()\n",
        "\n",
        "      # Update the learning rate.\n",
        "      scheduler.step()\n",
        "\n",
        "    # Calculate the average accuracy and loss over all of the batches.\n",
        "    train_acc  = total_train_acc/len(train_loader)\n",
        "    train_loss = total_train_loss/len(train_loader)\n",
        "    train_end = time.time()\n",
        "\n",
        "    # Put the model in evaluation mode\n",
        "    model.eval()\n",
        "\n",
        "    total_val_acc  = 0\n",
        "    total_val_loss = 0\n",
        "    val_start = time.time()\n",
        "    with torch.no_grad():\n",
        "      for batch_idx, (pair_token_ids, mask_ids,seg_ids,y) in enumerate(val_loader):\n",
        "\n",
        "        #clear any previously calculated gradients before performing a backward pass\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        # Unpack this validation batch from our dataloader. \n",
        "        pair_token_ids = pair_token_ids.to(device)\n",
        "        mask_ids = mask_ids.to(device)\n",
        "        seg_ids = seg_ids.to(device)\n",
        "        labels = y.to(device)\n",
        "        \n",
        "        #Get the loss and prediction\n",
        "        loss, prediction = model(pair_token_ids, \n",
        "                             token_type_ids=seg_ids, \n",
        "                             attention_mask=mask_ids, \n",
        "                             labels=labels).values()\n",
        "\n",
        "        # Calculate the accuracy for this batch\n",
        "        acc = accuracy(prediction, labels)\n",
        "\n",
        "        # Accumulate the validation loss and Accuracy\n",
        "        total_val_loss += loss.item()\n",
        "        total_val_acc  += acc.item()\n",
        "\n",
        "    # Calculate the average accuracy and loss over all of the batches.\n",
        "    val_acc  = total_val_acc/len(val_loader)\n",
        "    val_loss = total_val_loss/len(val_loader)\n",
        "\n",
        "    #end = time.time()\n",
        "    val_end = time.time()\n",
        "    hours, rem = divmod(val_end-train_start, 3600)\n",
        "    minutes, seconds = divmod(rem, 60)\n",
        "\n",
        "    print(f'Epoch {epoch+1}: train_loss: {train_loss:.4f} train_acc: {train_acc:.4f} | val_loss: {val_loss:.4f} val_acc: {val_acc:.4f}')\n",
        "    print(\"{:0>2}:{:0>2}:{:05.2f}\".format(int(hours),int(minutes),seconds))\n",
        "\n",
        "    training_stats.append(\n",
        "        {\n",
        "            'epoch': epoch + 1,\n",
        "            'Training Loss': train_loss,\n",
        "            'Valid. Loss': val_loss,\n",
        "            'Valid. Accur.': val_acc,\n",
        "            'Training Time': train_end-train_start,\n",
        "            'Validation Time': val_end-val_start\n",
        "        }\n",
        "    )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPAckrjXRu2z",
        "outputId": "279f6927-d51a-48f7-8d44-18dcff33d4a0"
      },
      "source": [
        "train(model, train_dataloader, validation_dataloader, optimizer,scheduler)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: train_loss: 0.3182 train_acc: 0.8686 | val_loss: 0.3564 val_acc: 0.8478\n",
            "00:00:30.71\n",
            "Epoch 2: train_loss: 0.1651 train_acc: 0.9399 | val_loss: 0.3288 val_acc: 0.8738\n",
            "00:00:30.68\n",
            "Epoch 3: train_loss: 0.0913 train_acc: 0.9734 | val_loss: 0.3967 val_acc: 0.8773\n",
            "00:00:30.53\n",
            "Epoch 4: train_loss: 0.0575 train_acc: 0.9852 | val_loss: 0.4631 val_acc: 0.8728\n",
            "00:00:30.47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "VYamelExRy2b",
        "outputId": "d8e44983-aafd-4c85-dce1-bdfaf52109fd"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Display floats with two decimal places.\n",
        "pd.set_option('precision', 2)\n",
        "\n",
        "# Create a DataFrame from our training statistics.\n",
        "df_stats = pd.DataFrame(data=training_stats)\n",
        "\n",
        "# Use the 'epoch' as the row index.\n",
        "df_stats = df_stats.set_index('epoch')\n",
        "\n",
        "# A hack to force the column headers to wrap.\n",
        "#df = df.style.set_table_styles([dict(selector=\"th\",props=[('max-width', '70px')])])\n",
        "\n",
        "# Display the table.\n",
        "df_stats"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Valid. Loss</th>\n",
              "      <th>Valid. Accur.</th>\n",
              "      <th>Training Time</th>\n",
              "      <th>Validation Time</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>epoch</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.32</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.85</td>\n",
              "      <td>27.92</td>\n",
              "      <td>2.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.17</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.87</td>\n",
              "      <td>27.89</td>\n",
              "      <td>2.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.09</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.88</td>\n",
              "      <td>27.74</td>\n",
              "      <td>2.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.06</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.87</td>\n",
              "      <td>27.68</td>\n",
              "      <td>2.79</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Training Loss  Valid. Loss  ...  Training Time  Validation Time\n",
              "epoch                              ...                                \n",
              "1               0.32         0.36  ...          27.92             2.79\n",
              "2               0.17         0.33  ...          27.89             2.80\n",
              "3               0.09         0.40  ...          27.74             2.79\n",
              "4               0.06         0.46  ...          27.68             2.79\n",
              "\n",
              "[4 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "id": "kMer1BqoTpOM",
        "outputId": "f33c4d86-0699-405c-db5c-29fabf7c8257"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "% matplotlib inline\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "# Use plot styling from seaborn.\n",
        "sns.set(style='darkgrid')\n",
        "\n",
        "# Increase the plot size and font size.\n",
        "sns.set(font_scale=1.5)\n",
        "plt.rcParams[\"figure.figsize\"] = (12,6)\n",
        "\n",
        "# Plot the learning curve.\n",
        "plt.plot(df_stats['Training Loss'], 'b-o', label=\"Training\")\n",
        "plt.plot(df_stats['Valid. Loss'], 'g-o', label=\"Validation\")\n",
        "\n",
        "# Label the plot.\n",
        "plt.title(\"Training & Validation Loss\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.xticks([1, 2, 3, 4])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaMTDsHPTsfK",
        "outputId": "ef719fb2-596e-4886-beaf-a01fa449161d"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Load the dataset into a pandas dataframe.\n",
        "df = pd.read_csv(\"data/twitter-2016test-BD.txt\", delimiter='\\t', header=None, names=['topic','label','tweet','id'])\n",
        "\n",
        "# Report the number of sentences.\n",
        "print('Number of test sentences: {:,}\\n'.format(df.shape[0]))\n",
        "\n",
        "# Create sentence and label lists\n",
        "\n",
        "test_tweet = df.tweet.values\n",
        "test_topic = df.topic.values\n",
        "y_test = df.label.values\n",
        "\n",
        "test_labels=[]\n",
        "label_dict = {'negative':0,'positive':1}\n",
        "\n",
        "for label in y_test:\n",
        "  test_labels.append(label_dict[label])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of test sentences: 10,551\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ytSNL_rJU_wR",
        "outputId": "d93b8e2b-5939-4ed2-bac4-0ffee0aabfdd"
      },
      "source": [
        "input_ids,attention_masks,token_type_ids,labels = processdata(test_topic,test_tweet,test_labels)\n",
        "\n",
        "# Set the batch size.  \n",
        "batch_size = 32  \n",
        "\n",
        "# Create the DataLoader.\n",
        "prediction_data = TensorDataset(input_ids, attention_masks,token_type_ids,labels)\n",
        "prediction_sampler = SequentialSampler(prediction_data)\n",
        "prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2079: FutureWarning: The `pad_to_max_length` argument is deprecated and will be removed in a future version, use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or use `padding='max_length'` to pad to a max length. In this case, you can give a specific length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the maximal input size of the model (e.g. 512 for Bert).\n",
            "  FutureWarning,\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBijHl2fWnSS"
      },
      "source": [
        "from sklearn import metrics\n",
        "from sklearn.metrics import precision_recall_fscore_support\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import recall_score\n",
        "\n",
        "def test(model,prediction_dataloader): \n",
        "\n",
        "  total_test_acc  = 0\n",
        "  total_F1_Score = 0\n",
        "  total_precision = 0\n",
        "  total_recall = 0\n",
        "\n",
        "  for batch_idx, (pair_token_ids, mask_ids,token_type_ids,y) in enumerate(prediction_dataloader):\n",
        "    pair_token_ids = pair_token_ids.to(device)\n",
        "    mask_ids = mask_ids.to(device)\n",
        "    labels = y.to(device)\n",
        "    token_type_ids=token_type_ids.to(device)\n",
        "\n",
        "    loss, prediction = model(pair_token_ids, \n",
        "                             token_type_ids=token_type_ids, \n",
        "                             attention_mask=mask_ids, \n",
        "                             labels=labels).values()\n",
        "\n",
        "\n",
        "    acc = accuracy(prediction, labels)\n",
        "\n",
        "    f1 = metrics.f1_score(labels.cpu(), torch.argmax(prediction, -1).cpu(), average='binary')\n",
        "    precision = precision_score(labels.cpu(), torch.argmax(prediction, -1).cpu(), average=\"binary\")\n",
        "    recall = recall_score(labels.cpu(), torch.argmax(prediction, -1).cpu(), average=\"binary\")\n",
        "\n",
        "    total_test_acc  += acc.item()\n",
        "    total_F1_Score += f1\n",
        "    total_precision += precision\n",
        "    total_recall += recall\n",
        "  \n",
        "  test_acc  = total_test_acc/len(prediction_dataloader)\n",
        "  test_f1 = total_F1_Score/len(prediction_dataloader)\n",
        "  test_precision = total_precision/len(prediction_dataloader)\n",
        "  test_recall = total_recall/len(prediction_dataloader)\n",
        "\n",
        "\n",
        "  print(f'test_acc: {test_acc:.4f}')\n",
        "  print(f'f1 Score: {test_f1:.4f}')\n",
        "  print(f'precision: {test_precision:.4f}')\n",
        "  print(f'recall: {test_recall:.4f}')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BNibJE57Wxtq",
        "outputId": "d17210dc-4b4f-49f8-d57a-ffd543fdf7bc"
      },
      "source": [
        "test(model,prediction_dataloader)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Recall is ill-defined and being set to 0.0 due to no true samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1515: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 due to no true nor predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  average, \"true nor predicted\", 'F-score is', len(true_sum)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1272: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "test_acc: 0.8969\n",
            "f1 Score: 0.8484\n",
            "precision: 0.8381\n",
            "recall: 0.8882\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ysavTJIPDt65"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}