BERT_soundscape.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "Czq1ndThQ2_E"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
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        "outputId": "f0aa7312-dd62-4d6f-9758-4c99c35cc4c3"
      },
      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.30.2-py3-none-any.whl (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m83.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m29.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m114.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m77.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement 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,>=0.14.1->transformers) (2023.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\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: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\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",
            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.15.1 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.30.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "import torch.nn.functional as F\n",
        "from transformers import BertTokenizer, BertConfig,AdamW, BertForSequenceClassification,get_linear_schedule_with_warmup\n",
        "\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix,classification_report\n",
        "from sklearn.metrics import accuracy_score,matthews_corrcoef\n",
        "\n",
        "from tqdm import tqdm, trange,tnrange,tqdm_notebook\n",
        "import random\n",
        "import os\n",
        "import io"
      ],
      "metadata": {
        "id": "2U-YqiS_RHuo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "n_gpu = torch.cuda.device_count()\n",
        "torch.cuda.get_device_name(0)\n",
        "\n",
        "SEED = 19\n",
        "\n",
        "random.seed(SEED)\n",
        "np.random.seed(SEED)\n",
        "torch.manual_seed(SEED)\n",
        "if device == torch.device(\"cuda\"):\n",
        "    torch.cuda.manual_seed_all(SEED)"
      ],
      "metadata": {
        "id": "Hj-FVYSFRcog"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "device = torch.device(\"cuda\")"
      ],
      "metadata": {
        "id": "mSC0-is6SL3U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv(\"train.txt\", delimiter=';', header=None, names=['sentence','label'])\n",
        "df_test = pd.read_csv(\"test.txt\", delimiter=';', header=None, names=['sentence','label'])\n",
        "df_val = pd.read_csv(\"val.txt\", delimiter=';', header=None, names=['sentence','label'])"
      ],
      "metadata": {
        "id": "_gHCBDltSSBt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df_train,df_test,df_val])"
      ],
      "metadata": {
        "id": "RFOhcfUeS9TZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['label'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "feDbvzijS_t_",
        "outputId": "e266998f-9824-4ddb-fc58-143ee749e61d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['sadness', 'anger', 'love', 'surprise', 'fear', 'joy'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "labelencoder = LabelEncoder()\n",
        "df['label_enc'] = labelencoder.fit_transform(df['label'])"
      ],
      "metadata": {
        "id": "WovyLD7fTEsj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['label','label_enc']].drop_duplicates(keep='first')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "n2WMkYNaTHIn",
        "outputId": "f735fafd-d6b2-49fd-ea20-8591b98072c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      label  label_enc\n",
              "0   sadness          4\n",
              "2     anger          0\n",
              "3      love          3\n",
              "6  surprise          5\n",
              "7      fear          1\n",
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          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns={'label':'label_desc'},inplace=True)\n",
        "df.rename(columns={'label_enc':'label'},inplace=True)"
      ],
      "metadata": {
        "id": "4J4hPJTATKuV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sentences = df.sentence.values\n",
        "\n",
        "print(\"Distribution of data based on labels: \",df.label.value_counts())\n",
        "\n",
        "\n",
        "MAX_LEN = 256\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased',do_lower_case=True)\n",
        "input_ids = [tokenizer.encode(sent, add_special_tokens=True,max_length=MAX_LEN,pad_to_max_length=True) for sent in sentences]\n",
        "labels = df.label.values\n",
        "\n",
        "print(\"Actual sentence before tokenization: \",sentences[2])\n",
        "print(\"Encoded Input from dataset: \",input_ids[2])\n",
        "\n",
        "attention_masks = []\n",
        "attention_masks = [[float(i>0) for i in seq] for seq in input_ids]\n",
        "print(attention_masks[2])"
      ],
      "metadata": {
        "colab": {
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          "height": 359,
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribution of data based on labels:  2    6761\n",
            "4    5797\n",
            "0    2709\n",
            "1    2373\n",
            "3    1641\n",
            "5     719\n",
            "Name: label, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
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              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64f1ad46c37b400fb166211821c812ae"
            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "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/python3.10/dist-packages/transformers/tokenization_utils_base.py:2377: 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",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Actual sentence before tokenization:  im grabbing a minute to post i feel greedy wrong\n",
            "Encoded Input from dataset:  [101, 10047, 9775, 1037, 3371, 2000, 2695, 1045, 2514, 20505, 3308, 102, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_inputs,validation_inputs,train_labels,validation_labels = train_test_split(input_ids,labels,random_state=41,test_size=0.1)\n",
        "train_masks,validation_masks,_,_ = train_test_split(attention_masks,input_ids,random_state=41,test_size=0.1)"
      ],
      "metadata": {
        "id": "nisp6SM1TYIn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_inputs = torch.tensor(train_inputs)\n",
        "validation_inputs = torch.tensor(validation_inputs)\n",
        "train_labels = torch.tensor(train_labels)\n",
        "validation_labels = torch.tensor(validation_labels)\n",
        "train_masks = torch.tensor(train_masks)\n",
        "validation_masks = torch.tensor(validation_masks)\n",
        "\n",
        "batch_size = 32\n",
        "\n",
        "train_data = TensorDataset(train_inputs,train_masks,train_labels)\n",
        "train_sampler = RandomSampler(train_data)\n",
        "train_dataloader = DataLoader(train_data,sampler=train_sampler,batch_size=batch_size)\n",
        "\n",
        "validation_data = TensorDataset(validation_inputs,validation_masks,validation_labels)\n",
        "validation_sampler = RandomSampler(validation_data)\n",
        "validation_dataloader = DataLoader(validation_data,sampler=validation_sampler,batch_size=batch_size)"
      ],
      "metadata": {
        "id": "HuuXeuvDTZXG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = BertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=6).to(device)\n",
        "\n",
        "lr = 2e-5\n",
        "adam_epsilon = 1e-8\n",
        "\n",
        "epochs = 3\n",
        "\n",
        "num_warmup_steps = 0\n",
        "num_training_steps = len(train_dataloader)*epochs\n",
        "\n",
        "optimizer = AdamW(model.parameters(), lr=lr,eps=adam_epsilon,correct_bias=False)\n",
        "scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=num_warmup_steps, num_training_steps=num_training_steps)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 190,
          "referenced_widgets": [
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.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",
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:411: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_loss_set = []\n",
        "learning_rate = []\n",
        "\n",
        "# Gradients gets accumulated by default\n",
        "model.zero_grad()\n",
        "\n",
        "# tnrange is a tqdm wrapper around the normal python range\n",
        "for _ in tnrange(1,epochs+1,desc='Epoch'):\n",
        "  print(\"<\" + \"=\"*22 + F\" Epoch {_} \"+ \"=\"*22 + \">\")\n",
        "  # Calculate total loss for this epoch\n",
        "  batch_loss = 0\n",
        "\n",
        "  for step, batch in enumerate(train_dataloader):\n",
        "    # Set our model to training mode (as opposed to evaluation mode)\n",
        "    model.train()\n",
        "\n",
        "    # Add batch to GPU\n",
        "    batch = tuple(t.to(device) for t in batch)\n",
        "    # Unpack the inputs from our dataloader\n",
        "    b_input_ids, b_input_mask, b_labels = batch\n",
        "\n",
        "    # Forward pass\n",
        "    outputs = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask, labels=b_labels)\n",
        "    loss = outputs[0]\n",
        "\n",
        "    # Backward pass\n",
        "    loss.backward()\n",
        "\n",
        "    # Clip the norm of the gradients to 1.0\n",
        "    # Gradient clipping is not in AdamW anymore\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 learning rate schedule\n",
        "    scheduler.step()\n",
        "\n",
        "    # Clear the previous accumulated gradients\n",
        "    optimizer.zero_grad()\n",
        "\n",
        "    # Update tracking variables\n",
        "    batch_loss += loss.item()\n",
        "\n",
        "  # Calculate the average loss over the training data.\n",
        "  avg_train_loss = batch_loss / len(train_dataloader)\n",
        "\n",
        "  #store the current learning rate\n",
        "  for param_group in optimizer.param_groups:\n",
        "    print(\"\\n\\tCurrent Learning rate: \",param_group['lr'])\n",
        "    learning_rate.append(param_group['lr'])\n",
        "\n",
        "  train_loss_set.append(avg_train_loss)\n",
        "  print(F'\\n\\tAverage Training loss: {avg_train_loss}')\n",
        "\n",
        "  # Validation\n",
        "\n",
        "  # Put model in evaluation mode to evaluate loss on the validation set\n",
        "  model.eval()\n",
        "\n",
        "  # Tracking variables\n",
        "  eval_accuracy,eval_mcc_accuracy,nb_eval_steps = 0, 0, 0\n",
        "\n",
        "  # Evaluate data for one epoch\n",
        "  for batch in validation_dataloader:\n",
        "    # Add batch to GPU\n",
        "    batch = tuple(t.to(device) for t in batch)\n",
        "    # Unpack the inputs from our dataloader\n",
        "    b_input_ids, b_input_mask, b_labels = batch\n",
        "    # Telling the model not to compute or store gradients, saving memory and speeding up validation\n",
        "    with torch.no_grad():\n",
        "      # Forward pass, calculate logit predictions\n",
        "      logits = model(b_input_ids, token_type_ids=None, attention_mask=b_input_mask)\n",
        "\n",
        "    # Move logits and labels to CPU\n",
        "    logits = logits[0].to('cpu').numpy()\n",
        "    label_ids = b_labels.to('cpu').numpy()\n",
        "\n",
        "    pred_flat = np.argmax(logits, axis=1).flatten()\n",
        "    labels_flat = label_ids.flatten()\n",
        "\n",
        "    df_metrics=pd.DataFrame({'Epoch':epochs,'Actual_class':labels_flat,'Predicted_class':pred_flat})\n",
        "\n",
        "    tmp_eval_accuracy = accuracy_score(labels_flat,pred_flat)\n",
        "    tmp_eval_mcc_accuracy = matthews_corrcoef(labels_flat, pred_flat)\n",
        "\n",
        "    eval_accuracy += tmp_eval_accuracy\n",
        "    eval_mcc_accuracy += tmp_eval_mcc_accuracy\n",
        "    nb_eval_steps += 1\n",
        "\n",
        "  print(F'\\n\\tValidation Accuracy: {eval_accuracy/nb_eval_steps}')\n",
        "  print(F'\\n\\tValidation MCC Accuracy: {eval_mcc_accuracy/nb_eval_steps}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572,
          "referenced_widgets": [
            "be2fec1f96934406abb612e6d1ce08da",
            "cf2da7035cf14536af65db2ebc806f9b",
            "af43e868052a461e8f98e166cd1ebdb6",
            "6a516e8fbfea4381a63aba5c77e17574",
            "c887f5a8c3a547a8884886e5fc492232",
            "9dc0171dbbdf43bb8fa2b5d0d10797f6",
            "a9e844e0d9a749fabc276d0b0d1d41d2",
            "1a3fc0a640284b66bbfa9c0a75abeab7",
            "6e205344f57f4aea985f44909389c2f6",
            "dfefde4ddafa44948d529d5f24b39ae1",
            "28eab27377ff471f86bd53ee85392dcc"
          ]
        },
        "id": "VZNEwMxmT62f",
        "outputId": "86b4a5b0-6c2e-4bd6-f1ef-793cc00ef9ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-092e2ef2c53d>:8: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
            "  for _ in tnrange(1,epochs+1,desc='Epoch'):\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "be2fec1f96934406abb612e6d1ce08da"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<====================== Epoch 1 ======================>\n",
            "\n",
            "\tCurrent Learning rate:  1.3333333333333333e-05\n",
            "\n",
            "\tAverage Training loss: 0.3472916715551783\n",
            "\n",
            "\tValidation Accuracy: 0.9285714285714286\n",
            "\n",
            "\tValidation MCC Accuracy: 0.9075846919167607\n",
            "<====================== Epoch 2 ======================>\n",
            "\n",
            "\tCurrent Learning rate:  6.666666666666667e-06\n",
            "\n",
            "\tAverage Training loss: 0.11324520723449255\n",
            "\n",
            "\tValidation Accuracy: 0.933531746031746\n",
            "\n",
            "\tValidation MCC Accuracy: 0.9143567468757698\n",
            "<====================== Epoch 3 ======================>\n",
            "\n",
            "\tCurrent Learning rate:  0.0\n",
            "\n",
            "\tAverage Training loss: 0.08025390558713157\n",
            "\n",
            "\tValidation Accuracy: 0.9399801587301587\n",
            "\n",
            "\tValidation MCC Accuracy: 0.922300505957127\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Calculate the confusion matrix\n",
        "confusion_mat = confusion_matrix(true_labels, pred_labels)\n",
        "\n",
        "# Get the unique labels\n",
        "labels = np.unique(true_labels)\n",
        "\n",
        "# Create a figure and axes\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "\n",
        "# Plot the confusion matrix\n",
        "sns.heatmap(confusion_mat, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=labels, yticklabels=labels, ax=ax)\n",
        "\n",
        "# Set the axis labels and title\n",
        "ax.set_xlabel(\"Predicted Labels\")\n",
        "ax.set_ylabel(\"True Labels\")\n",
        "ax.set_title(\"Confusion Matrix\")\n",
        "\n",
        "# Rotate the x-axis labels for better readability (optional)\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7eWHYROifK1i",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 566
        },
        "outputId": "fa4c1327-4698-4c8c-a500-a9ffb5ba4c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), \"bert_model_soundscape.pt\")"
      ],
      "metadata": {
        "id": "PoABSgfqoQ8V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "torch.save(model.state_dict(), \"/content/bert_model.pt\")"
      ],
      "metadata": {
        "id": "heaoETDIpV9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.save_pretrained(\"/content/tokenizer\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z_yMhgsypgs0",
        "outputId": "b3b2a631-c6ee-4fc5-99cb-451ed6a82a70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('/content/tokenizer/tokenizer_config.json',\n",
              " '/content/tokenizer/special_tokens_map.json',\n",
              " '/content/tokenizer/vocab.txt',\n",
              " '/content/tokenizer/added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}