3_class_agri_clasification.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "vxsFWNDc1MWK"
      },
      "outputs": [],
      "source": [
        "\n",
        "file_id = '1rDltFC9NikOhuEWKWCpVSeQ6pW3LRJed'"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from pydrive.auth import GoogleAuth\n",
        "from google.colab import drive\n",
        "from pydrive.drive import GoogleDrive\n",
        "from google.colab import auth\n",
        "from oauth2client.client import GoogleCredentials\n",
        "from google.colab import files"
      ],
      "metadata": {
        "id": "2cKz1Rem2379"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()\n",
        "gauth = GoogleAuth()\n",
        "gauth.credentials = GoogleCredentials.get_application_default()\n",
        "drive = GoogleDrive(gauth)\n",
        "download = drive.CreateFile({'id': file_id})\n",
        "# Download the file to a local disc\n",
        "download.GetContentFile('data.csv')"
      ],
      "metadata": {
        "id": "eXlbvqvQ2qbL"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "xWlbaAmx3Ct2"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Read the CSV file without headers\n",
        "df = pd.read_csv('data.csv',sep = '|')\n",
        "\n",
        "# Assign headers 'class' and 'sentence'\n",
        "df.columns = [ 'sentence','class']\n",
        "\n",
        "# Now you can use the modified DataFrame 'df' for further processing\n"
      ],
      "metadata": {
        "id": "e7ApSVkv27O4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "GXE098Dze9eA",
        "outputId": "7efb4a5f-be4b-4058-b4ff-76de0f4a4e72"
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      "execution_count": 6,
      "outputs": [
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              "                                              sentence  \\\n",
              "0    My bank account number is not correct. Please ...   \n",
              "1    The installment for this month has not been re...   \n",
              "2    I have not received the money for the previous...   \n",
              "3    The account number mentioned on the portal is ...   \n",
              "4    I haven't received the installment for the cur...   \n",
              "..                                                 ...   \n",
              "160  My installment has not arrived and I need to p...   \n",
              "161  I haven't received the installment, which is m...   \n",
              "162  I need to procure mushroom spawn for my mushro...   \n",
              "163  The installment hasn't arrived, and I need to ...   \n",
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              "\n",
              "                             class  \n",
              "0    Account number is not Correct  \n",
              "1         Installment not received  \n",
              "2         Installment not received  \n",
              "3    Account number is not Correct  \n",
              "4         Installment not received  \n",
              "..                             ...  \n",
              "160       Installment not received  \n",
              "161       Installment not received  \n",
              "162       Installment not received  \n",
              "163       Installment not received  \n",
              "164       Installment not received  \n",
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    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GiKjuPkw3F03",
        "outputId": "6da508df-3d6e-41d3-88dd-c8a30d8aeb31"
      },
      "execution_count": 7,
      "outputs": [
        {
          "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-4.30.1-py3-none-any.whl (7.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.2/7.2 MB\u001b[0m \u001b[31m79.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.1+cu118)\n",
            "Requirement 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",
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            "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[31m107.4 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[31m70.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "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",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\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-4.30.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
        "from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import accuracy_score, f1_score"
      ],
      "metadata": {
        "id": "SCz1jiPz4Nxk"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Specify the BERT model and tokenizer\n",
        "model_name = 'bert-base-uncased'\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)\n",
        "model = BertForSequenceClassification.from_pretrained(model_name, num_labels=df['class'].nunique())\n"
      ],
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        "colab": {
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            "9b2c9cddf5994fe385cd5098fbc9a02d",
            "d327f771919c4831952127ac4e664c09",
            "b4bc64459794410697dd2700a3a581ca",
            "6c22b6410bdc47b09dbe1e171e44102a",
            "0fcde2f191c74c5aa8cbb716b4d5153c",
            "67a494863b2049ec86a03616963021b2",
            "d4d0132aa9cd4c3b8fd45c1ee604101b",
            "0c86edb8bf014b439c17d056a384d1b9"
          ]
        },
        "id": "huofqnFY4QwO",
        "outputId": "4f8741b8-50aa-4827-dc34-bc58f43cb7b2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "92d0e0374c284b9f9c506b99cb7c1c13"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/28.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0f41966f516848e5ba994ad178d98a71"
            }
          },
          "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": "e9f43175434d4531abb582d43553ec41"
            }
          },
          "metadata": {}
        },
        {
          "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,
              "model_id": "8520407cf10442eda1e9fa6e6fc8657e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', '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.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_full = df.copy()"
      ],
      "metadata": {
        "id": "K1Wx9yEG5L3m"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df= df_full.sample(df_full.shape[0])\n",
        "df['class'].nunique()\n",
        "label_encoder = LabelEncoder()\n",
        "df['label'] = label_encoder.fit_transform(df['class'])"
      ],
      "metadata": {
        "id": "N83CtKGb5rxM"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "kPvOORP19pdx"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_length = 128\n",
        "train_encodings = tokenizer(list(train_df['sentence']), truncation=True, padding=True, max_length=max_length)"
      ],
      "metadata": {
        "id": "qImdYZEV9rks"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_encodings = tokenizer(list(val_df['sentence']), truncation=True, padding=True, max_length=max_length)\n"
      ],
      "metadata": {
        "id": "0Qkmata89yAA"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the tokenized data to PyTorch tensors\n",
        "train_dataset = TensorDataset(torch.tensor(train_encodings['input_ids']),\n",
        "                              torch.tensor(train_encodings['attention_mask']),\n",
        "                              torch.tensor(train_df['label'].values))"
      ],
      "metadata": {
        "id": "VLH6oRiV927G"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_dataset = TensorDataset(torch.tensor(val_encodings['input_ids']),\n",
        "                            torch.tensor(val_encodings['attention_mask']),\n",
        "                            torch.tensor(val_df['label'].values))"
      ],
      "metadata": {
        "id": "ue61F1Vk_FzS"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create data loaders for training and validation\n",
        "batch_size = 16\n",
        "train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_dataloader = DataLoader(val_dataset, batch_size=batch_size)\n"
      ],
      "metadata": {
        "id": "KPocWe1h_JXD"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Set the device for training\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "\n",
        "# Set the optimizer and learning rate\n",
        "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
        "from sklearn.metrics import accuracy_score, f1_score\n",
        "\n",
        "# Training loop\n",
        "epochs = 15\n",
        "model.to(device)\n",
        "model.train()\n",
        "\n",
        "prev_loss = float('inf')\n",
        "no_decrease_count = 0\n",
        "\n",
        "for epoch in range(epochs):\n",
        "    total_loss = 0\n",
        "    total_correct = 0\n",
        "    total_samples = 0\n",
        "    all_predicted_labels = []\n",
        "    all_true_labels = []\n",
        "\n",
        "    for batch in train_dataloader:\n",
        "        inputs = {'input_ids': batch[0].to(device),\n",
        "                  'attention_mask': batch[1].to(device),\n",
        "                  'labels': batch[2].to(device)}\n",
        "        outputs = model(**inputs)\n",
        "        loss = outputs.loss\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        predicted_labels = torch.argmax(outputs.logits, dim=1)\n",
        "        true_labels = inputs['labels']\n",
        "        total_loss += loss.item()\n",
        "        total_correct += (predicted_labels == true_labels).sum().item()\n",
        "        total_samples += len(true_labels)\n",
        "        all_predicted_labels.extend(predicted_labels.tolist())\n",
        "        all_true_labels.extend(true_labels.tolist())\n",
        "\n",
        "    # Calculate accuracy and F1 score\n",
        "    epoch_loss = total_loss / len(train_dataloader)\n",
        "    epoch_accuracy = total_correct / total_samples\n",
        "    epoch_f1_score = f1_score(all_true_labels, all_predicted_labels, average='weighted')\n",
        "\n",
        "    print(f\"Epoch {epoch+1}/{epochs} - Loss: {epoch_loss:.4f} - Accuracy: {epoch_accuracy:.4f} - F1 Score: {epoch_f1_score:.4f}\")\n",
        "\n",
        "    # Check if the loss decreased\n",
        "    if epoch_loss >= prev_loss:\n",
        "        no_decrease_count += 1\n",
        "    else:\n",
        "        no_decrease_count = 0\n",
        "\n",
        "    prev_loss = epoch_loss\n",
        "\n",
        "    # Stop training if there is no decrease in loss for 2 consecutive iterations\n",
        "    if no_decrease_count >= 2:\n",
        "        print(\"No decrease in loss for 2 consecutive iterations. Stopping training.\")\n",
        "        break\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9O332D4-4gKJ",
        "outputId": "7154a31f-51b9-4e6a-ead5-cc8a40dfe5ac"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15 - Loss: 1.0574 - Accuracy: 0.4242 - F1 Score: 0.4202\n",
            "Epoch 2/15 - Loss: 0.7780 - Accuracy: 0.7500 - F1 Score: 0.6952\n",
            "Epoch 3/15 - Loss: 0.5212 - Accuracy: 0.8939 - F1 Score: 0.8825\n",
            "Epoch 4/15 - Loss: 0.3214 - Accuracy: 0.9773 - F1 Score: 0.9767\n",
            "Epoch 5/15 - Loss: 0.1612 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 6/15 - Loss: 0.0883 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 7/15 - Loss: 0.0592 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 8/15 - Loss: 0.0418 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 9/15 - Loss: 0.0321 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 10/15 - Loss: 0.0258 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 11/15 - Loss: 0.0213 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 12/15 - Loss: 0.0192 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 13/15 - Loss: 0.0160 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 14/15 - Loss: 0.0142 - Accuracy: 1.0000 - F1 Score: 1.0000\n",
            "Epoch 15/15 - Loss: 0.0121 - Accuracy: 1.0000 - F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Evaluation\n",
        "model.eval()\n",
        "all_predicted_labels = []\n",
        "all_true_labels = []\n",
        "\n",
        "with torch.no_grad():\n",
        "    for batch in val_dataloader:\n",
        "        inputs = {'input_ids': batch[0].to(device),\n",
        "                  'attention_mask': batch[1].to(device),\n",
        "                  'labels': batch[2].to(device)}\n",
        "        outputs = model(**inputs)\n",
        "        logits = outputs.logits\n",
        "        predicted_labels = torch.argmax(logits, dim=1)\n",
        "        all_predicted_labels.extend(predicted_labels.cpu().numpy())\n",
        "        all_true_labels.extend(inputs['labels'].cpu().numpy())\n",
        "\n",
        "# Calculate accuracy and F1 score\n",
        "val_accuracy = accuracy_score(all_true_labels, all_predicted_labels)\n",
        "val_f1_score = f1_score(all_true_labels, all_predicted_labels, average='weighted')\n",
        "\n",
        "print(f\"Validation Accuracy: {val_accuracy:.4f}\")\n",
        "print(f\"Validation F1 Score: {val_f1_score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4S8C21Qb_U4h",
        "outputId": "0c5dcb79-94cb-4aa2-b272-462579dad775"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation Accuracy: 1.0000\n",
            "Validation F1 Score: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install git-lfs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bny8VKv8qtbY",
        "outputId": "965333d0-a67c-4c12-8f69-8770dcba65e7"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.9.2-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 38 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store"
      ],
      "metadata": {
        "id": "iQlf9EMWt9tS"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EId0gRusqfMb",
        "outputId": "d144d7d2-b938-4a0c-c4c2-40377b56ed4e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved in your configured git credential helpers (store).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://huggingface.co/GautamR/model_grievance_class"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Igbrlaj6Q0B",
        "outputId": "455d9004-3422-4177-d7b2-3d8e609358d2"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Error: Failed to call git rev-parse --git-dir: exit status 128 \n",
            "Git LFS initialized.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Commit as usual\n",
        "%cd model_grievance_class\n",
        "!git lfs install\n",
        "!git config --global user.email \"gautam@samagragovernance.in\"\n",
        "# Tip: using the same email than for your huggingface.co account will link your commits to your profile\n",
        "!git config --global user.name \"GautamR\"\n",
        "!echo \"hello\" >> README.md\n",
        "!git add . && git commit -m \"Update from $USER\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eODhCRQn8DZL",
        "outputId": "9dc0794d-ad13-4e2d-9f7a-405c0913f4fd"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Updated git hooks.\n",
            "Git LFS initialized.\n",
            "[main effb583] Update from\n",
            " 1 file changed, 2 insertions(+)\n",
            " create mode 100644 README.md\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_pretrained('.')\n",
        "tokenizer.save_pretrained('.')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jmo4_gUx8iDH",
        "outputId": "e55ec6f8-7eb3-40e9-e843-886fa6286467"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./tokenizer_config.json',\n",
              " './special_tokens_map.json',\n",
              " './vocab.txt',\n",
              " './added_tokens.json')"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod +x .git/hooks/pre-push"
      ],
      "metadata": {
        "id": "WkgjNawg8_uc"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git add .\n",
        "!git commit -m \"Initial commit\"\n",
        "!git push"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3xSieOwc9BbN",
        "outputId": "4d26d735-6bcd-4383-ed6e-314c4f221c33"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[main 449a958] Initial commit\n",
            " 5 files changed, 30584 insertions(+)\n",
            " create mode 100644 config.json\n",
            " create mode 100644 pytorch_model.bin\n",
            " create mode 100644 special_tokens_map.json\n",
            " create mode 100644 tokenizer_config.json\n",
            " create mode 100644 vocab.txt\n",
            "Enumerating objects: 11, done.\n",
            "Counting objects: 100% (11/11), done.\n",
            "Delta compression using up to 4 threads\n",
            "Compressing objects: 100% (9/9), done.\n",
            "Writing objects: 100% (10/10), 109.55 KiB | 5.22 MiB/s, done.\n",
            "Total 10 (delta 1), reused 0 (delta 0)\n",
            "remote: \u001b[33m-------------------------------------------------------------------------\u001b[0m\u001b[K\n",
            "remote: \u001b[33mYour push was accepted, but with warnings: \u001b[0m\u001b[K\n",
            "remote: \u001b[33m- Warning: empty or missing yaml metadata in repo card\u001b[0m\u001b[K\n",
            "remote: \u001b[33mhelp: https://huggingface.co/docs/hub/model-cards#model-card-metadata\u001b[0m\u001b[K\n",
            "remote: \u001b[33m-------------------------------------------------------------------------\u001b[0m\u001b[K\n",
            "remote: \u001b[32m-------------------------------------------------------------------------\u001b[0m\u001b[K\n",
            "remote: \u001b[32mPlease find the documentation at:\u001b[0m\u001b[K\n",
            "remote: \u001b[32mhttps://huggingface.co/docs/hub/model-cards#model-card-metadata\u001b[0m\u001b[K\n",
            "remote: \u001b[32m\u001b[0m\u001b[K\n",
            "remote: \u001b[32m-------------------------------------------------------------------------\u001b[0m\u001b[K\n",
            "To https://huggingface.co/GautamR/model_grievance_class\n",
            "   164d277..449a958  main -> main\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GPT data creation"
      ],
      "metadata": {
        "id": "p3ZaBBQJfUNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prompt =  ''' Pradhan Mantri Kisan Samman Nidhi (PIV-KISAN) is a new Central Sector Scheme to\n",
        "provide income support to all landholding farmers' families in the country to\n",
        "supplement their financial needs for procuring various inputs related to agriculture and\n",
        "allied activities as well as domestic needs. Under the Scheme, the entire financial\n",
        "liability towards transfer of benefit to targeted beneficiaries will be borne by\n",
        "Government of India. We are building a classifier for training a grievance redressal system for it which classifies feedback/grievance provided to it into 3 buckets.\n",
        "The  buckets are :\n",
        "['Account number is not Correct' ,'Installment not received', 'Others']\n",
        "\n",
        "'Account number not correct' is for those grievances/ feedback which talk about the bank account not being correct on the portal for these farmers.\n",
        "'Installment not received' is for those feedback which talk about the farmer not receiving the installment/money for that month.\n",
        "'Others'  cover all other types of grievances - including 'Gender being wrong ,'Online Application pnding approval,' pther Payment Related issues','Problem in Adhaar Correction' ,'problem in bio-metric based e-kyc','Problem in OTP based e-kyc', 'Tansaction Failed' etc etc.\n",
        "\n",
        "Can you create some training data in an Indian context ( in the form of a pipe separated file ) on which I can train BERT  to be able to classify the grievances. I need two columns - 'grievance' (the text from the user with the feedback) and 'class' (the classification into the above 3 buckets)  - can you provide 30 examples for each bucket in a csv format. Can you also include some bigger grievences of 4-5 sentences.  while most are around 2 sentences'''"
      ],
      "metadata": {
        "id": "G-aZGI8ifWbh"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ij8B4xIcpJMD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}