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Stress Categorization Using BERT Transformer.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "TdrNem1HbCQD"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
+        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Embedding, Flatten, Dense, LSTM, Dropout\n",
+        "from tensorflow.keras.utils import to_categorical\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import LabelEncoder"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "s64hY6QGbZgP",
+        "outputId": "3407cb60-0fdd-40d5-85cd-ab8cd2cfa4e1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Collecting transformers\n",
+            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m28.9 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) (3.12.2)\n",
+            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m17.5 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) (1.23.5)\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) (6.0.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\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[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
+            "  Downloading safetensors-0.3.2-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[31m51.3 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) (4.66.1)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (2023.6.0)\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) (4.7.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\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: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
+            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
+            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.2 tokenizers-0.13.3 transformers-4.31.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241,
+          "referenced_widgets": [
+            "32a00fccb8f446359ea356e01b50be59",
+            "db850c05bccd4dc9af135a680bad809e",
+            "99fbbbb3a9dd4bce9f79e0fa3be04e17",
+            "4fab7cf2e4c54d4fbe96b8cefef8bd82",
+            "f0f7ae319b314e309c61a0b007637b01",
+            "a6d0f7a34c5d4d3386ded0334163f67f",
+            "32cb7af7887f4a8584a0069b345f9b72",
+            "3e89a9d94cbd418f810d288add0b86a5",
+            "97919643cb5049d6af4f561d5abefc8a",
+            "f51e8488f2544cb28b80f7bb8203a0f1",
+            "c9e1e3ddf9b44ae4b7ee048aa8640987"
+          ]
+        },
+        "id": "q9IQ8LtfbEaY",
+        "outputId": "687198cd-5896-414d-fd5c-6f0b0dbd2a2c"
+      },
+      "outputs": [
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stderr",
+          "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/python3.10/dist-packages/transformers/tokenization_utils_base.py:2418: 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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "32a00fccb8f446359ea356e01b50be59",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Downloading model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "All PyTorch model weights were used when initializing TFBertForSequenceClassification.\n",
+            "\n",
+            "Some weights or buffers of the TF 2.0 model TFBertForSequenceClassification were not initialized from the PyTorch model 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"
+          ]
+        },
+        {
+          "metadata": {
+            "tags": null
+          },
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "     48/Unknown - 1493s 30s/step - loss: 1.2474 - accuracy: 0.3886"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import re\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
+        "from transformers import InputExample, InputFeatures\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# 1. Load and inspect the data\n",
+        "data = pd.read_excel('stress_data.xlsx')\n",
+        "\n",
+        "# 2. Clean and preprocess the data\n",
+        "def clean_text(text):\n",
+        "    text = text.lower()\n",
+        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
+        "    text = re.sub(r'\\d+|\\W+', ' ', text)\n",
+        "    return text\n",
+        "\n",
+        "data['Cleaned_Posts'] = data['Posts'].apply(clean_text)\n",
+        "\n",
+        "# Convert string labels to integer indices\n",
+        "label_encoder = LabelEncoder()\n",
+        "\n",
+        "data['LabelIndices'] = label_encoder.fit_transform(data['Labels'])\n",
+        "\n",
+        "# 3. Tokenize data using BERT's tokenizer\n",
+        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n",
+        "\n",
+        "# Split the data into train and test\n",
+        "train, test = train_test_split(data, test_size=0.2, random_state=42)\n",
+        "\n",
+        "# Convert data to InputExample format\n",
+        "def convert_data_to_input_example(data):\n",
+        "    return data.apply(lambda x: InputExample(guid=None, text_a=x['Cleaned_Posts'], text_b=None, label=x['LabelIndices']), axis=1)\n",
+        "\n",
+        "train_InputExamples = convert_data_to_input_example(train)\n",
+        "test_InputExamples = convert_data_to_input_example(test)\n",
+        "\n",
+        "# Convert to features for BERT input\n",
+        "def convert_input_example_to_feature(example):\n",
+        "    return tokenizer.encode_plus(example.text_a, add_special_tokens=True, max_length=128, pad_to_max_length=True, return_attention_mask=True, return_token_type_ids=False)\n",
+        "\n",
+        "train_features = train_InputExamples.apply(convert_input_example_to_feature)\n",
+        "test_features = test_InputExamples.apply(convert_input_example_to_feature)\n",
+        "\n",
+        "# Convert features to tensorflow dataset\n",
+        "def convert_features_to_tf_dataset(features, labels):\n",
+        "    def gen():\n",
+        "        for f, l in zip(features, labels):\n",
+        "            yield ({'input_ids': f['input_ids'], 'attention_mask': f['attention_mask']}, l)\n",
+        "    return tf.data.Dataset.from_generator(gen, ({'input_ids': tf.int32, 'attention_mask': tf.int32}, tf.int64), ({'input_ids': tf.TensorShape([None]), 'attention_mask': tf.TensorShape([None])}, tf.TensorShape([])))\n",
+        "\n",
+        "train_dataset = convert_features_to_tf_dataset(train_features, train['LabelIndices']).shuffle(100).batch(32).repeat(2)\n",
+        "test_dataset = convert_features_to_tf_dataset(test_features, test['LabelIndices']).batch(32)\n",
+        "\n",
+        "# 4. Fine-tune BERT on the dataset\n",
+        "model_new = TFBertForSequenceClassification.from_pretrained(\"bert-base-uncased\", num_labels=len(data['Labels'].unique()))\n",
+        "model_new.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=3e-5, epsilon=1e-08, clipnorm=1.0), loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=[tf.keras.metrics.SparseCategoricalAccuracy('accuracy')])\n",
+        "model_new.fit(train_dataset, epochs=1, validation_data=test_dataset)\n",
+        "\n",
+        "# 5. Evaluate the model\n",
+        "loss, accuracy = model_new.evaluate(test_dataset)\n",
+        "print(f\"Test accuracy: {accuracy}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "model_new.save_pretrained(\"./saved_model/\")\n",
+        "\n",
+        "!zip -r saved_model.zip ./saved_model/\n",
+        "\n",
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "metadata": {
+        "id": "eJ-539Cvm2qr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "EoY_YzaYmHjC"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "nF9CTCGxmH1m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "B-JSmVxEmICh"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# New Section"
+      ],
+      "metadata": {
+        "id": "Cl_clsbfmI2u"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "nYgWKFBdmEPR"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pip install transformers"
+      ],
+      "metadata": {
+        "id": "9khb6mJwmE3F",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "51a737a0-197f-491f-a815-4bc410350376"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting transformers\n",
+            "  Downloading transformers-4.32.0-py3-none-any.whl (7.5 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.5/7.5 MB\u001b[0m \u001b[31m72.5 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) (3.12.2)\n",
+            "Collecting huggingface-hub<1.0,>=0.15.1 (from transformers)\n",
+            "  Downloading huggingface_hub-0.16.4-py3-none-any.whl (268 kB)\n",
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+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\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[31m79.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
+            "  Downloading safetensors-0.3.3-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[31m86.6 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) (4.66.1)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (2023.6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.15.1->transformers) (4.7.1)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.2.0)\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: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
+            "Installing collected packages: tokenizers, safetensors, huggingface-hub, transformers\n",
+            "Successfully installed huggingface-hub-0.16.4 safetensors-0.3.3 tokenizers-0.13.3 transformers-4.32.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
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+          "height": 197,
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+            "7c7adbd1a76446c0aba2f535e2c1fabd",
+            "69fae47244e542f886a1e487a3126aef"
+          ]
+        },
+        "id": "64dIHCvibh3a",
+        "outputId": "28d3915d-306b-4009-825c-9ea66866e6ec"
+      },
+      "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": "b233a84faeeb4e6db05b54853a712306"
+            }
+          },
+          "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": "47380f5fb7a84117a5111b793112ccb7"
+            }
+          },
+          "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": "aea1a283ac1b41e2b62c1f09f6afff45"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import re\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
+        "from transformers import InputExample, InputFeatures\n",
+        "import tensorflow as tf\n",
+        "\n",
+        "# 1. Load and inspect the data\n",
+        "data = pd.read_excel('stress_data.xlsx')\n",
+        "\n",
+        "# 2. Clean and preprocess the data\n",
+        "def clean_text(text):\n",
+        "    text = text.lower()\n",
+        "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
+        "    text = re.sub(r'\\d+|\\W+', ' ', text)\n",
+        "    return text\n",
+        "\n",
+        "data['Cleaned_Posts'] = data['Posts'].apply(clean_text)\n",
+        "\n",
+        "# Convert string labels to integer indices\n",
+        "label_encoder = LabelEncoder()\n",
+        "\n",
+        "data['LabelIndices'] = label_encoder.fit_transform(data['Labels'])\n",
+        "\n",
+        "# 3. Tokenize data using BERT's tokenizer\n",
+        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\", do_lower_case=True)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "tokenizer"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lh_iyQJIhLHi",
+        "outputId": "effbbea1-4738-4dd6-89d0-b028e35deb69"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "BertTokenizer(name_or_path='bert-base-uncased', vocab_size=30522, model_max_length=512, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "Rs3J2e_fS16I",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 597
+        },
+        "outputId": "e07d3e9e-a6de-4acb-a20f-538d33d6e370"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                                  Posts            Labels  \\\n",
+              "0     I quit my job of 3 years due to my stress and ...       Work Stress   \n",
+              "1     I hate my Job and have a sick note due to stre...       Work Stress   \n",
+              "2     For those who resigned this (recession) year d...       Work Stress   \n",
+              "3     Anyone always sort of stressed due to this job...       Work Stress   \n",
+              "4     Wife has been unemployed for 3 months after sh...       Work Stress   \n",
+              "...                                                 ...               ...   \n",
+              "1817  Bullard Discusses Financial Stress, Rate Incre...  Financial Stress   \n",
+              "1818                  USA Financial Stress v. Equities   Financial Stress   \n",
+              "1819  Why short term financial stresses could actual...  Financial Stress   \n",
+              "1820  Please help! UK undergraduate students needed ...  Financial Stress   \n",
+              "1821  Danielle DiMartino Booth, CEO of Quill Intelli...  Financial Stress   \n",
+              "\n",
+              "                                          Cleaned_Posts  LabelIndices  \n",
+              "0     i quit my job of   years due to my stress and ...             3  \n",
+              "1     i hate my job and have a sick note due to stre...             3  \n",
+              "2     for those who resigned this recession year due...             3  \n",
+              "3     anyone always sort of stressed due to this job...             3  \n",
+              "4     wife has been unemployed for   months after sh...             3  \n",
+              "...                                                 ...           ...  \n",
+              "1817  bullard discusses financial stress rate increa...             0  \n",
+              "1818                   usa financial stress v equities              0  \n",
+              "1819  why short term financial stresses could actual...             0  \n",
+              "1820  please help uk undergraduate students needed f...             0  \n",
+              "1821  danielle dimartino booth ceo of quill intellig...             0  \n",
+              "\n",
+              "[1822 rows x 4 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-88e42b60-8aee-4c88-9fa0-0ad9f4820cf8\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "    .dataframe thead th {\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Posts</th>\n",
+              "      <th>Labels</th>\n",
+              "      <th>Cleaned_Posts</th>\n",
+              "      <th>LabelIndices</th>\n",
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+              "      <th>0</th>\n",
+              "      <td>I quit my job of 3 years due to my stress and ...</td>\n",
+              "      <td>Work Stress</td>\n",
+              "      <td>i quit my job of   years due to my stress and ...</td>\n",
+              "      <td>3</td>\n",
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+              "    <tr>\n",
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+              "      <td>Work Stress</td>\n",
+              "      <td>i hate my job and have a sick note due to stre...</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>For those who resigned this (recession) year d...</td>\n",
+              "      <td>Work Stress</td>\n",
+              "      <td>for those who resigned this recession year due...</td>\n",
+              "      <td>3</td>\n",
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+              "      <td>3</td>\n",
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+              "      <td>Work Stress</td>\n",
+              "      <td>wife has been unemployed for   months after sh...</td>\n",
+              "      <td>3</td>\n",
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+              "      <td>...</td>\n",
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+              "      <th>1817</th>\n",
+              "      <td>Bullard Discusses Financial Stress, Rate Incre...</td>\n",
+              "      <td>Financial Stress</td>\n",
+              "      <td>bullard discusses financial stress rate increa...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1818</th>\n",
+              "      <td>USA Financial Stress v. Equities</td>\n",
+              "      <td>Financial Stress</td>\n",
+              "      <td>usa financial stress v equities</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1819</th>\n",
+              "      <td>Why short term financial stresses could actual...</td>\n",
+              "      <td>Financial Stress</td>\n",
+              "      <td>why short term financial stresses could actual...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1820</th>\n",
+              "      <td>Please help! UK undergraduate students needed ...</td>\n",
+              "      <td>Financial Stress</td>\n",
+              "      <td>please help uk undergraduate students needed f...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1821</th>\n",
+              "      <td>Danielle DiMartino Booth, CEO of Quill Intelli...</td>\n",
+              "      <td>Financial Stress</td>\n",
+              "      <td>danielle dimartino booth ceo of quill intellig...</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>1822 rows × 4 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88e42b60-8aee-4c88-9fa0-0ad9f4820cf8')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-88e42b60-8aee-4c88-9fa0-0ad9f4820cf8 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-88e42b60-8aee-4c88-9fa0-0ad9f4820cf8');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: #E8F0FE;\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: #1967D2;\n",
+              "    height: 32px;\n",
+              "    padding: 0 0 0 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: #E2EBFA;\n",
+              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: #174EA6;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart:hover {\n",
+              "    background-color: #434B5C;\n",
+              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const charts = await google.colab.kernel.invokeFunction(\n",
+              "          'suggestCharts', [key], {});\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-7a1ef45f-d10e-455e-9b16-01d8033d4331 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ],
+      "source": [
+        "data"
+      ]
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+      "source": [],
+      "metadata": {
+        "id": "wxVfqH-slZWd"
+      },
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+      "outputs": []
+    }
+  ],
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+    },
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+}

config.json

@@ -0,0 +1,37 @@
+{
+  "_name_or_path": "bert-base-uncased",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1",
+    "2": "LABEL_2",
+    "3": "LABEL_3"
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+    "LABEL_2": 2,
+    "LABEL_3": 3
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.31.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

tf_model.h5

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