bert-large-finetune-validation-3epoch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "df4c22d3-c023-4a4e-8fd2-1179a715e88c",
   "metadata": {},
   "source": [
    "# Testing Model over updated weights \n",
    "\n",
    "## Epochs = 3\n",
    "## Accuracy = 97\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35b84b76-34c7-4ba6-9cbb-c993ebb55b0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-large-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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "BertForSequenceClassification(\n",
       "  (bert): BertModel(\n",
       "    (embeddings): BertEmbeddings(\n",
       "      (word_embeddings): Embedding(30522, 1024, padding_idx=0)\n",
       "      (position_embeddings): Embedding(512, 1024)\n",
       "      (token_type_embeddings): Embedding(2, 1024)\n",
       "      (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
       "      (dropout): Dropout(p=0.1, inplace=False)\n",
       "    )\n",
       "    (encoder): BertEncoder(\n",
       "      (layer): ModuleList(\n",
       "        (0-23): 24 x BertLayer(\n",
       "          (attention): BertAttention(\n",
       "            (self): BertSdpaSelfAttention(\n",
       "              (query): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (key): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (value): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "            (output): BertSelfOutput(\n",
       "              (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "              (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
       "              (dropout): Dropout(p=0.1, inplace=False)\n",
       "            )\n",
       "          )\n",
       "          (intermediate): BertIntermediate(\n",
       "            (dense): Linear(in_features=1024, out_features=4096, bias=True)\n",
       "            (intermediate_act_fn): GELUActivation()\n",
       "          )\n",
       "          (output): BertOutput(\n",
       "            (dense): Linear(in_features=4096, out_features=1024, bias=True)\n",
       "            (LayerNorm): LayerNorm((1024,), eps=1e-12, elementwise_affine=True)\n",
       "            (dropout): Dropout(p=0.1, inplace=False)\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (pooler): BertPooler(\n",
       "      (dense): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "      (activation): Tanh()\n",
       "    )\n",
       "  )\n",
       "  (dropout): Dropout(p=0.1, inplace=False)\n",
       "  (classifier): Linear(in_features=1024, out_features=2, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from transformers import BertTokenizer, BertForSequenceClassification\n",
    "from safetensors.torch import load_file\n",
    "\n",
    "# Define the path to the best checkpoint\n",
    "checkpoint_path = \"./bert-large-hate-speech3epoch/checkpoint-30633\"  # Update to actual best checkpoint path\n",
    "\n",
    "# Load tokenizer\n",
    "tokenizer = BertTokenizer.from_pretrained(\"bert-large-uncased\")\n",
    "\n",
    "# Load model architecture\n",
    "model = BertForSequenceClassification.from_pretrained(\"bert-large-uncased\", num_labels=2)\n",
    "\n",
    "# Load fine-tuned weights\n",
    "weights_path = f\"{checkpoint_path}/model.safetensors\"\n",
    "model.load_state_dict(load_file(weights_path))\n",
    "model.eval()  # Set to evaluation mode\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c8069474-eaa8-4f60-8c65-0f4f45a3c30f",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cpu\")\n",
    "model.to(device)  # Move model to GPU\n",
    "def predict_hate_speech(texts, batch_size=32):\n",
    "    \"\"\"Efficiently tokenize and predict labels in batches.\"\"\"\n",
    "    all_preds = []\n",
    "    model.eval()  # Ensure model is in evaluation mode\n",
    "    \n",
    "    for i in range(0, len(texts), batch_size):\n",
    "        batch_texts = texts[i : i + batch_size]\n",
    "        \n",
    "        # Tokenize the batch\n",
    "        inputs = tokenizer(batch_texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=128)\n",
    "        inputs = {key: val.to(device) for key, val in inputs.items()}  # Move to GPU\n",
    "\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**inputs)\n",
    "        \n",
    "        # Get predicted labels\n",
    "        logits = outputs.logits\n",
    "        batch_preds = torch.argmax(logits, dim=-1).cpu().tolist()  # Move to CPU for processing\n",
    "        all_preds.extend(batch_preds)\n",
    "\n",
    "    return all_preds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "54a0fbd6-ed4a-47e3-8b2a-0b0235912499",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text:  i ve seen afro americans who look and undoubtedly act like him\n",
      "Prediction: [1, 1]\n",
      "\n",
      "Text: hyper text transfer protocol www occultopedia com f feral children http www long lands ac uk chimp la http www occultopedia com wild peter him aragon again every time i will see filthy comment about georgia without providing any facts i will respond\n",
      "Prediction: [0, 0, 0, 0, 1, 1, 1, 0]\n",
      "\n",
      "Text: i will respond until armenian scum will stop\n",
      "Prediction: [0, 0]\n",
      "\n",
      "Text: the reason large white families were encouraged those days what a slut to build up the population of white germans which were ravaged by the jewish attacks at them during and in hyperinflation\n",
      "Prediction: [0, 1, 1, 1, 1, 0]\n",
      "\n",
      "Text: i see a nation where i can walk out my front door and not feel like a minority in my own country\n",
      "Prediction: [0, 0, 1]\n",
      "\n",
      "Text: where a white woman can go about in public and not fear assault by mud,no i see that as giving up\n",
      "Prediction: [1, 1, 1, 0]\n",
      "\n",
      "Text: i hope one day to proudly say again that i am an american when whatever take this country back\n",
      "Prediction: [0, 1, 1]\n",
      "\n",
      "Text: okay i have been through some other threads on this section about nice and watched a few videos on you there\n",
      "Prediction: [0, 0, 0, 0]\n",
      "\n",
      "Text: it has obviously been misunderstood and i would have been keen to learn more\n",
      "Prediction: [0, 0, 0]\n",
      "\n",
      "Text: i retract always because an animal lacks the skills to properly raise a half human child\n",
      "Prediction: [1, 1, 1]\n",
      "\n",
      "Text: i apologize for my ignorance\n",
      "Prediction: [0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Example sentences\n",
    "samples = [\n",
    " \" i ve seen afro americans who look and undoubtedly act like him\",\n",
    "\"hyper text transfer protocol www occultopedia com f feral children http www long lands ac uk chimp la http www occultopedia com wild peter him aragon again every time i will see filthy comment about georgia without providing any facts i will respond\",\n",
    "\"i will respond until armenian scum will stop\",\n",
    "\"the reason large white families were encouraged those days what a slut to build up the population of white germans which were ravaged by the jewish attacks at them during and in hyperinflation\",\n",
    "\"i see a nation where i can walk out my front door and not feel like a minority in my own country\",\n",
    "\"where a white woman can go about in public and not fear assault by mud,\"\n",
    "\"no i see that as giving up\",\n",
    "\"i hope one day to proudly say again that i am an american when whatever take this country back\",\n",
    "\"okay i have been through some other threads on this section about nice and watched a few videos on you there\",\n",
    "\"it has obviously been misunderstood and i would have been keen to learn more\",\n",
    "\"i retract always because an animal lacks the skills to properly raise a half human child\",\n",
    "\"i apologize for my ignorance\"\n",
    "]\n",
    "\n",
    "# Run predictions\n",
    "for text in samples:\n",
    "    result = predict_hate_speech(text)\n",
    "    print(f\"Text: {text}\\nPrediction: {result}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "774ee47c-dcfe-4b89-bdc9-8ccc77e8a6de",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"HateSpeechDatasetBalanced.csv\")\n",
    "val_texts = df[\"Content\"].tolist()\n",
    "val_labels = df[\"Label\"].tolist()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e8dcd51b-fa0b-4d5e-84be-821a02ad5fd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "                  precision    recall  f1-score   support\n",
      "\n",
      "Not Hate Speech       0.94      0.97      0.95    361594\n",
      "    Hate Speech       0.97      0.94      0.95    364525\n",
      "\n",
      "       accuracy                           0.95    726119\n",
      "      macro avg       0.95      0.95      0.95    726119\n",
      "   weighted avg       0.95      0.95      0.95    726119\n",
      "\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import torch\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from transformers import BertTokenizer, BertForSequenceClassification\n",
    "from safetensors.torch import load_file\n",
    "\n",
    "\n",
    "predicted_labels = predict_hate_speech(val_texts)\n",
    "\n",
    "# Generate confusion matrix\n",
    "cm = confusion_matrix(val_labels, predicted_labels)\n",
    "\n",
    "# Print classification report\n",
    "print(\"Classification Report:\\n\", classification_report(val_labels, predicted_labels, target_names=[\"Not Hate Speech\", \"Hate Speech\"]))\n",
    "\n",
    "# Plot confusion matrix\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=[\"Not Hate Speech\", \"Hate Speech\"], yticklabels=[\"Not Hate Speech\", \"Hate Speech\"])\n",
    "plt.xlabel(\"Predicted Label\")\n",
    "plt.ylabel(\"True Label\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8c7ad38e-0899-4b8f-845d-1c16ef4af821",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_fscore_support\n",
    "\n",
    "# Compute precision, recall, and f1-score\n",
    "precision, recall, f1, _ = precision_recall_fscore_support(val_labels, predicted_labels, average=None)\n",
    "\n",
    "# Create DataFrame for visualization\n",
    "metrics_df = pd.DataFrame({\n",
    "    \"Class\": [\"Not Hate Speech\", \"Hate Speech\"],\n",
    "    \"Precision\": precision,\n",
    "    \"Recall\": recall,\n",
    "    \"F1-score\": f1\n",
    "})\n",
    "\n",
    "# Plot\n",
    "metrics_df.set_index(\"Class\").plot(kind=\"bar\", figsize=(8, 5), colormap=\"coolwarm\", edgecolor=\"black\")\n",
    "plt.title(\"Precision, Recall, and F1-score\")\n",
    "plt.ylabel(\"Score\")\n",
    "plt.xticks(rotation=0)\n",
    "plt.ylim(0, 1)\n",
    "plt.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4cee6336-2290-4469-842f-008b0a945841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Text:  i ve seen afro americans who look and undoubtedly act like him\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: hyper text transfer protocol www occultopedia com f feral children http www long lands ac uk chimp la http www occultopedia com wild peter him aragon again every time i will see filthy comment about georgia without providing any facts i will respond\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: i will respond until armenian scum will stop\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: the reason large white families were encouraged those days what a slut to build up the population of white germans which were ravaged by the jewish attacks at them during and in hyperinflation\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: i see a nation where i can walk out my front door and not feel like a minority in my own country\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: where a white woman can go about in public and not fear assault by mud,no i see that as giving up\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: i hope one day to proudly say again that i am an american when whatever take this country back\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: okay i have been through some other threads on this section about nice and watched a few videos on you there\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: it has obviously been misunderstood and i would have been keen to learn more\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: i retract always because an animal lacks the skills to properly raise a half human child\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: i apologize for my ignorance\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: I have no empathy for the palestinians, they deserve it\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: Justice was done in 1940s germany\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: monkeys dont deserve the right to vote\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: sluts should be beaten\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: I support the LGBTQ people\n",
      "Prediction: Not Hate Speech\n",
      "\n",
      "Text: Some cultures dont deserve to live\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: They were dancing on the rooftops after 9/11\n",
      "Prediction: Hate Speech\n",
      "\n",
      "Text: immigrants bring diseases into the country\n",
      "Prediction: Hate Speech\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Set device (GPU if available)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)  # Move model to GPU\n",
    "\n",
    "def predict_hate_speech(texts, batch_size=32):\n",
    "    \"\"\"Efficiently tokenize and predict labels in batches.\"\"\"\n",
    "    all_preds = []\n",
    "    model.eval()  # Ensure model is in evaluation mode\n",
    "\n",
    "    for i in range(0, len(texts), batch_size):\n",
    "        batch_texts = texts[i : i + batch_size]\n",
    "\n",
    "        # Tokenize the batch\n",
    "        inputs = tokenizer(batch_texts, return_tensors=\"pt\", padding=True, truncation=True, max_length=128)\n",
    "        inputs = {key: val.to(device) for key, val in inputs.items()}  # Move to GPU\n",
    "\n",
    "        with torch.no_grad():\n",
    "            outputs = model(**inputs)\n",
    "\n",
    "        # Get predicted labels (0 = Not Hate Speech, 1 = Hate Speech)\n",
    "        logits = outputs.logits\n",
    "        batch_preds = torch.argmax(logits, dim=-1).cpu().tolist()  # Move to CPU\n",
    "        all_preds.extend(batch_preds)\n",
    "\n",
    "    return all_preds\n",
    "\n",
    "# Example sentences\n",
    "samples = [\n",
    " \" i ve seen afro americans who look and undoubtedly act like him\",\n",
    "\"hyper text transfer protocol www occultopedia com f feral children http www long lands ac uk chimp la http www occultopedia com wild peter him aragon again every time i will see filthy comment about georgia without providing any facts i will respond\",\n",
    "\"i will respond until armenian scum will stop\",\n",
    "\"the reason large white families were encouraged those days what a slut to build up the population of white germans which were ravaged by the jewish attacks at them during and in hyperinflation\",\n",
    "\"i see a nation where i can walk out my front door and not feel like a minority in my own country\",\n",
    "\"where a white woman can go about in public and not fear assault by mud,\"\n",
    "\"no i see that as giving up\",\n",
    "\"i hope one day to proudly say again that i am an american when whatever take this country back\",\n",
    "\"okay i have been through some other threads on this section about nice and watched a few videos on you there\",\n",
    "\"it has obviously been misunderstood and i would have been keen to learn more\",\n",
    "\"i retract always because an animal lacks the skills to properly raise a half human child\",\n",
    "\"i apologize for my ignorance\",\n",
    "'I have no empathy for the palestinians, they deserve it',\n",
    "'Justice was done in 1940s germany',\n",
    "    'monkeys dont deserve the right to vote',\n",
    "    'sluts should be beaten', \n",
    "    'I support the LGBTQ people', \n",
    "    'Some cultures dont deserve to live',\n",
    "    'They were dancing on the rooftops after 9/11', \n",
    "    'immigrants bring diseases into the country'\n",
    "]\n",
    "\n",
    "# Run predictions\n",
    "predictions = predict_hate_speech(samples)\n",
    "\n",
    "# Print results\n",
    "for text, pred in zip(samples, predictions):\n",
    "    label = \"Hate Speech\" if pred == 1 else \"Not Hate Speech\"\n",
    "    print(f\"Text: {text}\\nPrediction: {label}\\n\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4622aa93-fa01-4955-97eb-3a67de6483b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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