DBP-CAT.ipynb

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       "Generating train split:   0%|          | 0/560000 [00:00<?, ? examples/s]"
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       "Generating test split:   0%|          | 0/70000 [00:00<?, ? examples/s]"
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     "text": [
      "Vocabulary size: 50002\n"
     ]
    },
    {
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     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:591: 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"
     ]
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     "text": [
      "Training...\n"
     ]
    },
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     "text": [
      "100%|██████████| 8750/8750 [1:04:24<00:00,  2.26it/s]\n"
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    {
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     "text": [
      "Epoch 1/5 Loss: 0.12996144127249717\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 8750/8750 [1:04:21<00:00,  2.27it/s]\n"
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      "Epoch 2/5 Loss: 0.06815697464145987\n"
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      "100%|██████████| 8750/8750 [1:04:20<00:00,  2.27it/s]\n"
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      "Epoch 3/5 Loss: 0.05200324921938591\n"
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     "text": [
      "100%|██████████| 8750/8750 [1:04:21<00:00,  2.27it/s]\n"
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      "Epoch 4/5 Loss: 0.04161729629899242\n"
     ]
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    {
     "name": "stderr",
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     "text": [
      "100%|██████████| 8750/8750 [1:04:21<00:00,  2.27it/s]\n"
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      "Epoch 5/5 Loss: 0.034360936810911104\n",
      "Evaluating...\n"
     ]
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     "text": [
      "100%|██████████| 1094/1094 [02:36<00:00,  6.98it/s]"
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      "\n",
      "Accuracy: 0.9805\n",
      "F1 Score: 0.9805\n",
      "Precision: 0.9806\n",
      "Recall: 0.9805\n"
     ]
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   "source": [
    "import os\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix\n",
    "from datasets import load_dataset\n",
    "from torch.utils.data import DataLoader\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from transformers import AdamW, get_scheduler\n",
    "import string  # Ensure string is imported\n",
    "\n",
    "# Set device\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "# Load DBpedia dataset\n",
    "dataset = load_dataset('dbpedia_14')\n",
    "\n",
    "# Prepare train/test datasets\n",
    "train_dataset = dataset['train']\n",
    "test_dataset = dataset['test']\n",
    "\n",
    "# Tokenizer: Build Vocabulary\n",
    "def basic_tokenizer(text):\n",
    "    return text.lower().translate(str.maketrans(\"\", \"\", string.punctuation)).split()\n",
    "\n",
    "def build_vocab(dataset, max_vocab_size=50000):\n",
    "    from collections import Counter\n",
    "    counter = Counter()\n",
    "    for example in dataset:\n",
    "        # Combine title and content\n",
    "        text = example['title'] + \" \" + example['content']\n",
    "        tokens = basic_tokenizer(text)\n",
    "        counter.update(tokens)\n",
    "    most_common = counter.most_common(max_vocab_size)\n",
    "    vocab = {word: idx + 2 for idx, (word, _) in enumerate(most_common)}  # Reserve 0 for [PAD], 1 for [UNK]\n",
    "    vocab[\"[PAD]\"] = 0\n",
    "    vocab[\"[UNK]\"] = 1\n",
    "    return vocab\n",
    "\n",
    "vocab = build_vocab(train_dataset, max_vocab_size=50000)\n",
    "vocab_size = len(vocab)\n",
    "print(f\"Vocabulary size: {vocab_size}\")\n",
    "\n",
    "# Custom Tokenizer\n",
    "def custom_tokenizer(example, vocab, max_length=128):\n",
    "    text = example['title'] + \" \" + example['content']\n",
    "    tokens = basic_tokenizer(text)\n",
    "    token_ids = [vocab.get(token, vocab[\"[UNK]\"]) for token in tokens]\n",
    "    if len(token_ids) < max_length:\n",
    "        token_ids += [vocab[\"[PAD]\"]] * (max_length - len(token_ids))  # Padding\n",
    "    else:\n",
    "        token_ids = token_ids[:max_length]  # Truncate\n",
    "    return token_ids\n",
    "\n",
    "def tokenize_dataset(dataset, vocab, max_length=128):\n",
    "    tokenized_data = []\n",
    "    for example in dataset:\n",
    "        tokenized_data.append({\n",
    "            'input_ids': custom_tokenizer(example, vocab, max_length),\n",
    "            'labels': example['label']\n",
    "        })\n",
    "    return tokenized_data\n",
    "\n",
    "# Tokenize datasets\n",
    "tokenized_train = tokenize_dataset(train_dataset, vocab)\n",
    "tokenized_test = tokenize_dataset(test_dataset, vocab)\n",
    "\n",
    "# Convert to PyTorch Dataset\n",
    "class CustomDataset(torch.utils.data.Dataset):\n",
    "    def __init__(self, data):\n",
    "        self.data = data\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return {\n",
    "            'input_ids': torch.tensor(self.data[idx]['input_ids'], dtype=torch.long),\n",
    "            'labels': torch.tensor(self.data[idx]['labels'], dtype=torch.long)\n",
    "        }\n",
    "\n",
    "train_dataset = CustomDataset(tokenized_train)\n",
    "test_dataset = CustomDataset(tokenized_test)\n",
    "\n",
    "# DataLoaders\n",
    "batch_size = 64\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# Cyclic Attention Transformer\n",
    "class CyclicAttentionBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, dropout=0.1):\n",
    "        super(CyclicAttentionBlock, self).__init__()\n",
    "        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm = nn.LayerNorm(embed_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.gate = nn.Linear(embed_dim, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        attn_output, _ = self.attention(x, x, x)\n",
    "        x = self.norm(x + attn_output)\n",
    "        cyclic_term = torch.roll(attn_output, shifts=1, dims=0)\n",
    "        g = torch.sigmoid(self.gate(attn_output))\n",
    "        x = self.norm(x + g * cyclic_term)\n",
    "        return self.dropout(x)\n",
    "\n",
    "class CombinedTransformerBlock(nn.Module):\n",
    "    def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1):\n",
    "        super(CombinedTransformerBlock, self).__init__()\n",
    "        self.attention1 = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm1 = nn.LayerNorm(embed_dim)\n",
    "        self.cyclic_attention = CyclicAttentionBlock(embed_dim, num_heads, dropout)\n",
    "        self.attention2 = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout)\n",
    "        self.norm2 = nn.LayerNorm(embed_dim)\n",
    "        self.ff = nn.Sequential(\n",
    "            nn.Linear(embed_dim, ff_dim),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(ff_dim, embed_dim)\n",
    "        )\n",
    "        self.norm3 = nn.LayerNorm(embed_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "\n",
    "    def forward(self, x):\n",
    "        attn_output, _ = self.attention1(x, x, x)\n",
    "        x = self.norm1(x + attn_output)\n",
    "        x = self.cyclic_attention(x)\n",
    "        attn_output, _ = self.attention2(x, x, x)\n",
    "        x = self.norm2(x + attn_output)\n",
    "        ff_output = self.ff(x)\n",
    "        x = self.norm3(x + self.dropout(ff_output))\n",
    "        return x\n",
    "\n",
    "class CustomTransformer(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_dim, num_heads, ff_dim, num_classes, num_layers):\n",
    "        super(CustomTransformer, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_dim)\n",
    "        self.layers = nn.ModuleList([\n",
    "            CombinedTransformerBlock(embed_dim, num_heads, ff_dim) for _ in range(num_layers)\n",
    "        ])\n",
    "        self.global_pooling = nn.AdaptiveAvgPool1d(1)\n",
    "        self.fc = nn.Linear(embed_dim, num_classes)\n",
    "\n",
    "    def forward(self, input_ids):\n",
    "        x = self.embedding(input_ids)\n",
    "        x = x.permute(1, 0, 2)\n",
    "        for layer in self.layers:\n",
    "            x = layer(x)\n",
    "        x = x.permute(1, 2, 0)\n",
    "        x = self.global_pooling(x).squeeze(-1)\n",
    "        logits = self.fc(x)\n",
    "        return logits\n",
    "\n",
    "# Model Initialization\n",
    "embed_dim = 1024\n",
    "num_heads = 8\n",
    "ff_dim = 2048\n",
    "num_classes = 14  # DBpedia has 14 classes\n",
    "num_layers = 3\n",
    "epochs = 5\n",
    "\n",
    "model = CustomTransformer(vocab_size, embed_dim, num_heads, ff_dim, num_classes, num_layers).to(device)\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
    "\n",
    "# Training Loop\n",
    "print(\"Training...\")\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for batch in tqdm(train_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        optimizer.zero_grad()\n",
    "        logits = model(input_ids)\n",
    "        loss = F.cross_entropy(logits, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        total_loss += loss.item()\n",
    "    print(f\"Epoch {epoch + 1}/{epochs} Loss: {total_loss / len(train_loader)}\")\n",
    "\n",
    "# Evaluation Loop\n",
    "print(\"Evaluating...\")\n",
    "model.eval()\n",
    "all_logits = []\n",
    "all_labels = []\n",
    "with torch.no_grad():\n",
    "    for batch in tqdm(test_loader):\n",
    "        input_ids = batch['input_ids'].to(device)\n",
    "        labels = batch['labels'].to(device)\n",
    "        logits = model(input_ids)\n",
    "        all_logits.append(logits.cpu().numpy())\n",
    "        all_labels.append(labels.cpu().numpy())\n",
    "\n",
    "all_logits = np.concatenate(all_logits, axis=0)\n",
    "all_labels = np.concatenate(all_labels, axis=0)\n",
    "\n",
    "# Metrics\n",
    "predictions = np.argmax(all_logits, axis=1)\n",
    "accuracy = accuracy_score(all_labels, predictions)\n",
    "f1 = f1_score(all_labels, predictions, average=\"weighted\")\n",
    "precision = precision_score(all_labels, predictions, average=\"weighted\")\n",
    "recall = recall_score(all_labels, predictions, average=\"weighted\")\n",
    "\n",
    "print(f\"\\nAccuracy: {accuracy:.4f}\")\n",
    "print(f\"F1 Score: {f1:.4f}\")\n",
    "print(f\"Precision: {precision:.4f}\")\n",
    "print(f\"Recall: {recall:.4f}\")\n"
   ]
  }
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