{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "initial_id",
    "outputId": "85dee483-9370-4e16-ecb1-1c2d545ee1fb",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:26:10.771980Z",
     "start_time": "2024-12-24T16:26:10.767703Z"
    }
   },
   "source": [
    "\n",
    "!pip install geopy > delete.txt\n",
    "!pip install datasets > delete.txt\n",
    "!pip install torch torchvision datasets > delete.txt\n",
    "!pip install huggingface_hub > delete.txt\n",
    "!pip install pyhocon > delete.txt\n",
    "!pip install transformers > delete.txt\n",
    "!pip install gensim > delete.txt\n",
    "!rm delete.txt\n"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n!pip install geopy > delete.txt\\n!pip install datasets > delete.txt\\n!pip install torch torchvision datasets > delete.txt\\n!pip install huggingface_hub > delete.txt\\n!pip install pyhocon > delete.txt\\n!pip install transformers > delete.txt\\n!pip install gensim > delete.txt\\n!rm delete.txt\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b0a77c981c32a0c8",
    "outputId": "fe03df52-1418-4034-8124-3bf9030ed5d7",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:26:10.813215Z",
     "start_time": "2024-12-24T16:26:10.810118Z"
    }
   },
   "cell_type": "code",
   "source": "!huggingface-cli login",
   "id": "b0a77c981c32a0c8",
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "colab": {
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      "b4353c16501c4c4fbedfdd018a99c899"
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    },
    "id": "a4aa3b759defc904",
    "outputId": "b1868c23-e675-41db-aa26-5eed9de60d9f",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:26:14.452521Z",
     "start_time": "2024-12-24T16:26:10.866207Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset_train = load_dataset(\"CISProject/FOX_NBC\", split=\"train\")\n",
    "\n",
    "# MODIFY IT TO THE TEST DATASET PATH\n",
    "dataset_test = load_dataset(\"CISProject/FOX_NBC\", split=\"test\")"
   ],
   "id": "a4aa3b759defc904",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 86,
     "referenced_widgets": [
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      "737e4582bd6b469dafd5fe0fd3b8d0bc",
      "0c9cc3d477a24fa89d1485ffb4f50bfd"
     ]
    },
    "id": "ce6e6b982e22e9fe",
    "outputId": "f38ef6b3-35ac-41dc-a8ae-f0dd28b1f84d",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:26:16.191425Z",
     "start_time": "2024-12-24T16:26:14.463529Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import torch\n",
    "import re\n",
    "from transformers import BertTokenizer\n",
    "from transformers import RobertaTokenizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from gensim.models import KeyedVectors\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "def preprocess_data(data,\n",
    "                    mode=\"train\",\n",
    "                    vectorizer=None,\n",
    "                    w2v_model=None,\n",
    "                    max_features=4096,\n",
    "                    max_seq_length=128,\n",
    "                    num_proc=4):\n",
    "    if w2v_model is None:\n",
    "        raise ValueError(\"w2v_model must be provided for Word2Vec embeddings.\")\n",
    "\n",
    "    # tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
    "    tokenizer = RobertaTokenizer.from_pretrained(\"roberta-base\")\n",
    "    # 1. Clean text once\n",
    "    def clean_text(examples):\n",
    "        import re\n",
    "        cleaned = []\n",
    "        for text in examples[\"title\"]:\n",
    "            text = text.lower()\n",
    "            text = re.sub(r'[^\\w\\s]', '', text)\n",
    "            text = text.strip()\n",
    "            cleaned.append(text)\n",
    "        return {\"clean_title\": cleaned}\n",
    "\n",
    "    data = data.map(clean_text, batched=True, num_proc=num_proc)\n",
    "\n",
    "    # 2. Fit CountVectorizer on training data if needed\n",
    "    if mode == \"train\" and vectorizer is None:\n",
    "        # Collect all cleaned titles to fit\n",
    "        all_titles = data[\"clean_title\"]\n",
    "        #vectorizer = CountVectorizer(max_features=max_features, ngram_range=(1,2))\n",
    "        vectorizer = TfidfVectorizer(max_features=max_features)\n",
    "        vectorizer.fit(all_titles)\n",
    "        print(\"vectorizer fitted on training data.\")\n",
    "\n",
    "    # 3. Transform titles with vectorizer once\n",
    "    def vectorize_batch(examples):\n",
    "        import numpy as np\n",
    "        freq = vectorizer.transform(examples[\"clean_title\"]).toarray().astype(np.float32)\n",
    "        return {\"freq_inputs\": freq}\n",
    "\n",
    "    data = data.map(vectorize_batch, batched=True, num_proc=num_proc)\n",
    "\n",
    "    # 4. Tokenize with BERT once\n",
    "    def tokenize_batch(examples):\n",
    "        tokenized = tokenizer(\n",
    "            examples[\"title\"],\n",
    "            padding=\"max_length\",\n",
    "            truncation=True,\n",
    "            max_length=max_seq_length\n",
    "        )\n",
    "        return {\n",
    "            \"input_ids\": tokenized[\"input_ids\"],\n",
    "            \"attention_mask\": tokenized[\"attention_mask\"]\n",
    "        }\n",
    "\n",
    "    data = data.map(tokenize_batch, batched=True, num_proc=num_proc)\n",
    "\n",
    "    # 5. Convert titles into tokens for W2V\n",
    "    def split_tokens(examples):\n",
    "        tokens_list = [t.split() for t in examples[\"clean_title\"]]\n",
    "        return {\"tokens\": tokens_list}\n",
    "\n",
    "    data = data.map(split_tokens, batched=True, num_proc=num_proc)\n",
    "\n",
    "    # Build an embedding dictionary for all unique tokens (do this once before embedding map)\n",
    "    unique_tokens = set()\n",
    "    for tokens in data[\"tokens\"]:\n",
    "        unique_tokens.update(tokens)\n",
    "\n",
    "    embedding_dim = w2v_model.vector_size\n",
    "    embedding_dict = {}\n",
    "    for tk in unique_tokens:\n",
    "        if tk in w2v_model:\n",
    "            embedding_dict[tk] = w2v_model[tk].astype(np.float32)\n",
    "        else:\n",
    "            embedding_dict[tk] = np.zeros((embedding_dim,), dtype=np.float32)\n",
    "\n",
    "    def w2v_embedding_batch(examples):\n",
    "        import numpy as np\n",
    "        batch_w2v = []\n",
    "        for tokens in examples[\"tokens\"]:\n",
    "            vectors = [embedding_dict[tk] for tk in tokens[:max_seq_length]]\n",
    "            if len(vectors) < max_seq_length:\n",
    "                vectors += [np.zeros((embedding_dim,), dtype=np.float32)] * (max_seq_length - len(vectors))\n",
    "            batch_w2v.append(vectors)\n",
    "        return {\"pos_inputs\": batch_w2v}\n",
    "\n",
    "\n",
    "    data = data.map(w2v_embedding_batch, batched=True, batch_size=32, num_proc=num_proc)\n",
    "\n",
    "    # 7. Create labels\n",
    "    def make_labels(examples):\n",
    "        labels = examples[\"labels\"]\n",
    "        return {\"labels\": labels}\n",
    "\n",
    "    data = data.map(make_labels, batched=True, num_proc=num_proc)\n",
    "\n",
    "    # Convert freq_inputs and pos_inputs to torch tensors in a final map step\n",
    "    def to_tensors(examples):\n",
    "        import torch\n",
    "\n",
    "        freq_inputs = torch.tensor(examples[\"freq_inputs\"], dtype=torch.float32)\n",
    "        input_ids = torch.tensor(examples[\"input_ids\"])\n",
    "        attention_mask = torch.tensor(examples[\"attention_mask\"])\n",
    "        pos_inputs = torch.tensor(examples[\"pos_inputs\"], dtype=torch.float32)\n",
    "        labels = torch.tensor(examples[\"labels\"],dtype=torch.long)\n",
    "\n",
    "        # seq_inputs shape: (batch_size, 2, seq_len)\n",
    "        seq_inputs = torch.stack([input_ids, attention_mask], dim=1)\n",
    "\n",
    "        return {\n",
    "            \"freq_inputs\": freq_inputs,\n",
    "            \"seq_inputs\": seq_inputs,\n",
    "            \"pos_inputs\": pos_inputs,\n",
    "            \"labels\": labels\n",
    "        }\n",
    "\n",
    "    # Apply final conversion to tensor\n",
    "    processed_data = data.map(to_tensors, batched=True, num_proc=num_proc)\n",
    "\n",
    "    return processed_data, vectorizer\n"
   ],
   "id": "ce6e6b982e22e9fe",
   "outputs": [],
   "execution_count": 4
  },
  {
   "metadata": {
    "colab": {
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     "height": 226,
     "referenced_widgets": [
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     ]
    },
    "id": "b605d3b4f5ff547a",
    "outputId": "f365a98e-c181-4754-9fac-77aa1e8639db",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:27:18.269951Z",
     "start_time": "2024-12-24T16:26:16.194928Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from gensim.models import KeyedVectors\n",
    "w2v_model = KeyedVectors.load_word2vec_format(\"./GoogleNews-vectors-negative300.bin\", binary=True) #PATH TO PRETRAINED WORD2VEC MODEL\n",
    "\n",
    "dataset_train,vectorizer = preprocess_data(\n",
    "    data=dataset_train,\n",
    "    mode=\"train\",\n",
    "    w2v_model=w2v_model,\n",
    "    max_features=8192,\n",
    "    max_seq_length=128\n",
    ")\n",
    "\n",
    "dataset_test, _ = preprocess_data(\n",
    "    data=dataset_test,\n",
    "    mode=\"test\",\n",
    "    vectorizer=vectorizer,\n",
    "    w2v_model=w2v_model,\n",
    "    max_features=8192,\n",
    "    max_seq_length=128\n",
    ")"
   ],
   "id": "b605d3b4f5ff547a",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vectorizer fitted on training data.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "0aec436603c54b82b962cb31750a5921"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
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      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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       "model_id": "2061954a6f7e47e08803c4c54e852571"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/3044 [00:00<?, ? examples/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/plain": [
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      ],
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     "data": {
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    {
     "data": {
      "text/plain": [
       "Map (num_proc=4):   0%|          | 0/3044 [00:00<?, ? examples/s]"
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     "end_time": "2024-12-24T16:28:32.064840Z",
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   "cell_type": "code",
   "source": [
    "# TODO: import all packages necessary for your custom model\n",
    "import pandas as pd\n",
    "import os\n",
    "from torch.utils.data import DataLoader\n",
    "from transformers import PreTrainedModel, PretrainedConfig, AutoConfig, AutoModel\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from transformers import RobertaModel, RobertaConfig,RobertaForSequenceClassification, BertModel\n",
    "from model.network import Classifier\n",
    "from model.frequential import FreqNetwork\n",
    "from model.sequential import SeqNetwork\n",
    "from model.positional import PosNetwork\n",
    "\n",
    "class CustomConfig(PretrainedConfig):\n",
    "    model_type = \"headlineclassifier\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        base_exp_dir=\"./exp/fox_nbc/\",\n",
    "        # dataset={\"data_dir\": \"./data/CASE_NAME/data.csv\", \"transform\": True},\n",
    "        train={\n",
    "            \"learning_rate\": 2e-5,\n",
    "            \"learning_rate_alpha\": 0.05,\n",
    "            \"end_iter\": 10,\n",
    "            \"batch_size\": 32,\n",
    "            \"warm_up_end\": 2,\n",
    "            \"anneal_end\": 5,\n",
    "            \"save_freq\": 1,\n",
    "            \"val_freq\": 1,\n",
    "        },\n",
    "        model={\n",
    "            \"freq\": {\n",
    "                \"tfidf_input_dim\": 8145,\n",
    "                \"tfidf_output_dim\": 128,\n",
    "                \"tfidf_hidden_dim\": 512,\n",
    "                \"n_layers\": 2,\n",
    "                \"skip_in\": [80],\n",
    "                \"weight_norm\": True,\n",
    "            },\n",
    "            \"pos\": {\n",
    "                \"input_dim\": 300,\n",
    "                \"output_dim\": 128,\n",
    "                \"hidden_dim\": 256,\n",
    "                \"n_layers\": 2,\n",
    "                \"skip_in\": [80],\n",
    "                \"weight_norm\": True,\n",
    "            },\n",
    "            \"cls\": {\n",
    "                \"combined_input\": 1024, #1024\n",
    "                \"combined_dim\": 128,\n",
    "                \"num_classes\": 2,\n",
    "                \"n_layers\": 2,\n",
    "                \"skip_in\": [80],\n",
    "                \"weight_norm\": True,\n",
    "            },\n",
    "        },\n",
    "        **kwargs,\n",
    "    ):\n",
    "        super().__init__(**kwargs)\n",
    "\n",
    "        self.base_exp_dir = base_exp_dir\n",
    "        # self.dataset = dataset\n",
    "        self.train = train\n",
    "        self.model = model\n",
    "\n",
    "# TODO: define all parameters needed for your model, as well as calling the model itself\n",
    "class CustomModel(PreTrainedModel):\n",
    "    config_class = CustomConfig\n",
    "\n",
    "    def __init__(self, config):\n",
    "        super().__init__(config)\n",
    "        self.conf = config\n",
    "        self.freq = FreqNetwork(**self.conf.model[\"freq\"])\n",
    "        self.pos = PosNetwork(**self.conf.model[\"pos\"])\n",
    "        self.cls = Classifier(**self.conf.model[\"cls\"])\n",
    "        self.fc = nn.Linear(self.conf.model[\"cls\"][\"combined_input\"],2)\n",
    "        self.seq = RobertaModel.from_pretrained(\"roberta-base\")\n",
    "        # self.seq = BertModel.from_pretrained(\"bert-base-uncased\")\n",
    "        #for param in self.roberta.parameters():\n",
    "        #    param.requires_grad = False\n",
    "        self.dropout = nn.Dropout(0.2)\n",
    "\n",
    "    def forward(self, x):\n",
    "        freq_inputs = x[\"freq_inputs\"]\n",
    "        seq_inputs = x[\"seq_inputs\"]\n",
    "        pos_inputs = x[\"pos_inputs\"]\n",
    "        seq_feature = self.seq(\n",
    "            input_ids=seq_inputs[:,0,:],\n",
    "            attention_mask=seq_inputs[:,1,:]\n",
    "        ).pooler_output # last_hidden_state[:, 0, :]\n",
    "        freq_feature = self.freq(freq_inputs) # Shape: (batch_size, 128)\n",
    "\n",
    "        pos_feature = self.pos(pos_inputs) #Shape: (batch_size, 128)\n",
    "        inputs = torch.cat((seq_feature, freq_feature, pos_feature), dim=1)  # Shape: (batch_size, 384)\n",
    "        # inputs = torch.cat((seq_feature, freq_feature), dim=1)  # Shape: (batch_size,256)\n",
    "        # inputs = seq_feature\n",
    "\n",
    "        x = inputs\n",
    "        x = self.dropout(x)\n",
    "        outputs = self.fc(x)\n",
    "\n",
    "        return outputs\n",
    "\n",
    "    def save_model(self, save_path):\n",
    "        \"\"\"Save the model locally using the Hugging Face format.\"\"\"\n",
    "        self.save_pretrained(save_path)\n",
    "\n",
    "    def push_model(self, repo_name):\n",
    "        \"\"\"Push the model to the Hugging Face Hub.\"\"\"\n",
    "        self.push_to_hub(repo_name)"
   ],
   "id": "9266d67887120863",
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-12-24T16:28:35.657792Z",
     "start_time": "2024-12-24T16:28:35.392033Z"
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   },
   "cell_type": "code",
   "source": [
    "AutoConfig.register(\"headlineclassifier\", CustomConfig)\n",
    "AutoModel.register(CustomConfig, CustomModel)\n",
    "config = CustomConfig()\n",
    "model = CustomModel(config)"
   ],
   "id": "77b94c012f4fae3a",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swall\\anaconda3\\envs\\newsCLS\\Lib\\site-packages\\torch\\nn\\utils\\weight_norm.py:143: FutureWarning: `torch.nn.utils.weight_norm` is deprecated in favor of `torch.nn.utils.parametrizations.weight_norm`.\n",
      "  WeightNorm.apply(module, name, dim)\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 492,
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      "a19e3145844d4ee79dacb1582ec26f77",
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      "8f3bd4440a04439081e1274117edd230",
      "fec45a75aede4ea1a18adbd486a08b83",
      "b1a9e3e423b44888b4084656c8e1d454",
      "47646b605d0740af8ad11d25077629ec",
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      "af645d045ecc43aaaba1a65e7fb23b6a"
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    "ExecuteTime": {
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   },
   "cell_type": "code",
   "source": [
    "from transformers import AutoModel, AutoConfig\n",
    "config = AutoConfig.from_pretrained(\"CISProject/News-Headline-Classifier-Notebook\")\n",
    "model = AutoModel.from_pretrained(\"CISProject/News-Headline-Classifier-Notebook\",config = config)"
   ],
   "id": "b20d11caa1d25445",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/518M [00:00<?, ?B/s]"
      ],
      "application/vnd.jupyter.widget-view+json": {
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swall\\anaconda3\\envs\\newsCLS\\Lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\swall\\.cache\\huggingface\\hub\\models--CISProject--News-Headline-Classifier-Notebook. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n",
      "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-base and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
      "Some weights of the model checkpoint at CISProject/News-Headline-Classifier-Notebook were not used when initializing CustomModel: ['cls.lin0.parametrizations.weight.original0', 'cls.lin0.parametrizations.weight.original1', 'cls.lin1.parametrizations.weight.original0', 'cls.lin1.parametrizations.weight.original1', 'cls.lin2.parametrizations.weight.original0', 'cls.lin2.parametrizations.weight.original1', 'freq.lin0.parametrizations.weight.original0', 'freq.lin0.parametrizations.weight.original1', 'freq.lin1.parametrizations.weight.original0', 'freq.lin1.parametrizations.weight.original1', 'freq.lin2.parametrizations.weight.original0', 'freq.lin2.parametrizations.weight.original1', 'pos.lin0.parametrizations.weight.original0', 'pos.lin0.parametrizations.weight.original1', 'pos.lin1.parametrizations.weight.original0', 'pos.lin1.parametrizations.weight.original1', 'pos.lin2.parametrizations.weight.original0', 'pos.lin2.parametrizations.weight.original1']\n",
      "- This IS expected if you are initializing CustomModel 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 CustomModel 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 CustomModel were not initialized from the model checkpoint at CISProject/News-Headline-Classifier-Notebook and are newly initialized: ['cls.lin0.weight_g', 'cls.lin0.weight_v', 'cls.lin1.weight_g', 'cls.lin1.weight_v', 'cls.lin2.weight_g', 'cls.lin2.weight_v', 'freq.lin0.weight_g', 'freq.lin0.weight_v', 'freq.lin1.weight_g', 'freq.lin1.weight_v', 'freq.lin2.weight_g', 'freq.lin2.weight_v', 'pos.lin0.weight_g', 'pos.lin0.weight_v', 'pos.lin1.weight_g', 'pos.lin1.weight_v', 'pos.lin2.weight_g', 'pos.lin2.weight_v']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "id": "1d23cedfe1d79660",
    "ExecuteTime": {
     "end_time": "2024-12-24T16:29:36.873566Z",
     "start_time": "2024-12-24T16:29:23.549424Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "from tqdm import tqdm\n",
    "# Define a collate function to handle the batched data\n",
    "def collate_fn(batch):\n",
    "    freq_inputs = torch.stack([torch.tensor(item[\"freq_inputs\"]) for item in batch])\n",
    "    seq_inputs = torch.stack([torch.tensor(item[\"seq_inputs\"]) for item in batch])\n",
    "    pos_inputs = torch.stack([torch.tensor(item[\"pos_inputs\"]) for item in batch])\n",
    "    labels = torch.tensor([torch.tensor(item[\"labels\"],dtype=torch.long) for item in batch])\n",
    "    return {\"freq_inputs\": freq_inputs, \"seq_inputs\": seq_inputs, \"pos_inputs\": pos_inputs}, labels\n",
    "\n",
    "train_loader = DataLoader(dataset_train, batch_size=config.train[\"batch_size\"], shuffle=True,collate_fn=collate_fn)\n",
    "test_loader = DataLoader(dataset_test, batch_size=config.train[\"batch_size\"], shuffle=False,collate_fn=collate_fn)\n",
    "\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "\n",
    "criterion = torch.nn.CrossEntropyLoss()\n",
    "def evaluate_model(model, val_loader, criterion, device=\"cuda\"):\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    with torch.no_grad():\n",
    "        for batch_inputs, labels in tqdm(val_loader, desc=\"Testing\", leave=False):\n",
    "            freq_inputs = batch_inputs[\"freq_inputs\"].to(device)\n",
    "            seq_inputs = batch_inputs[\"seq_inputs\"].to(device)\n",
    "            pos_inputs = batch_inputs[\"pos_inputs\"].to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            preds = model({\"freq_inputs\": freq_inputs, \"seq_inputs\": seq_inputs, \"pos_inputs\": pos_inputs})\n",
    "            loss = criterion(preds, labels)\n",
    "            _, preds = torch.max(preds, dim=1)\n",
    "            # preds = (torch.sigmoid(preds) > 0.5).float()\n",
    "            val_loss += loss.item()\n",
    "            total += labels.size(0)\n",
    "            correct += (preds == labels).sum().item()\n",
    "            all_preds.extend(preds.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "\n",
    "    return accuracy_score(all_labels, all_preds), classification_report(all_labels, all_preds)\n",
    "\n",
    "\n",
    "accuracy, report = evaluate_model(model, test_loader, criterion)\n",
    "print(f\"Accuracy: {accuracy:.4f}\")\n",
    "print(report)"
   ],
   "id": "1d23cedfe1d79660",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "                                                        "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8988\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.88      0.89       356\n",
      "           1       0.90      0.91      0.91       405\n",
      "\n",
      "    accuracy                           0.90       761\n",
      "   macro avg       0.90      0.90      0.90       761\n",
      "weighted avg       0.90      0.90      0.90       761\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    }
   ],
   "execution_count": 12
  }
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