scratch model submitted

submission_scratch_notebook.ipynb

@@ -0,0 +1,174 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "025d2c7e",
+   "metadata": {},
+   "source": [
+    "# Bi-LSTM Submission\n",
+    "Generate predictions using the trained Bi-LSTM model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c33463c3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install -q transformers torch pandas numpy huggingface_hub"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8c71e09",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from transformers import AutoTokenizer\n",
+    "from huggingface_hub import hf_hub_download\n",
+    "\n",
+    "# Config\n",
+    "LABELS = [\"anger\", \"fear\", \"joy\", \"sadness\", \"surprise\"]\n",
+    "MAX_LEN = 100\n",
+    "BATCH_SIZE = 32\n",
+    "EMBED_DIM = 100\n",
+    "HIDDEN_DIM = 128\n",
+    "DROPOUT = 0.3\n",
+    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Paths\n",
+    "TEST_CSV = \"/kaggle/input/2025-sep-dl-gen-ai-project/test.csv\"\n",
+    "SUBMISSION_CSV = \"submission.csv\"\n",
+    "MODEL_FILE = \"model.pth\"\n",
+    "\n",
+    "# HF Repo (Replace with your repo ID from training notebook)\n",
+    "HF_REPO_ID = \"hrshlgunjal/emotion-classifier-bilstm\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c30485d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Download Model if needed\n",
+    "if not os.path.exists(MODEL_FILE):\n",
+    "    try:\n",
+    "        print(f\"Downloading model from {HF_REPO_ID}...\")\n",
+    "        model_path = hf_hub_download(repo_id=HF_REPO_ID, filename=\"pytorch_model.bin\")\n",
+    "        import shutil\n",
+    "        shutil.copy(model_path, MODEL_FILE)\n",
+    "        print(\"Model downloaded.\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"Could not download model: {e}\")\n",
+    "        print(\"Please ensure HF_REPO_ID is correct or upload 'model.pth' manually.\")\n",
+    "else:\n",
+    "    print(\"Model file found locally.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0ba4952",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class BiLSTM(nn.Module):\n",
+    "    def __init__(self, vocab_size):\n",
+    "        super().__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, EMBED_DIM)\n",
+    "        self.lstm = nn.LSTM(EMBED_DIM, HIDDEN_DIM, batch_first=True, bidirectional=True, dropout=DROPOUT, num_layers=2)\n",
+    "        self.fc = nn.Linear(HIDDEN_DIM * 2, len(LABELS))\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.embedding(x)\n",
+    "        _, (hidden, _) = self.lstm(x)\n",
+    "        x = torch.cat((hidden[-2], hidden[-1]), dim=1)\n",
+    "        return self.fc(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5594b26",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def predict():\n",
+    "    if not os.path.exists(TEST_CSV):\n",
+    "        print(\"Test data not found.\")\n",
+    "        return\n",
+    "\n",
+    "    # Load Data\n",
+    "    df = pd.read_csv(TEST_CSV)\n",
+    "    if \"text\" not in df.columns: df = df.rename(columns={\"comment_text\": \"text\"})\n",
+    "    \n",
+    "    # Tokenizer\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+    "    \n",
+    "    # Dataset\n",
+    "    class TestDS(Dataset):\n",
+    "        def __init__(self, df, tokenizer):\n",
+    "            self.texts = df['text'].tolist()\n",
+    "            self.tokenizer = tokenizer\n",
+    "        def __len__(self): return len(self.texts)\n",
+    "        def __getitem__(self, idx):\n",
+    "            enc = self.tokenizer(self.texts[idx], truncation=True, padding='max_length', max_length=MAX_LEN, return_tensors='pt')\n",
+    "            return enc['input_ids'].squeeze(0)\n",
+    "\n",
+    "    loader = DataLoader(TestDS(df, tokenizer), batch_size=BATCH_SIZE)\n",
+    "\n",
+    "    # Load Model\n",
+    "    model = BiLSTM(tokenizer.vocab_size).to(DEVICE)\n",
+    "    \n",
+    "    if os.path.exists(MODEL_FILE):\n",
+    "        model.load_state_dict(torch.load(MODEL_FILE, map_location=DEVICE))\n",
+    "        print(f\"Loaded weights from {MODEL_FILE}\")\n",
+    "    else:\n",
+    "        print(\"No model weights found! Predictions will be random.\")\n",
+    "            \n",
+    "    model.eval()\n",
+    "    \n",
+    "    # Inference\n",
+    "    all_preds = []\n",
+    "    print(\"Predicting...\")\n",
+    "    with torch.no_grad():\n",
+    "        for batch in loader:\n",
+    "            logits = model(batch.to(DEVICE))\n",
+    "            probs = torch.sigmoid(logits)\n",
+    "            all_preds.append(probs.cpu().numpy())\n",
+    "            \n",
+    "    all_preds = np.vstack(all_preds)\n",
+    "    \n",
+    "    # Convert to binary (0/1) as per submission requirement\n",
+    "    binary_preds = (all_preds >= 0.5).astype(int)\n",
+    "    \n",
+    "    # Create Submission\n",
+    "    submission = pd.DataFrame(binary_preds, columns=LABELS)\n",
+    "    submission['id'] = df['id']\n",
+    "    submission = submission[['id'] + LABELS]\n",
+    "    submission.to_csv(SUBMISSION_CSV, index=False)\n",
+    "    print(f\"Saved submission to {SUBMISSION_CSV}\")\n",
+    "    print(submission.head())\n",
+    "\n",
+    "predict()"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

training_scratch_notebook.ipynb

@@ -0,0 +1,2678 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8746fae6",
+   "metadata": {
+    "papermill": {
+     "duration": 0.004991,
+     "end_time": "2025-12-02T21:50:17.265155",
+     "exception": false,
+     "start_time": "2025-12-02T21:50:17.260164",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# Bi-LSTM Emotion Classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15aa58bc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-02T21:50:17.274278Z",
+     "iopub.status.busy": "2025-12-02T21:50:17.274012Z",
+     "iopub.status.idle": "2025-12-02T21:51:28.685152Z",
+     "shell.execute_reply": "2025-12-02T21:51:28.684384Z"
+    },
+    "papermill": {
+     "duration": 71.417599,
+     "end_time": "2025-12-02T21:51:28.686724",
+     "exception": false,
+     "start_time": "2025-12-02T21:50:17.269125",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q wandb huggingface_hub transformers torch scikit-learn pandas numpy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "228b7b20",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-02T21:51:28.768325Z",
+     "iopub.status.busy": "2025-12-02T21:51:28.768036Z",
+     "iopub.status.idle": "2025-12-02T21:51:31.795433Z",
+     "shell.execute_reply": "2025-12-02T21:51:31.794721Z"
+    },
+    "papermill": {
+     "duration": 3.048968,
+     "end_time": "2025-12-02T21:51:31.796586",
+     "exception": false,
+     "start_time": "2025-12-02T21:51:28.747618",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "from torch.optim import AdamW\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from transformers import AutoTokenizer\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import f1_score\n",
+    "import wandb\n",
+    "from huggingface_hub import login\n",
+    "from kaggle_secrets import UserSecretsClient\n",
+    "\n",
+    "# Secrets\n",
+    "user_secrets = UserSecretsClient()\n",
+    "wandb.login(key=user_secrets.get_secret(\"wandb_api_key\"))\n",
+    "login(token=user_secrets.get_secret(\"hf_api_key\"))\n",
+    "\n",
+    "# Config\n",
+    "PROJECT = \"emotion-classification-dl\"\n",
+    "RUN_NAME = \"simple-bilstm-scratch\"\n",
+    "MODEL_NAME = \"emotion-classifier-bilstm\"\n",
+    "LABELS = [\"anger\", \"fear\", \"joy\", \"sadness\", \"surprise\"]\n",
+    "MAX_LEN = 100\n",
+    "BATCH_SIZE = 32\n",
+    "EPOCHS = 10\n",
+    "LR = 1e-3\n",
+    "EMBED_DIM = 100\n",
+    "HIDDEN_DIM = 128\n",
+    "DROPOUT = 0.3\n",
+    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Seed\n",
+    "torch.manual_seed(42)\n",
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9052c10c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-02T21:51:46.423323Z",
+     "iopub.status.busy": "2025-12-02T21:51:46.423092Z",
+     "iopub.status.idle": "2025-12-02T21:51:46.428663Z",
+     "shell.execute_reply": "2025-12-02T21:51:46.428144Z"
+    },
+    "papermill": {
+     "duration": 0.026678,
+     "end_time": "2025-12-02T21:51:46.429799",
+     "exception": false,
+     "start_time": "2025-12-02T21:51:46.403121",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "class EmotionDS(Dataset):\n",
+    "    def __init__(self, df, tokenizer):\n",
+    "        self.texts = df['text'].tolist()\n",
+    "        self.labels = df[LABELS].values.astype(np.float32)\n",
+    "        self.tokenizer = tokenizer\n",
+    "\n",
+    "    def __len__(self): return len(self.texts)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        enc = self.tokenizer(self.texts[idx], truncation=True, padding='max_length', max_length=MAX_LEN, return_tensors='pt')\n",
+    "        return {\n",
+    "            'input_ids': enc['input_ids'].squeeze(0),\n",
+    "            'labels': torch.tensor(self.labels[idx])\n",
+    "        }\n",
+    "\n",
+    "class BiLSTM(nn.Module):\n",
+    "    def __init__(self, vocab_size):\n",
+    "        super().__init__()\n",
+    "        self.embedding = nn.Embedding(vocab_size, EMBED_DIM)\n",
+    "        self.lstm = nn.LSTM(EMBED_DIM, HIDDEN_DIM, batch_first=True, bidirectional=True, dropout=DROPOUT, num_layers=2)\n",
+    "        self.fc = nn.Linear(HIDDEN_DIM * 2, len(LABELS))\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        x = self.embedding(x)\n",
+    "        _, (hidden, _) = self.lstm(x)\n",
+    "        # Concat forward and backward hidden states from last layer\n",
+    "        x = torch.cat((hidden[-2], hidden[-1]), dim=1)\n",
+    "        return self.fc(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32a69a3d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-02T21:51:46.596265Z",
+     "iopub.status.busy": "2025-12-02T21:51:46.596031Z",
+     "iopub.status.idle": "2025-12-02T23:08:14.788585Z",
+     "shell.execute_reply": "2025-12-02T23:08:14.787581Z"
+    },
+    "papermill": {
+     "duration": 4588.214315,
+     "end_time": "2025-12-02T23:08:14.790036",
+     "exception": false,
+     "start_time": "2025-12-02T21:51:46.575721",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def train():\n",
+    "    # Data\n",
+    "    df = pd.read_csv(\"/kaggle/input/2025-sep-dl-gen-ai-project/train.csv\")\n",
+    "    if \"text\" not in df.columns: df = df.rename(columns={\"comment_text\": \"text\"}) # Handle column name\n",
+    "    \n",
+    "    train_df, val_df = train_test_split(df, test_size=0.2, random_state=42)\n",
+    "    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-uncased\")\n",
+    "    \n",
+    "    train_loader = DataLoader(EmotionDS(train_df, tokenizer), batch_size=BATCH_SIZE, shuffle=True)\n",
+    "    val_loader = DataLoader(EmotionDS(val_df, tokenizer), batch_size=BATCH_SIZE)\n",
+    "    \n",
+    "    # Model\n",
+    "    model = BiLSTM(tokenizer.vocab_size).to(DEVICE)\n",
+    "    optimizer = AdamW(model.parameters(), lr=LR)\n",
+    "    criterion = nn.BCEWithLogitsLoss()\n",
+    "    \n",
+    "    # WandB\n",
+    "    wandb.init(project=PROJECT, name=RUN_NAME)\n",
+    "    \n",
+    "    best_f1 = 0\n",
+    "    for epoch in range(EPOCHS):\n",
+    "        model.train()\n",
+    "        train_loss = 0\n",
+    "        for batch in train_loader:\n",
+    "            optimizer.zero_grad()\n",
+    "            loss = criterion(model(batch['input_ids'].to(DEVICE)), batch['labels'].to(DEVICE))\n",
+    "            loss.backward()\n",
+    "            optimizer.step()\n",
+    "            train_loss += loss.item()\n",
+    "            \n",
+    "        # Validation\n",
+    "        model.eval()\n",
+    "        preds, targets = [], []\n",
+    "        with torch.no_grad():\n",
+    "            for batch in val_loader:\n",
+    "                logits = model(batch['input_ids'].to(DEVICE))\n",
+    "                preds.append(torch.sigmoid(logits).cpu().numpy())\n",
+    "                targets.append(batch['labels'].cpu().numpy())\n",
+    "        \n",
+    "        preds = np.vstack(preds)\n",
+    "        targets = np.vstack(targets)\n",
+    "        f1 = f1_score(targets, (preds >= 0.5).astype(int), average='macro')\n",
+    "        \n",
+    "        print(f\"Epoch {epoch+1}: Loss {train_loss/len(train_loader):.4f}, Val F1 {f1:.4f}\")\n",
+    "        wandb.log({\"loss\": train_loss/len(train_loader), \"val_f1\": f1})\n",
+    "        \n",
+    "        if f1 > best_f1:\n",
+    "            best_f1 = f1\n",
+    "            torch.save(model.state_dict(), \"model.pth\")\n",
+    "            \n",
+    "    wandb.finish()\n",
+    "    return best_f1\n",
+    "\n",
+    "if os.path.exists(\"/kaggle/input/2025-sep-dl-gen-ai-project/train.csv\"):\n",
+    "    train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5212836",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2025-12-02T23:08:15.471904Z",
+     "iopub.status.busy": "2025-12-02T23:08:15.471612Z",
+     "iopub.status.idle": "2025-12-02T23:08:15.485244Z",
+     "shell.execute_reply": "2025-12-02T23:08:15.484621Z"
+    },
+    "papermill": {
+     "duration": 0.038319,
+     "end_time": "2025-12-02T23:08:15.486251",
+     "exception": false,
+     "start_time": "2025-12-02T23:08:15.447932",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Upload\n",
+    "if os.path.exists(\"model.pth\"):\n",
+    "    api = HfApi()\n",
+    "    repo_id = f\"{api.whoami()['name']}/{MODEL_NAME}\"\n",
+    "    create_repo(repo_id, exist_ok=True)\n",
+    "    api.upload_file(path_or_fileobj=\"model.pth\", path_in_repo=\"pytorch_model.bin\", repo_id=repo_id)\n",
+    "    print(f\"Uploaded to https://huggingface.co/{repo_id}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kaggle": {
+   "accelerator": "nvidiaTeslaT4",
+   "dataSources": [
+    {
+     "databundleVersionId": 13800781,
+     "sourceId": 115439,
+     "sourceType": "competition"
+    }
+   ],
+   "dockerImageVersionId": 31193,
+   "isGpuEnabled": true,
+   "isInternetEnabled": true,
+   "language": "python",
+   "sourceType": "notebook"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.13"
+  },
+  "papermill": {
+   "default_parameters": {},
+   "duration": 4685.493997,
+   "end_time": "2025-12-02T23:08:19.013597",
+   "environment_variables": {},
+   "exception": null,
+   "input_path": "__notebook__.ipynb",
+   "output_path": "__notebook__.ipynb",
+   "parameters": {},
+   "start_time": "2025-12-02T21:50:13.519600",
+   "version": "2.6.0"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
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+       "_view_module_version": "2.0.0",
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+       "description": "",
+       "description_allow_html": false,
+       "layout": "IPY_MODEL_ff14fb3db5d24ad99736c82ac409c4a2",
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+       "style": "IPY_MODEL_029db6685a984cacacc3c434eeecd35e",
+       "tabbable": null,
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+       "value": "pytorch_model.bin: 100%"
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