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model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f46834fd6aba4676d4232a308276e2913eeaf4f155d2cc739a6218f7255d2f05
+size 7415509

threshold-detect.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "caa2786c-27ba-484a-9ff4-da4e378ef019",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ujson as json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b1f04aee-ba66-4e67-9183-36cf2534c08f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testd = json.load(open('filtered2/test.json'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "a663864b-2795-4aac-9817-8c6d8c31c4e5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16000"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(testd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "65be569c-13bb-4b61-bfd1-5be33428409b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from joblib import load\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "from transformers import BertModel, BertConfig\n",
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "\n",
+    "classes = [\n",
+    "    \"Automotive\",\n",
+    "    \"Business\",\n",
+    "    \"Crime\",\n",
+    "    \"Economics\",\n",
+    "    \"Entertainment\",\n",
+    "    \"Finance\",\n",
+    "    \"Financial Crime\",\n",
+    "    \"General\",\n",
+    "    \"Health\",\n",
+    "    \"Lifestyle\",\n",
+    "    \"Politics\",\n",
+    "    \"Science\",\n",
+    "    \"Sports\",\n",
+    "    \"Tech\",\n",
+    "    \"Travel\",\n",
+    "    \"Weather\",\n",
+    "]\n",
+    "mlb = MultiLabelBinarizer(classes=classes)\n",
+    "mlb.fit([[]])\n",
+    "\n",
+    "NUM_LABELS = len(classes)\n",
+    "\n",
+    "\n",
+    "# class SimpleMLP(nn.Module):\n",
+    "#     def __init__(self, input_dim=1024, num_labels=NUM_LABELS):\n",
+    "#         super().__init__()\n",
+    "#         self.net = nn.Sequential(\n",
+    "#             nn.Linear(input_dim, 1024),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Dropout(0.1),\n",
+    "#             nn.Linear(1024, 512),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Linear(512, 512),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Linear(512, 512),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Linear(512, 256),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Linear(256, 128),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.Linear(128, 64),\n",
+    "#             nn.ReLU(),\n",
+    "#             nn.LayerNorm(64),\n",
+    "#             nn.Linear(64, num_labels),\n",
+    "#         )\n",
+    "\n",
+    "#     def forward(self, x):\n",
+    "#         return self.net(x)  # logits\n",
+    "\n",
+    "\n",
+    "# THEME_MODEL = SimpleMLP(num_labels=len(mlb.classes_))\n",
+    "# device = 0 if torch.cuda.is_available() else -1\n",
+    "# if device == 0:\n",
+    "#     THEME_MODEL.load_state_dict(torch.load('qwen_embedding_theme/mlp_model.pth'))\n",
+    "# else:\n",
+    "#     THEME_MODEL.load_state_dict(torch.load(\n",
+    "#         'qwen_embedding_theme/mlp_model.pth', map_location=torch.device('cpu')))\n",
+    "# THEME_MODEL.eval()\n",
+    "# if torch.cuda.is_available():\n",
+    "#     THEME_MODEL.to(device)\n",
+    "\n",
+    "# SCALER = load('qwen_embedding_theme/scaler.joblib')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "c895eac1-6b42-4b8c-ab26-3a79b507e8a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "classes: ['Automotive', 'Business', 'Crime', 'Economics', 'Entertainment', 'Finance', 'Financial Crime', 'General', 'Health', 'Lifestyle', 'Politics', 'Science', 'Sports', 'Tech', 'Travel', 'Weather']\n",
+      "mlb.classes_: ['Automotive', 'Business', 'Crime', 'Economics', 'Entertainment', 'Finance', 'Financial Crime', 'General', 'Health', 'Lifestyle', 'Politics', 'Science', 'Sports', 'Tech', 'Travel', 'Weather']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"classes:\", classes)\n",
+    "print(\"mlb.classes_:\", list(mlb.classes_))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "857c360a-07c4-41ef-bcd3-a90b862eaea4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def batch(iterable, n=10):\n",
+    "    l = len(iterable)\n",
+    "    for ndx in range(0, l, n):\n",
+    "        yield iterable[ndx:min(ndx + n, l)]\n",
+    "\n",
+    "def get_multilabel_themes(embeddings, batch_size=16):\n",
+    "    results = []\n",
+    "\n",
+    "    for batch_embeddings in batch(embeddings, n=batch_size):\n",
+    "        # Transform embeddings with the pre-fitted scaler\n",
+    "        batch_embeddings = SCALER.transform(batch_embeddings)\n",
+    "        batch_embeddings = torch.tensor(batch_embeddings, dtype=torch.float)\n",
+    "        if device == 0:\n",
+    "            batch_embeddings = batch_embeddings.to(\"cuda\")\n",
+    "\n",
+    "        with torch.no_grad():\n",
+    "            predictions = THEME_MODEL(batch_embeddings)\n",
+    "            probabilities = torch.sigmoid(predictions)\n",
+    "\n",
+    "        # Convert probabilities to CPU and numpy format for easier processing with sklearn\n",
+    "        probabilities = probabilities.cpu().numpy()\n",
+    "\n",
+    "        # Prepare the list of dictionaries with theme names and scores\n",
+    "        for probability in probabilities:\n",
+    "            result = [{'name': label, 'score': round(float(score), 2)} for label, score in\n",
+    "                      zip(mlb.classes_, probability)]\n",
+    "            if not result:\n",
+    "                result = [{'name': label, 'score': round(float(score), 2)} for label, score in\n",
+    "                          zip(mlb.classes_, probability)]\n",
+    "                result = [max(result, key=lambda x: x['score'])]\n",
+    "            result = sorted(result, key=lambda x: x['score'], reverse=True)\n",
+    "            results.append(result)\n",
+    "\n",
+    "    return results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "3f23a7c4-12df-45b7-96f8-148f7cbff251",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class WideShallowMLP(nn.Module):\n",
+    "    def __init__(\n",
+    "        self,\n",
+    "        input_dim=1024,\n",
+    "        num_labels=NUM_LABELS,\n",
+    "        dropout=0.35,\n",
+    "        activation=\"gelu\",   # \"gelu\" or \"silu\"\n",
+    "        hidden2=768,         # 2nd layer width\n",
+    "        temperature=0.6,     # logit temperature scaling\n",
+    "    ):\n",
+    "        super().__init__()\n",
+    "\n",
+    "        if activation == \"gelu\":\n",
+    "            act = nn.GELU()\n",
+    "        elif activation == \"silu\":\n",
+    "            act = nn.SiLU()\n",
+    "        else:\n",
+    "            raise ValueError(f\"Unknown activation: {activation}\")\n",
+    "\n",
+    "        self.temperature = float(temperature)\n",
+    "\n",
+    "        self.net = nn.Sequential(\n",
+    "            nn.Linear(input_dim, 1024),\n",
+    "            act,\n",
+    "            nn.LayerNorm(1024),\n",
+    "            nn.Dropout(dropout),\n",
+    "\n",
+    "            nn.Linear(1024, hidden2),\n",
+    "            act,\n",
+    "            nn.LayerNorm(hidden2),\n",
+    "            nn.Dropout(dropout),\n",
+    "\n",
+    "            nn.Linear(hidden2, num_labels),\n",
+    "        )\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        logits = self.net(x)\n",
+    "        if self.temperature != 1.0:\n",
+    "            logits = logits / self.temperature\n",
+    "        return logits\n",
+    "\n",
+    "THEME_MODEL = WideShallowMLP()\n",
+    "device = 0 if torch.cuda.is_available() else -1\n",
+    "if device == 0:\n",
+    "    THEME_MODEL.load_state_dict(torch.load('exp_artifacts_grid/ws_gelu_d0.35_wd0.003_h2768_t0.6.pth'))\n",
+    "else:\n",
+    "    THEME_MODEL.load_state_dict(torch.load(\n",
+    "        'exp_artifacts_grid/ws_gelu_d0.35_wd0.003_h2768_t0.6.pth', map_location=torch.device('cpu')))\n",
+    "THEME_MODEL.eval()\n",
+    "if torch.cuda.is_available():\n",
+    "    THEME_MODEL.to(device)\n",
+    "\n",
+    "SCALER = load('exp_artifacts_grid/scaler.joblib')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "22c1a4c1-d0e1-44f9-8518-6d81acec062e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "thresholds = np.load('exp_artifacts_grid/ws_gelu_d0.35_wd0.003_h2768_t0.6_thresholds.npy')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "f8a98800-9a1d-417e-904f-06f814be81fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.5 , 0.6 , 0.65, 0.55, 0.5 , 0.5 , 0.45, 0.5 , 0.6 , 0.75, 0.45,\n",
+       "       0.5 , 0.5 , 0.6 , 0.55, 0.2 ], dtype=float32)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "thresholds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c4ac491c-96bb-48d9-9037-ffd758d81ad8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qwen_themes = get_multilabel_themes([i['embedding'] for i in testd])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "9a131376-cfbc-4ebd-9f32-859e61e989e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Business', 'score': 0.86},\n",
+       " {'name': 'Automotive', 'score': 0.47},\n",
+       " {'name': 'Travel', 'score': 0.37},\n",
+       " {'name': 'Tech', 'score': 0.19},\n",
+       " {'name': 'Lifestyle', 'score': 0.07},\n",
+       " {'name': 'General', 'score': 0.04},\n",
+       " {'name': 'Health', 'score': 0.03},\n",
+       " {'name': 'Politics', 'score': 0.03},\n",
+       " {'name': 'Economics', 'score': 0.02},\n",
+       " {'name': 'Finance', 'score': 0.02},\n",
+       " {'name': 'Science', 'score': 0.02},\n",
+       " {'name': 'Sports', 'score': 0.02},\n",
+       " {'name': 'Entertainment', 'score': 0.01},\n",
+       " {'name': 'Crime', 'score': 0.0},\n",
+       " {'name': 'Financial Crime', 'score': 0.0},\n",
+       " {'name': 'Weather', 'score': 0.0}]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "qwen_themes[12469]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "17e1e5db-9fe6-431b-bc58-255e1288bd44",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Business', 'Travel']"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "testd[12469]['themes']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "1a4fa192-a344-4d7d-bd7b-ff13f723dfd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.preprocessing import MultiLabelBinarizer\n",
+    "from sklearn.metrics import (\n",
+    "    accuracy_score,\n",
+    "    hamming_loss,\n",
+    "    precision_score,\n",
+    "    recall_score,\n",
+    "    f1_score,\n",
+    "    jaccard_score,\n",
+    "    classification_report\n",
+    ")\n",
+    "\n",
+    "def print_multilabel_metrics(y_true, y_pred):\n",
+    "    \"\"\"\n",
+    "    y_true: List[List[str]]  -> ground truth labels\n",
+    "    y_pred: List[List[str]]  -> predicted labels\n",
+    "    \"\"\"\n",
+    "\n",
+    "    mlb = MultiLabelBinarizer()\n",
+    "    y_true_bin = mlb.fit_transform(y_true)\n",
+    "    y_pred_bin = mlb.transform(y_pred)\n",
+    "\n",
+    "    acc = accuracy_score(y_true_bin, y_pred_bin)\n",
+    "    h_loss = hamming_loss(y_true_bin, y_pred_bin)\n",
+    "    prec_micro = precision_score(y_true_bin, y_pred_bin, average=\"micro\", zero_division=0)\n",
+    "    rec_micro = recall_score(y_true_bin, y_pred_bin, average=\"micro\", zero_division=0)\n",
+    "    f1_micro = f1_score(y_true_bin, y_pred_bin, average=\"micro\", zero_division=0)\n",
+    "    jacc = jaccard_score(y_true_bin, y_pred_bin, average=\"samples\", zero_division=0)\n",
+    "\n",
+    "    print(\"Accuracy:\", acc)\n",
+    "    print(\"Hamming Loss:\", h_loss)\n",
+    "    print(\"Precision (micro):\", prec_micro)\n",
+    "    print(\"Recall (micro):\", rec_micro)\n",
+    "    print(\"F1-Score (micro):\", f1_micro)\n",
+    "    print(\"Jaccard Similarity (samples avg):\", jacc)\n",
+    "    print(\"\\nClassification Report:\")\n",
+    "    print(classification_report(y_true_bin, y_pred_bin, target_names=mlb.classes_, zero_division=0))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f517dcbf-8a64-49e6-a9b1-754353548ca8",
+   "metadata": {},
+   "source": [
+    "# score >= 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "2e071073-bd2b-4ad9-b0dd-00c9ee4dc756",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.230875\n",
+      "Hamming Loss: 0.113875\n",
+      "Precision (micro): 0.4182678401718937\n",
+      "Recall (micro): 0.9531544256120528\n",
+      "F1-Score (micro): 0.5814020275121334\n",
+      "Jaccard Similarity (samples avg): 0.524701389322483\n",
+      "\n",
+      "Classification Report:\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "     Automotive       0.52      0.95      0.67      1014\n",
+      "       Business       0.39      0.97      0.56      2698\n",
+      "          Crime       0.37      0.96      0.53      1337\n",
+      "      Economics       0.31      0.97      0.47      1000\n",
+      "  Entertainment       0.45      0.97      0.61      1161\n",
+      "        Finance       0.37      0.97      0.54      1371\n",
+      "Financial Crime       0.49      0.94      0.64      1009\n",
+      "        General       0.26      0.89      0.41      1065\n",
+      "         Health       0.44      0.95      0.61      1295\n",
+      "      Lifestyle       0.29      0.91      0.44      1023\n",
+      "       Politics       0.48      0.96      0.64      2414\n",
+      "        Science       0.45      0.96      0.61      1056\n",
+      "         Sports       0.64      0.96      0.77      1118\n",
+      "           Tech       0.46      0.97      0.62      1476\n",
+      "         Travel       0.47      0.95      0.63      1143\n",
+      "        Weather       0.63      0.94      0.75      1060\n",
+      "\n",
+      "      micro avg       0.42      0.95      0.58     21240\n",
+      "      macro avg       0.44      0.95      0.59     21240\n",
+      "   weighted avg       0.44      0.95      0.59     21240\n",
+      "    samples avg       0.53      0.95      0.64     21240\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_labels = [sorted(i['themes']) for i in testd]\n",
+    "y_pred = [sorted(list(set(j['name'] for j in i if j['score'] >= 0.1))) for i in qwen_themes]\n",
+    "print_multilabel_metrics(all_labels, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8746d0e7-8897-4d1f-8a46-1fccd618d198",
+   "metadata": {},
+   "source": [
+    "# score with ML detected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "63d3005f-df66-4205-ba00-31d60d109930",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.5604375\n",
+      "Hamming Loss: 0.04196484375\n",
+      "Precision (micro): 0.727671018956318\n",
+      "Recall (micro): 0.789783427495292\n",
+      "F1-Score (micro): 0.7574560314270878\n",
+      "Jaccard Similarity (samples avg): 0.712795386904762\n",
+      "\n",
+      "Classification Report:\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "     Automotive       0.82      0.81      0.82      1014\n",
+      "       Business       0.70      0.74      0.72      2698\n",
+      "          Crime       0.72      0.73      0.72      1337\n",
+      "      Economics       0.62      0.79      0.70      1000\n",
+      "  Entertainment       0.79      0.83      0.81      1161\n",
+      "        Finance       0.70      0.81      0.75      1371\n",
+      "Financial Crime       0.74      0.78      0.76      1009\n",
+      "        General       0.47      0.67      0.55      1065\n",
+      "         Health       0.80      0.78      0.79      1295\n",
+      "      Lifestyle       0.70      0.63      0.66      1023\n",
+      "       Politics       0.74      0.86      0.80      2414\n",
+      "        Science       0.72      0.79      0.76      1056\n",
+      "         Sports       0.89      0.88      0.89      1118\n",
+      "           Tech       0.78      0.80      0.79      1476\n",
+      "         Travel       0.80      0.80      0.80      1143\n",
+      "        Weather       0.75      0.89      0.81      1060\n",
+      "\n",
+      "      micro avg       0.73      0.79      0.76     21240\n",
+      "      macro avg       0.73      0.79      0.76     21240\n",
+      "   weighted avg       0.73      0.79      0.76     21240\n",
+      "    samples avg       0.76      0.82      0.76     21240\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "threshold = {'Automotive': 0.5, 'Business': 0.6, 'Crime': 0.65, 'Economics': 0.55, 'Entertainment': 0.5, 'Finance': 0.5, 'Financial Crime': 0.45, 'General': 0.5, 'Health': 0.6, 'Lifestyle': 0.75, 'Politics': 0.45, 'Science': 0.5, 'Sports': 0.5, 'Tech': 0.6, 'Travel': 0.55, 'Weather': 0.2}\n",
+    "all_labels = [sorted(i['themes']) for i in testd]\n",
+    "y_pred = [sorted(list(set(j['name'] for j in i if j['score'] >= threshold[j['name']]))) for i in qwen_themes]\n",
+    "print_multilabel_metrics(all_labels, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57e004e7-a815-465f-9991-efde869338c4",
+   "metadata": {},
+   "source": [
+    "# score >= 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "ddb9dfdc-5cba-4d9d-bdf6-fa80ea157bf6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.5435\n",
+      "Hamming Loss: 0.04390625\n",
+      "Precision (micro): 0.7069707757264674\n",
+      "Recall (micro): 0.8040960451977401\n",
+      "F1-Score (micro): 0.7524120005286576\n",
+      "Jaccard Similarity (samples avg): 0.7082564100829726\n",
+      "\n",
+      "Classification Report:\n",
+      "                 precision    recall  f1-score   support\n",
+      "\n",
+      "     Automotive       0.82      0.81      0.82      1014\n",
+      "       Business       0.66      0.79      0.72      2698\n",
+      "          Crime       0.64      0.80      0.71      1337\n",
+      "      Economics       0.60      0.81      0.69      1000\n",
+      "  Entertainment       0.79      0.83      0.81      1161\n",
+      "        Finance       0.70      0.81      0.75      1371\n",
+      "Financial Crime       0.76      0.76      0.76      1009\n",
+      "        General       0.47      0.67      0.55      1065\n",
+      "         Health       0.76      0.82      0.79      1295\n",
+      "      Lifestyle       0.56      0.77      0.65      1023\n",
+      "       Politics       0.77      0.84      0.80      2414\n",
+      "        Science       0.72      0.79      0.76      1056\n",
+      "         Sports       0.89      0.88      0.89      1118\n",
+      "           Tech       0.74      0.84      0.78      1476\n",
+      "         Travel       0.78      0.81      0.80      1143\n",
+      "        Weather       0.86      0.76      0.81      1060\n",
+      "\n",
+      "      micro avg       0.71      0.80      0.75     21240\n",
+      "      macro avg       0.72      0.80      0.75     21240\n",
+      "   weighted avg       0.72      0.80      0.76     21240\n",
+      "    samples avg       0.75      0.83      0.76     21240\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_labels = [sorted(i['themes']) for i in testd]\n",
+    "y_pred = [sorted(list(set(j['name'] for j in i if j['score'] >= 0.5))) for i in qwen_themes]\n",
+    "print_multilabel_metrics(all_labels, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f50c82e-d524-450c-aef5-0170af19123d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}