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null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a35327b14b0c4eaf815f52b737098d08": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "18VpPI1iEIyw", "outputId": "8acf0b02-9100-45d4-d2b1-7efd882ca1fc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ], "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ] }, { "cell_type": "code", "source": [ "!pip install transformers datasets torch scikit-learn" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "lWmny_C1E8Bl", "outputId": "36b831ee-6856-4151-f262-b791c2336bfd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in /usr/local/lib/python3.11/dist-packages (4.52.4)\n", "Requirement already satisfied: datasets in /usr/local/lib/python3.11/dist-packages (2.14.4)\n", "Requirement already satisfied: torch in /usr/local/lib/python3.11/dist-packages (2.6.0+cu124)\n", "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.11/dist-packages (from transformers) (3.18.0)\n", "Requirement already satisfied: huggingface-hub<1.0,>=0.30.0 in /usr/local/lib/python3.11/dist-packages (from 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collected packages: nvidia-nvjitlink-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n", " Attempting uninstall: nvidia-nvjitlink-cu12\n", " Found existing installation: nvidia-nvjitlink-cu12 12.5.82\n", " Uninstalling nvidia-nvjitlink-cu12-12.5.82:\n", " Successfully uninstalled nvidia-nvjitlink-cu12-12.5.82\n", " Attempting uninstall: nvidia-curand-cu12\n", " Found existing installation: nvidia-curand-cu12 10.3.6.82\n", " Uninstalling nvidia-curand-cu12-10.3.6.82:\n", " Successfully uninstalled nvidia-curand-cu12-10.3.6.82\n", " Attempting uninstall: nvidia-cufft-cu12\n", " Found existing installation: nvidia-cufft-cu12 11.2.3.61\n", " Uninstalling nvidia-cufft-cu12-11.2.3.61:\n", " Successfully uninstalled nvidia-cufft-cu12-11.2.3.61\n", " Attempting uninstall: nvidia-cuda-runtime-cu12\n", " Found existing installation: nvidia-cuda-runtime-cu12 12.5.82\n", " Uninstalling nvidia-cuda-runtime-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-runtime-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-nvrtc-cu12\n", " Found existing installation: nvidia-cuda-nvrtc-cu12 12.5.82\n", " Uninstalling nvidia-cuda-nvrtc-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-nvrtc-cu12-12.5.82\n", " Attempting uninstall: nvidia-cuda-cupti-cu12\n", " Found existing installation: nvidia-cuda-cupti-cu12 12.5.82\n", " Uninstalling nvidia-cuda-cupti-cu12-12.5.82:\n", " Successfully uninstalled nvidia-cuda-cupti-cu12-12.5.82\n", " Attempting uninstall: nvidia-cublas-cu12\n", " Found existing installation: nvidia-cublas-cu12 12.5.3.2\n", " Uninstalling nvidia-cublas-cu12-12.5.3.2:\n", " Successfully uninstalled nvidia-cublas-cu12-12.5.3.2\n", " Attempting uninstall: nvidia-cusparse-cu12\n", " Found existing installation: nvidia-cusparse-cu12 12.5.1.3\n", " Uninstalling nvidia-cusparse-cu12-12.5.1.3:\n", " Successfully uninstalled nvidia-cusparse-cu12-12.5.1.3\n", " Attempting uninstall: nvidia-cudnn-cu12\n", " Found existing installation: nvidia-cudnn-cu12 9.3.0.75\n", " Uninstalling nvidia-cudnn-cu12-9.3.0.75:\n", " Successfully uninstalled nvidia-cudnn-cu12-9.3.0.75\n", " Attempting uninstall: nvidia-cusolver-cu12\n", " Found existing installation: nvidia-cusolver-cu12 11.6.3.83\n", " Uninstalling nvidia-cusolver-cu12-11.6.3.83:\n", " Successfully uninstalled nvidia-cusolver-cu12-11.6.3.83\n", "Successfully installed nvidia-cublas-cu12-12.4.5.8 nvidia-cuda-cupti-cu12-12.4.127 nvidia-cuda-nvrtc-cu12-12.4.127 nvidia-cuda-runtime-cu12-12.4.127 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.2.1.3 nvidia-curand-cu12-10.3.5.147 nvidia-cusolver-cu12-11.6.1.9 nvidia-cusparse-cu12-12.3.1.170 nvidia-nvjitlink-cu12-12.4.127\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "#this just installs bert locally\n", "!mkdir -p /content/bert_temp\n", "!wget https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin -P /content/bert_temp\n", "!wget https://huggingface.co/bert-base-uncased/resolve/main/config.json -P /content/bert_temp\n", "!wget https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt -P /content/bert_temp" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "-X-6U4fMGKBk", "outputId": "a9e60262-6770-432d-cd4e-a9ba5015480e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2025-06-24 04:01:05-- https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin\n", "Resolving huggingface.co (huggingface.co)... 3.163.189.74, 3.163.189.90, 3.163.189.114, ...\n", "Connecting to huggingface.co (huggingface.co)|3.163.189.74|:443... connected.\n", "HTTP request sent, awaiting response... 302 Found\n", "Location: 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"code", "source": [ "import pandas as pd\n", "import torch\n", "import numpy as np\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report, f1_score, precision_score, recall_score\n", "from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n", "from transformers import DataCollatorWithPadding\n", "from datasets import Dataset\n", "import warnings\n", "\n", "\n", "warnings.filterwarnings('ignore')\n", "\n", "\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Using device: {device}\")\n", "\n", "# input\n", "try:\n", " df = pd.read_csv(\"/content/drive/MyDrive/MIT PROJECT 2/Proper_Dataset.csv\")\n", " print(\"Data loaded successfully!\")\n", " print(f\"Dataset shape: {df.shape}\")\n", " print(f\"Label distribution before mapping:\\n{df['label'].value_counts()}\")\n", "\n", " #we got one clm here...\n", " df['content'] = df['text']\n", " df['label'] = df['label'].map({'FAKE': 0, 'REAL': 1})\n", "\n", " #ensuring num of real and fake rows havent chnaged\n", " print(f\"Label distribution after mapping:\\n{df['label'].value_counts()}\")\n", "\n", " # spllttin dataset for train-test(80:20)\n", " train_df, test_df = train_test_split(\n", " df[['content', 'label']],\n", " test_size=0.2,\n", " stratify=df['label'],\n", " random_state=42\n", " )\n", "\n", " train_dataset = Dataset.from_pandas(train_df.reset_index(drop=True))\n", " test_dataset = Dataset.from_pandas(test_df.reset_index(drop=True))\n", "\n", " print(f\"Train size: {len(train_dataset)}, Test size: {len(test_dataset)}\")\n", "\n", "except Exception as e:\n", " print(f\"Error loading data: {e}\")\n", " raise\n", "\n", "# loads a BERT tokenizer and uses it to tokenize a dataset\n", "try:\n", " print(\"Loading tokenizer...\")\n", " tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "\n", " def tokenize_function(examples):\n", " return tokenizer(examples[\"content\"], truncation=True, max_length=512, padding='max_length')\n", "\n", " print(\"Tokenizing train set...\")\n", " train_dataset = train_dataset.map(tokenize_function, batched=True)\n", " print(\"Tokenizing test set...\")\n", " test_dataset = test_dataset.map(tokenize_function, batched=True)\n", "\n", " print(\"Tokenization complete!\")\n", "except Exception as e:\n", " print(f\"Error during tokenization: {e}\")\n", " raise\n", "\n", "\n", "num_fake = 43391\n", "num_real = 64641\n", "#above 2 lines r the number of fake news and real news. since they r imbalanced, weight is added below\n", "total = num_fake + num_real\n", "\n", "weights = torch.tensor([\n", " total / (2 * num_fake),\n", " total / (2 * num_real)\n", "], dtype=torch.float32).to(device)\n", "\n", "print(f\"Class weights: {weights}\")\n", "\n", "#loadin model frm local space\n", "try:\n", " print(\"Loading model...\")\n", " model = BertForSequenceClassification.from_pretrained(\n", " \"./bert_temp\",\n", " num_labels=2\n", " ).to(device)\n", " print(\"Model loaded successfully from local files!\")\n", "except Exception as e:\n", " print(f\"Error loading model: {e}\")\n", " raise\n", "\n", "# Custom Trainer with weighted loss\n", "class WeightedTrainer(Trainer):\n", " def compute_loss(self, model, inputs, return_outputs=False, **kwargs):\n", " if 'num_items_in_batch' in kwargs:\n", " _ = kwargs.pop('num_items_in_batch')\n", " labels = inputs.get(\"labels\")\n", " outputs = model(**inputs)\n", " logits = outputs.get('logits')\n", " loss_fct = torch.nn.CrossEntropyLoss(weight=weights)\n", " loss = loss_fct(logits.view(-1, 2), labels.view(-1))\n", " return (loss, outputs) if return_outputs else loss\n", "\n", "# Training args\n", "training_args = TrainingArguments(\n", " output_dir=\"./results\",\n", " eval_strategy=\"no\",\n", " save_strategy=\"epoch\",\n", " num_train_epochs=4,\n", " per_device_train_batch_size=8,\n", " per_device_eval_batch_size=16,\n", " learning_rate=2e-5,\n", " weight_decay=0.01,\n", " load_best_model_at_end=False,\n", " logging_dir=\"./logs\",\n", " logging_steps=10,\n", " fp16=True,\n", " report_to=\"none\",\n", " save_total_limit=2,\n", " seed=42\n", ")\n", "\n", "# Metrics function\n", "def compute_metrics(eval_pred):\n", " logits, labels = eval_pred\n", " preds = np.argmax(logits, axis=-1)\n", " return {\n", " \"accuracy\": (preds == labels).mean(),\n", " \"f1\": f1_score(labels, preds, average=\"weighted\"),\n", " \"precision\": precision_score(labels, preds, average=\"weighted\"),\n", " \"recall\": recall_score(labels, preds, average=\"weighted\")\n", " }\n", "\n", "\n", "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)#will make sure all inputs in a batch are the same length by padding them but only as much as needed\n", "\n", "try:\n", " print(\"Initializing trainer...\")\n", " trainer = WeightedTrainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_dataset,\n", " eval_dataset=test_dataset,\n", " compute_metrics=compute_metrics,\n", " data_collator=data_collator\n", " )\n", "\n", " print(\"Starting training...\")\n", " train_results = trainer.train()\n", "\n", " # Savin thge model\n", " print(\"Training complete! Saving model...\")\n", " trainer.save_model(\"/content/drive/MyDrive/MIT PROJECT 2/fake_news_bert_model\")\n", " tokenizer.save_pretrained(\"/content/drive/MyDrive/MIT PROJECT 2/fake_news_bert_model\")\n", " print(\"Model saved successfully!\")\n", "\n", "\n", " print(\"Final evaluation on test data:\")\n", " metrics = trainer.evaluate()\n", " print(metrics)\n", "\n", "except Exception as e:\n", " print(f\"Error during training: {e}\")\n", " raise" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "84552219c8e5403789c915bb1b376e70", "ccba7f6d5d2b47a69228239a6f078e66", "3236fe971daa4059bb6832a992386043", "5cdd624accd5450bb4445c4398427d02", "1b53ce62f9b149a18f649df87ff23d37", "a4f5a31a724f42d99b183ce8f1b85fd1", "cfb8da56a6ec42c59f9310464ab27103", "dec1aa9f4a1d4fe8a0ac64889cc41954", "3ab82e41d00549bebd0a7807917c9bbf", "ce332063c5d1491cacbed80ec565ce37", "799efb4fbbe24703803cc825a1098f50", "f9693f613a294678aa6c6aaa0baecf12", "8a704d74f4aa4aef88f40275b11fcb8e", "20668c333f2c4a2b8482c7d299933117", "8952ee2759954ca992ff816c1eafc22b", "2884610ddce74b538ba45d68b45a3c17", "428349b41dc54bfeb09fd91183b60cde", "8183c82541124a19af3694b1c05014b7", "e67261da139642eca2c865429846301b", "85c7eb4f0aec4b6b81d5ce41d4099d76", "90d9c99130af4d15a7fac9de1d87ca41", "f31c0b2463504944acf9673592b598de", "1efd487db475444f9168c20ba61fcd61", "a31e6dd808ac4438a3b3bd532548f1fe", "6f9d8bf39bba417d8189e7f2a87a99b9", "7fc7d8d70c194688b9a6a52fbd6340cb", "8be157f9427f4c098734d326e9bb4b00", "45ab4f063f3d43dc86fe3905a005b174", "377d4e1576294294b318e1260136408f", "83540863a39b4e19be6965ba8879c9dd", "dd89b8a64ecf49b3b6b69e5b98fe1d9e", "331414e732f54580a0b9f4442328b90d", "ceb0333d8d3840d58c3cc79f929fe9fc", "d84c511f45aa408d9738875d0aee1571", "d04446ba387b4740a4871adc8d2894d6", "d265d4e4909847e3b5eef7a08b0662e8", "b3643a7970444668931297c4d831370f", "ec72b0afb4b84775a9763f7f50f9bf15", "519e48d31fbf483d830d2170a6348953", "fd79b62bc2834222a3d9a81176e3a6d3", "0d98babd0b974f15bec430739db09433", "df441c97fed34791a4af08487517e73a", "a202d395199d4f969b6d8c8016306997", "98aeb58042cf43d7a6e94616004050f6", "75d28505a95546c08deeb5acd5f1672e", "38ef7c373baf46eea20212a88e5494b6", "258881e00c5d499f91940f6a1086d355", "ce5ab560370e467aab0eeb91e624cd48", "a5f68943e90b4184bffec5d6ce2fe988", "d3435dfbf85349828e59c16bb3351955", "6a5dcf05bc164883b686accd3e07ef24", "cbe16d3b10c4494e8a6337e02ee10547", "fc4ba0e4383b49658cbefb65d0f42cc3", "c0a130e74b1741ec8bf49d5b47600eca", "58b55ab45b0a475499686a3731a864ef", "4b1a3ff9a3084e4e9495a84daa9fab34", "f745357f0cf44474a000234f5b6382b4", "c0b146f2eed54f68b9c4b53f9732545c", "9482c9950ad7454c80ea7c06e6bdfa21", "938e85587e0f4a638e002cb498a80456", "6b0b6611944349a4bd6f184f5219bac8", "f0cf0860c6484d0d8f16362156055b11", "8c7bb251a8d0486bb11dbefdd91471b2", "2d7b0886b7074f1bbf69d167c7fdfd4c", "6a85336ecdc34385b35fa8f357d7131a", "9ecd41b7339543e8ad94ba2229b53a51" ] }, "id": "KdwDWWWXGRKl", "outputId": "db1659b0-a65f-4a6f-8980-b5a35049e1a4" }, "execution_count": null, "outputs": [ { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "Using device: cuda\n", "Data loaded successfully!\n", "Dataset shape: (108032, 2)\n", "Label distribution before mapping:\n", "label\n", "REAL 64641\n", "FAKE 43391\n", "Name: count, dtype: int64\n", "Label distribution after mapping:\n", "label\n", "1 64641\n", "0 43391\n", "Name: count, dtype: int64\n", "Train size: 86425, Test size: 21607\n", "Loading tokenizer...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "84552219c8e5403789c915bb1b376e70", "version_major": 2, "version_minor": 0 }, "text/plain": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00\n", " \n", " \n", " [ 7337/43216 28:01 < 2:17:02, 4.36 it/s, Epoch 0.68/4]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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100.665000
200.687400
300.566000
400.516400
500.406500
600.354700
700.267600
800.236800
900.227700
1000.189200
1100.162900
1200.155700
1300.259900
1400.142200
1500.227400
1600.154500
1700.213200
1800.121500
1900.231500
2000.059900
2100.251700
2200.298800
2300.090800
2400.113300
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2600.249600
2700.304600
2800.010700
2900.340200
3000.502400
3100.073400
3200.194600
3300.079100
3400.196500
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3700.137600
3800.256700
3900.145300
4000.213100
4100.202600
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4300.218500
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4500.264200
4600.118400
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5300.093700
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5500.109500
5600.284800
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5900.192700
6000.317500
6100.007000
6200.108000
6300.213700
6400.072200
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StepTraining Loss
100.665000
200.687400
300.566000
400.516400
500.406500
600.354700
700.267600
800.236800
900.227700
1000.189200
1100.162900
1200.155700
1300.259900
1400.142200
1500.227400
1600.154500
1700.213200
1800.121500
1900.231500
2000.059900
2100.251700
2200.298800
2300.090800
2400.113300
2500.025700
2600.249600
2700.304600
2800.010700
2900.340200
3000.502400
3100.073400
3200.194600
3300.079100
3400.196500
3500.198800
3600.096600
3700.137600
3800.256700
3900.145300
4000.213100
4100.202600
4200.069300
4300.218500
4400.219300
4500.264200
4600.118400
4700.187600
4800.210100
4900.279500
5000.272700
5100.161900
5200.263200
5300.093700
5400.246900
5500.109500
5600.284800
5700.120100
5800.313800
5900.192700
6000.317500
6100.007000
6200.108000
6300.213700
6400.072200
6500.204500
6600.242800
6700.126700
6800.351000
6900.239400
7000.195600
7100.050900
7200.135700
7300.144200
7400.058100
7500.050300
7600.337600
7700.119800
7800.175600
7900.251800
8000.055800
8100.135700
8200.004300
8300.282500
8400.291200
8500.079700
8600.321600
8700.010200
8800.102500
8900.166200
9000.149300
9100.334700
9200.143100
9300.054800
9400.037800
9500.081400
9600.199700
9700.298600
9800.037800
9900.076200
10000.001400
10100.171300
10200.181900
10300.081200
10400.144900
10500.161900
10600.109900
10700.042700
10800.056300
10900.162200
11000.216000
11100.154800
11200.150200
11300.254000
11400.003900
11500.172800
11600.409500
11700.017300
11800.049400
11900.228800
12000.270700
12100.278000
12200.321400
12300.023700
12400.071300
12500.155700
12600.164400
12700.048200
12800.201300
12900.108400
13000.149000
13100.057500
13200.004600
13300.003100
13400.087000
13500.002800
13600.139600
13700.057600
13800.121700
13900.091400
14000.241200
14100.406400
14200.077300
14300.007900
14400.174600
14500.301000
14600.105300
14700.353800
14800.104600
14900.212200
15000.196300
15100.132600
15200.087600
15300.126300
15400.125200
15500.022100
15600.110600
15700.160500
15800.003100
15900.054900
16000.075500
16100.231500
16200.102100
16300.053200
16400.143400
16500.285700
16600.081300
16700.126400
16800.029500
16900.163000
17000.003800
17100.318300
17200.003000
17300.114800
17400.081600
17500.119000
17600.140700
17700.090300
17800.124000
17900.062800
18000.108900
18100.146700
18200.083200
18300.152000
18400.003800
18500.147800
18600.236800
18700.180700
18800.086600
18900.203600
19000.052900
19100.146300
19200.254600
19300.104500
19400.265200
19500.084200
19600.005400
19700.332900
19800.204700
19900.343700
20000.038100
20100.098500
20200.088800
20300.084500
20400.096500
20500.067000
20600.032800
20700.320900
20800.001400
20900.009100
21000.016100
21100.144300
21200.204700
21300.064100
21400.182200
21500.146800
21600.286000
21700.087300
21800.116300
21900.140400
22000.015200
22100.008000
22200.240700
22300.265500
22400.070800
22500.107800
22600.088800
22700.228400
22800.211700
22900.109100
23000.127200
23100.148300
23200.013500
23300.122900
23400.125100
23500.142000
23600.002600
23700.277600
23800.001500
23900.116700
24000.044600
24100.263800
24200.117800
24300.065800
24400.084000
24500.111600
24600.201400
24700.177800
24800.162400
24900.109400
25000.004500
25100.186800
25200.149500
25300.011900
25400.055000
25500.129400
25600.072500
25700.029900
25800.116200
25900.011800
26000.102000
26100.198700
26200.044700
26300.146100
26400.057700
26500.117400
26600.185900
26700.098200
26800.133400
26900.006400
27000.023200
27100.283400
27200.155300
27300.243100
27400.015900
27500.074100
27600.284500
27700.093100
27800.060900
27900.006200
28000.144700
28100.165100
28200.018100
28300.079700
28400.131900
28500.091900
28600.163600
28700.186300
28800.116200
28900.087500
29000.006100
29100.063800
29200.034100
29300.005700
29400.049500
29500.184100
29600.069200
29700.084500
29800.389800
29900.121000
30000.102900
30100.091000
30200.054600
30300.314900
30400.135000
30500.097500
30600.041500
30700.130300
30800.078100
30900.160900
31000.136000
31100.119100
31200.036000
31300.012200
31400.162400
31500.080800
31600.115500
31700.128900
31800.091000
31900.079700
32000.153700
32100.102600
32200.085300
32300.140600
32400.099900
32500.079000
32600.001800
32700.113800
32800.181600
32900.148000
33000.183900
33100.120000
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" ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Training complete! Saving model...\n", "Model saved successfully!\n", "Final evaluation on test data:\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", "

\n", " \n", " \n", " [1351/1351 02:50]\n", "
\n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "{'eval_loss': 0.1399463564157486, 'eval_accuracy': 0.9819502938862406, 'eval_f1': 0.9819359815744226, 'eval_precision': 0.9819511409023604, 'eval_recall': 0.9819502938862406, 'eval_runtime': 170.6802, 'eval_samples_per_second': 126.593, 'eval_steps_per_second': 7.915, 'epoch': 4.0}\n" ] } ] }, { "cell_type": "markdown", "source": [ "# *below code section r the code to get back some variables coz we ran out of runtime so *" ], "metadata": { "id": "mfTdScObo2RL" } }, { "cell_type": "code", "source": [ "# Custom Trainer with weighted loss\n", "class WeightedTrainer(Trainer):\n", " def compute_loss(self, model, inputs, return_outputs=False):\n", " labels = inputs.get(\"labels\")\n", " outputs = model(**inputs)\n", " logits = outputs.get(\"logits\")\n", " loss_fct = torch.nn.CrossEntropyLoss(weight=weights)\n", " loss = loss_fct(logits.view(-1, model.config.num_labels), labels.view(-1))\n", " return (loss, outputs) if return_outputs else loss\n" ], "metadata": { "id": "Lc2P7o_ULK2_" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import pandas as pd\n", "import torch\n", "import numpy as np\n", "from transformers import BertTokenizer, BertForSequenceClassification, Trainer\n", "from sklearn.model_selection import train_test_split\n", "from datasets import Dataset\n", "\n", "# Set device\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "\n", "# Load saved tokenizer and model\n", "model_path = \"/content/drive/MyDrive/MIT PROJECT 2/fake_news_bert_model\"\n", "tokenizer = BertTokenizer.from_pretrained(model_path)\n", "model = BertForSequenceClassification.from_pretrained(model_path).to(device)\n", "\n", "# Reload test data\n", "df = pd.read_csv(\"/content/drive/MyDrive/MIT PROJECT 2/Proper_Dataset.csv\")\n", "df['label'] = df['label'].map({'FAKE': 0, 'REAL': 1})\n", "df['content'] = df['text']\n", "df = df[['content', 'label']]\n", "\n", "# Split and tokenize test set\n", "_, test_df = train_test_split(df, test_size=0.2, stratify=df['label'], random_state=42)\n", "test_dataset = Dataset.from_pandas(test_df.reset_index(drop=True))\n", "\n", "# Tokenization\n", "def tokenize_function(examples):\n", " return tokenizer(examples[\"content\"], truncation=True, max_length=512, padding='max_length')\n", "\n", "test_dataset = test_dataset.map(tokenize_function, batched=True)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "9a01b3cf92694499b4fda4b57b9c6f31", "6c4f1a0ff6d243a79830dda52dffcf9f", "6c8133691a674622ac34fb5af858c3fc", "436a26a7819f4f6581d1d9eb8dfb336b", "c2d7c04d27c34dfb80eb87dd49c1454c", "ed242a62690b4e3dadb400e0f3a661fe", "8b23c51134df480885b5afef44b4c114", "602aff37256349808375284bb76ccc55", "f4c29aa1c5dd4f2c99dfc4ce93626d28", "aab5d535803e466a875773bc1931c0fa", "1a724b9e3670416bad1acf269b7855ea" ] }, "id": "Bc4Y0Ky1FmFO", "outputId": "adcff6df-6835-43d1-f3e2-81b476c49255" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/21607 [00:00" ], "text/html": [] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n", "\n", " FAKE 0.98 0.97 0.98 8678\n", " REAL 0.98 0.99 0.98 12929\n", "\n", " accuracy 0.98 21607\n", " macro avg 0.98 0.98 0.98 21607\n", "weighted avg 0.98 0.98 0.98 21607\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "CONFUSION MATRIX" ], "metadata": { "id": "SSz_FyeZ465P" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import confusion_matrix\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "\n", "cm = confusion_matrix(true_labels, preds)\n", "labels = [\"FAKE\", \"REAL\"]\n", "\n", "plt.figure(figsize=(6, 5))\n", "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=labels, yticklabels=labels)\n", "plt.xlabel(\"Predicted\")\n", "plt.ylabel(\"Actual\")\n", "plt.title(\"Confusion Matrix\")\n", "plt.show()\n" ], "metadata": { "id": "l12_wLOiMOw1", "colab": { "base_uri": "https://localhost:8080/", "height": 487 }, "outputId": "ffa19b30-411b-408f-9eaa-435777d6303a" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "ROC curve" ], "metadata": { "id": "5TBz5qtA4uU5" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import roc_curve, auc\n", "import matplotlib.pyplot as plt\n", "\n", "# Probabilities for the positive class\n", "y_probs = predictions.predictions[:, 1]\n", "\n", "\n", "fpr, tpr, thresholds = roc_curve(true_labels, y_probs)\n", "roc_auc = auc(fpr, tpr)\n", "\n", "plt.figure(figsize=(7,6))\n", "plt.plot(fpr, tpr, color=\"darkorange\", lw=2, label=f\"ROC curve (AUC = {roc_auc:.2f})\")\n", "plt.plot([0, 1], [0, 1], color=\"navy\", lw=2, linestyle=\"--\")\n", "plt.xlabel(\"False Positive Rate\")\n", "plt.ylabel(\"True Positive Rate\")\n", "plt.title(\"Receiver Operating Characteristic\")\n", "plt.legend(loc=\"lower right\")\n", "plt.grid()\n", "plt.show()\n" ], "metadata": { "id": "qHIOpEnqMaYW", "colab": { "base_uri": "https://localhost:8080/", "height": 564 }, "outputId": "2ae63e18-aecf-449b-dabb-f43fcdf7162d" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "import torch\n", "import transformers\n", "import datasets\n", "import sklearn\n", "import numpy\n", "import pandas as pd\n", "\n", "print(\"\\nLibrary Versions:\\n\")\n", "print(f\"PyTorch: {torch.__version__}\")\n", "print(f\"Transformers: {transformers.__version__}\")\n", "print(f\"Datasets: {datasets.__version__}\")\n", "print(f\"Scikit-learn: {sklearn.__version__}\")\n", "print(f\"Numpy: {numpy.__version__}\")\n", "print(f\"Pandas: {pd.__version__}\")\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZoQYS1EoazGW", "outputId": "d9329a08-fada-4ebc-d9da-ac9861054003" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Library Versions:\n", "\n", "PyTorch: 2.6.0+cu124\n", "Transformers: 4.52.4\n", "Datasets: 2.14.4\n", "Scikit-learn: 1.6.1\n", "Numpy: 2.0.2\n", "Pandas: 2.2.2\n" ] } ] } ] }