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+ "_model_module_version": "1.5.0",
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+ "cells": [
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+ {
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+ "source": [
1394
+ "## Penjelasan BERT\n",
1395
+ "BERT (Bidirectional Encoder Representations from Transformers) adalah model bahasa yang sangat populer dalam pemrosesan bahasa alami (NLP). BERT menggunakan transformer yang membaca teks secara bidirectional, berbeda dengan model tradisional yang hanya membaca dari kiri ke kanan atau sebaliknya.\n",
1396
+ "\n",
1397
+ "### Pemahaman Dasar BERT\n",
1398
+ "* Arsitektur: BERT terdiri dari beberapa lapisan encoder dari transformer. Inputnya berupa urutan token (kata) yang dikonversi menjadi embedding.\n",
1399
+ "* Pre-training: BERT dilatih dalam dua tahap: Masked Language Modeling (MLM) dan Next Sentence Prediction (NSP).\n",
1400
+ "* Fine-tuning: BERT dapat disesuaikan untuk tugas spesifik seperti klasifikasi teks, penerjemahan, atau analisis sentimen."
1401
+ ],
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+ "metadata": {
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+ "id": "zlg9u3KJ_mHW"
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+ }
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+ },
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+ {
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+ "cell_type": "code",
1408
+ "source": [
1409
+ "import torch\n",
1410
+ "from torch.utils.data import DataLoader, Dataset\n",
1411
+ "from transformers import BertTokenizer, BertForSequenceClassification, AdamW\n",
1412
+ "from tqdm import tqdm\n",
1413
+ "\n",
1414
+ "# Dataset sederhana\n",
1415
+ "class SimpleDataset(Dataset):\n",
1416
+ " def __init__(self, texts, labels, tokenizer, max_len):\n",
1417
+ " self.texts = texts\n",
1418
+ " self.labels = labels\n",
1419
+ " self.tokenizer = tokenizer\n",
1420
+ " self.max_len = max_len\n",
1421
+ "\n",
1422
+ " def __len__(self):\n",
1423
+ " return len(self.texts)\n",
1424
+ "\n",
1425
+ " def __getitem__(self, idx):\n",
1426
+ " text = self.texts[idx]\n",
1427
+ " label = self.labels[idx]\n",
1428
+ " encoding = self.tokenizer.encode_plus(\n",
1429
+ " text,\n",
1430
+ " add_special_tokens=True,\n",
1431
+ " max_length=self.max_len,\n",
1432
+ " return_token_type_ids=False,\n",
1433
+ " padding='max_length',\n",
1434
+ " return_attention_mask=True,\n",
1435
+ " return_tensors='pt',\n",
1436
+ " truncation=True\n",
1437
+ " )\n",
1438
+ " return {\n",
1439
+ " 'input_ids': encoding['input_ids'].flatten(),\n",
1440
+ " 'attention_mask': encoding['attention_mask'].flatten(),\n",
1441
+ " 'labels': torch.tensor(label, dtype=torch.long)\n",
1442
+ " }\n",
1443
+ "\n",
1444
+ "# Contoh data kecil\n",
1445
+ "texts = [\n",
1446
+ " \"I love this movie!\",\n",
1447
+ " \"This film was terrible.\",\n",
1448
+ " \"Amazing acting and storyline.\",\n",
1449
+ " \"The plot was boring.\",\n",
1450
+ " \"Great visuals and sound!\",\n",
1451
+ " \"I hate this movie.\",\n",
1452
+ " \"Fantastic experience!\",\n",
1453
+ " \"It was a waste of time.\"\n",
1454
+ "]\n",
1455
+ "labels = [1, 0, 1, 0, 1, 0, 1, 0] # 1: Positif, 0: Negatif\n",
1456
+ "\n",
1457
+ "# Load tokenizer dan model BERT\n",
1458
+ "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
1459
+ "model = BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)\n",
1460
+ "\n",
1461
+ "# Membuat dataset dan DataLoader\n",
1462
+ "dataset = SimpleDataset(texts, labels, tokenizer, max_len=16)\n",
1463
+ "dataloader = DataLoader(dataset, batch_size=2, shuffle=True)\n",
1464
+ "\n",
1465
+ "# Memindahkan model ke GPU jika tersedia\n",
1466
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
1467
+ "model.to(device)\n",
1468
+ "\n",
1469
+ "# Optimizer\n",
1470
+ "optimizer = AdamW(model.parameters(), lr=2e-5)\n",
1471
+ "\n",
1472
+ "# Training loop sederhana\n",
1473
+ "model.train()\n",
1474
+ "for epoch in range(3): # Melatih selama 3 epoch\n",
1475
+ " print(f\"Epoch {epoch+1}\")\n",
1476
+ "\n",
1477
+ " for batch in tqdm(dataloader):\n",
1478
+ " # Memindahkan batch ke GPU jika tersedia\n",
1479
+ " input_ids = batch['input_ids'].to(device)\n",
1480
+ " attention_mask = batch['attention_mask'].to(device)\n",
1481
+ " labels = batch['labels'].to(device)\n",
1482
+ "\n",
1483
+ " # Forward pass\n",
1484
+ " outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
1485
+ " loss = outputs.loss\n",
1486
+ "\n",
1487
+ " # Backward pass\n",
1488
+ " optimizer.zero_grad()\n",
1489
+ " loss.backward()\n",
1490
+ " optimizer.step()\n",
1491
+ "\n",
1492
+ " print(f\"Loss: {loss.item()}\")\n",
1493
+ "\n",
1494
+ "# Evaluasi sederhana\n",
1495
+ "model.eval()\n",
1496
+ "with torch.no_grad():\n",
1497
+ " for i, text in enumerate(texts):\n",
1498
+ " encoding = tokenizer.encode_plus(\n",
1499
+ " text,\n",
1500
+ " add_special_tokens=True,\n",
1501
+ " max_length=16,\n",
1502
+ " return_token_type_ids=False,\n",
1503
+ " padding='max_length',\n",
1504
+ " return_attention_mask=True,\n",
1505
+ " return_tensors='pt',\n",
1506
+ " truncation=True\n",
1507
+ " )\n",
1508
+ " input_ids = encoding['input_ids'].to(device)\n",
1509
+ " attention_mask = encoding['attention_mask'].to(device)\n",
1510
+ "\n",
1511
+ " output = model(input_ids, attention_mask=attention_mask)\n",
1512
+ " prediction = torch.argmax(output.logits, dim=-1).item()\n",
1513
+ "\n",
1514
+ " label = \"Positive\" if prediction == 1 else \"Negative\"\n",
1515
+ " print(f\"Text: {text}\\nPredicted Label: {label}\\n\")\n"
1516
+ ],
1517
+ "metadata": {
1518
+ "colab": {
1519
+ "base_uri": "https://localhost:8080/",
1520
+ "height": 1000,
1521
+ "referenced_widgets": [
1522
+ "4debe50db88e48fa9a083d691aad2bd5",
1523
+ "21745f14645b4786a85261621411be2f",
1524
+ "6206fd713e3545bbbcd02952c23711f3",
1525
+ "27ebe53cd42445c9943c11ea627da7d9",
1526
+ "1af147c6ea114527957c9e2a2234e68e",
1527
+ "010bc2703d564b39b65c42a9a773ebe6",
1528
+ "61de1507580c493782eab17a91b71b6b",
1529
+ "76ca4e2a2a624d0981adf03e15493651",
1530
+ "dd78effe9fdc4365befb3c446ea9b5f7",
1531
+ "0fbd8ff0d95e4df1bb1331599f8f653b",
1532
+ "e529c1703d61450b8534d2a0a2123645",
1533
+ "f5825285fb4743e6a62aad8745d13371",
1534
+ "f6e41d5dac874fdcb7d631969646ce33",
1535
+ "7e97c7a956984d30b7a5d9432208b2c6",
1536
+ "9d94f4a9ee904a8f9e069b105571cc36",
1537
+ "908871f8d1bb4bedb135fd55cbbbd0ab",
1538
+ "84c50c244b7a4a9886b70b0ee266d053",
1539
+ "7802d3cce31944e983f814a34e5e3319",
1540
+ "ede6611a3b244f9a957dd5d5010e1b26",
1541
+ "bd03f4735d5e40e8817a2f019902c104",
1542
+ "1c739bbc97ae4a67bb3722863200e1b1",
1543
+ "7b787aca59a14b0b8ae33f66c0f99d78",
1544
+ "1e2cdb9ebda24fd38995a2f5b8d9605c",
1545
+ "69a259273b4340d48ad5a7b382918596",
1546
+ "f3b21ba904fa4bc387b632051accc067",
1547
+ "880b1be1091440a780a49f632382f6a9",
1548
+ "2893974093f64d49811ab2590888bed3",
1549
+ "e8f8658399e64e14b8b644fa25156e42",
1550
+ "680cd806532b40b2894682ec9915bbaf",
1551
+ "5ccccb8c8491468ead3da3618a8b43d2",
1552
+ "cf44e869239a4e4e86eff7eaa04e23b2",
1553
+ "87060f8fdf7e4cd1a72e7866d87d3824",
1554
+ "f91f43e7cd33413b8effe56fd285bf50",
1555
+ "24642e27df804bc6a1ef3ff9e0480b8d",
1556
+ "6b6fa065b6454c538a1e8a9178ad1ba3",
1557
+ "814babcb10244851a7e1ab157ff76768",
1558
+ "e3dfe0725f77407bb4e63bd289b5ad0e",
1559
+ "6a2722be3b504eb7906e9f487a107441",
1560
+ "bf8cce2d8c07425abc4ba50e27ef79c6",
1561
+ "0750e23cddd64a699d56af4c6cdd59e7",
1562
+ "741d6b125d5b4d179c7deeefdeb1cdbb",
1563
+ "a54383977c4147d4a6ea713d282f5625",
1564
+ "f9f67b577746465b8125dda609933c0c",
1565
+ "978fa96cbc724e2a95b1cdb6c986a75d"
1566
+ ]
1567
+ },
1568
+ "id": "yCNRpB1WVNQm",
1569
+ "outputId": "c982edd7-088f-400e-889c-5f74b0447a76"
1570
+ },
1571
+ "execution_count": 26,
1572
+ "outputs": [
1573
+ {
1574
+ "output_type": "display_data",
1575
+ "data": {
1576
+ "text/plain": [
1577
+ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]"
1578
+ ],
1579
+ "application/vnd.jupyter.widget-view+json": {
1580
+ "version_major": 2,
1581
+ "version_minor": 0,
1582
+ "model_id": "4debe50db88e48fa9a083d691aad2bd5"
1583
+ }
1584
+ },
1585
+ "metadata": {}
1586
+ },
1587
+ {
1588
+ "output_type": "display_data",
1589
+ "data": {
1590
+ "text/plain": [
1591
+ "vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
1592
+ ],
1593
+ "application/vnd.jupyter.widget-view+json": {
1594
+ "version_major": 2,
1595
+ "version_minor": 0,
1596
+ "model_id": "f5825285fb4743e6a62aad8745d13371"
1597
+ }
1598
+ },
1599
+ "metadata": {}
1600
+ },
1601
+ {
1602
+ "output_type": "display_data",
1603
+ "data": {
1604
+ "text/plain": [
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+ "tokenizer.json: 0%| | 0.00/466k [00:00<?, ?B/s]"
1606
+ ],
1607
+ "application/vnd.jupyter.widget-view+json": {
1608
+ "version_major": 2,
1609
+ "version_minor": 0,
1610
+ "model_id": "1e2cdb9ebda24fd38995a2f5b8d9605c"
1611
+ }
1612
+ },
1613
+ "metadata": {}
1614
+ },
1615
+ {
1616
+ "output_type": "display_data",
1617
+ "data": {
1618
+ "text/plain": [
1619
+ "model.safetensors: 60%|#####9 | 262M/440M [00:00<?, ?B/s]"
1620
+ ],
1621
+ "application/vnd.jupyter.widget-view+json": {
1622
+ "version_major": 2,
1623
+ "version_minor": 0,
1624
+ "model_id": "24642e27df804bc6a1ef3ff9e0480b8d"
1625
+ }
1626
+ },
1627
+ "metadata": {}
1628
+ },
1629
+ {
1630
+ "output_type": "stream",
1631
+ "name": "stderr",
1632
+ "text": [
1633
+ "Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
1634
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
1635
+ ]
1636
+ },
1637
+ {
1638
+ "output_type": "stream",
1639
+ "name": "stdout",
1640
+ "text": [
1641
+ "Epoch 1\n"
1642
+ ]
1643
+ },
1644
+ {
1645
+ "output_type": "stream",
1646
+ "name": "stderr",
1647
+ "text": [
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+ "\n",
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+ " 0%| | 0/4 [00:00<?, ?it/s]\u001b[A\n",
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+ " 25%|██▌ | 1/4 [00:02<00:06, 2.16s/it]\u001b[A\n",
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+ " 50%|█████ | 2/4 [00:02<00:01, 1.00it/s]\u001b[A\n",
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+ " 75%|███████▌ | 3/4 [00:02<00:00, 1.70it/s]\u001b[A\n",
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+ "100%|██████████| 4/4 [00:02<00:00, 1.57it/s]\n"
1654
+ ]
1655
+ },
1656
+ {
1657
+ "output_type": "stream",
1658
+ "name": "stdout",
1659
+ "text": [
1660
+ "Loss: 0.7502166032791138\n",
1661
+ "Epoch 2\n"
1662
+ ]
1663
+ },
1664
+ {
1665
+ "output_type": "stream",
1666
+ "name": "stderr",
1667
+ "text": [
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+ "\n",
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+ " 0%| | 0/4 [00:00<?, ?it/s]\u001b[A\n",
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+ " 25%|██▌ | 1/4 [00:00<00:00, 8.83it/s]\u001b[A\n",
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+ " 50%|█████ | 2/4 [00:00<00:00, 7.89it/s]\u001b[A\n",
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+ " 75%|███████▌ | 3/4 [00:00<00:00, 8.55it/s]\u001b[A\n",
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+ "100%|██████████| 4/4 [00:00<00:00, 8.02it/s]\n"
1674
+ ]
1675
+ },
1676
+ {
1677
+ "output_type": "stream",
1678
+ "name": "stdout",
1679
+ "text": [
1680
+ "Loss: 0.7814399003982544\n",
1681
+ "Epoch 3\n"
1682
+ ]
1683
+ },
1684
+ {
1685
+ "output_type": "stream",
1686
+ "name": "stderr",
1687
+ "text": [
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+ "\n",
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+ " 0%| | 0/4 [00:00<?, ?it/s]\u001b[A\n",
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+ " 25%|██▌ | 1/4 [00:00<00:00, 7.50it/s]\u001b[A\n",
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+ " 50%|█████ | 2/4 [00:00<00:00, 7.19it/s]\u001b[A\n",
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+ " 75%|███████▌ | 3/4 [00:00<00:00, 6.00it/s]\u001b[A\n",
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+ "100%|██████████| 4/4 [00:00<00:00, 6.32it/s]\n"
1694
+ ]
1695
+ },
1696
+ {
1697
+ "output_type": "stream",
1698
+ "name": "stdout",
1699
+ "text": [
1700
+ "Loss: 0.4791209101676941\n",
1701
+ "Text: I love this movie!\n",
1702
+ "Predicted Label: Positive\n",
1703
+ "\n",
1704
+ "Text: This film was terrible.\n",
1705
+ "Predicted Label: Negative\n",
1706
+ "\n",
1707
+ "Text: Amazing acting and storyline.\n",
1708
+ "Predicted Label: Positive\n",
1709
+ "\n",
1710
+ "Text: The plot was boring.\n",
1711
+ "Predicted Label: Negative\n",
1712
+ "\n",
1713
+ "Text: Great visuals and sound!\n",
1714
+ "Predicted Label: Positive\n",
1715
+ "\n",
1716
+ "Text: I hate this movie.\n",
1717
+ "Predicted Label: Negative\n",
1718
+ "\n",
1719
+ "Text: Fantastic experience!\n",
1720
+ "Predicted Label: Positive\n",
1721
+ "\n",
1722
+ "Text: It was a waste of time.\n",
1723
+ "Predicted Label: Negative\n",
1724
+ "\n"
1725
+ ]
1726
+ }
1727
+ ]
1728
+ }
1729
+ ]
1730
+ }