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Ataur77
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restaurants-chatbot-t5

Safetensors
t5
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Files changed (8) hide show
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  2. config.json +60 -0
  3. generation_config.json +7 -0
  4. model.safetensors +3 -0
  5. mt5_bangla_chatbot.ipynb +1831 -0
  6. special_tokens_map.json +125 -0
  7. spiece.model +3 -0
  8. tokenizer_config.json +941 -0
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+ "3 প্রশ্ন: ছুটির দিনে কি খোলা থাকে? \n",
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+ "0 হ্যাঁ, আমাদের হটলাইন নম্বরে ফোন করলেই রিজার্ভ ... \n",
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+ "4 খাবারের দাম ১০০ টাকা থেকে শুরু। "
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1207
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1208
+ "\n",
1209
+ " async function convertToInteractive(key) {\n",
1210
+ " const element = document.querySelector('#df-725b8b89-a10b-4045-8026-84dda36f435e');\n",
1211
+ " const dataTable =\n",
1212
+ " await google.colab.kernel.invokeFunction('convertToInteractive',\n",
1213
+ " [key], {});\n",
1214
+ " if (!dataTable) return;\n",
1215
+ "\n",
1216
+ " const docLinkHtml = 'Like what you see? Visit the ' +\n",
1217
+ " '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
1218
+ " + ' to learn more about interactive tables.';\n",
1219
+ " element.innerHTML = '';\n",
1220
+ " dataTable['output_type'] = 'display_data';\n",
1221
+ " await google.colab.output.renderOutput(dataTable, element);\n",
1222
+ " const docLink = document.createElement('div');\n",
1223
+ " docLink.innerHTML = docLinkHtml;\n",
1224
+ " element.appendChild(docLink);\n",
1225
+ " }\n",
1226
+ " </script>\n",
1227
+ " </div>\n",
1228
+ "\n",
1229
+ "\n",
1230
+ " <div id=\"df-137925ad-495d-405f-947c-b00844c0cbb0\">\n",
1231
+ " <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-137925ad-495d-405f-947c-b00844c0cbb0')\"\n",
1232
+ " title=\"Suggest charts\"\n",
1233
+ " style=\"display:none;\">\n",
1234
+ "\n",
1235
+ "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
1236
+ " width=\"24px\">\n",
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+ " <g>\n",
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+ " <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
1239
+ " </g>\n",
1240
+ "</svg>\n",
1241
+ " </button>\n",
1242
+ "\n",
1243
+ "<style>\n",
1244
+ " .colab-df-quickchart {\n",
1245
+ " --bg-color: #E8F0FE;\n",
1246
+ " --fill-color: #1967D2;\n",
1247
+ " --hover-bg-color: #E2EBFA;\n",
1248
+ " --hover-fill-color: #174EA6;\n",
1249
+ " --disabled-fill-color: #AAA;\n",
1250
+ " --disabled-bg-color: #DDD;\n",
1251
+ " }\n",
1252
+ "\n",
1253
+ " [theme=dark] .colab-df-quickchart {\n",
1254
+ " --bg-color: #3B4455;\n",
1255
+ " --fill-color: #D2E3FC;\n",
1256
+ " --hover-bg-color: #434B5C;\n",
1257
+ " --hover-fill-color: #FFFFFF;\n",
1258
+ " --disabled-bg-color: #3B4455;\n",
1259
+ " --disabled-fill-color: #666;\n",
1260
+ " }\n",
1261
+ "\n",
1262
+ " .colab-df-quickchart {\n",
1263
+ " background-color: var(--bg-color);\n",
1264
+ " border: none;\n",
1265
+ " border-radius: 50%;\n",
1266
+ " cursor: pointer;\n",
1267
+ " display: none;\n",
1268
+ " fill: var(--fill-color);\n",
1269
+ " height: 32px;\n",
1270
+ " padding: 0;\n",
1271
+ " width: 32px;\n",
1272
+ " }\n",
1273
+ "\n",
1274
+ " .colab-df-quickchart:hover {\n",
1275
+ " background-color: var(--hover-bg-color);\n",
1276
+ " box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
1277
+ " fill: var(--button-hover-fill-color);\n",
1278
+ " }\n",
1279
+ "\n",
1280
+ " .colab-df-quickchart-complete:disabled,\n",
1281
+ " .colab-df-quickchart-complete:disabled:hover {\n",
1282
+ " background-color: var(--disabled-bg-color);\n",
1283
+ " fill: var(--disabled-fill-color);\n",
1284
+ " box-shadow: none;\n",
1285
+ " }\n",
1286
+ "\n",
1287
+ " .colab-df-spinner {\n",
1288
+ " border: 2px solid var(--fill-color);\n",
1289
+ " border-color: transparent;\n",
1290
+ " border-bottom-color: var(--fill-color);\n",
1291
+ " animation:\n",
1292
+ " spin 1s steps(1) infinite;\n",
1293
+ " }\n",
1294
+ "\n",
1295
+ " @keyframes spin {\n",
1296
+ " 0% {\n",
1297
+ " border-color: transparent;\n",
1298
+ " border-bottom-color: var(--fill-color);\n",
1299
+ " border-left-color: var(--fill-color);\n",
1300
+ " }\n",
1301
+ " 20% {\n",
1302
+ " border-color: transparent;\n",
1303
+ " border-left-color: var(--fill-color);\n",
1304
+ " border-top-color: var(--fill-color);\n",
1305
+ " }\n",
1306
+ " 30% {\n",
1307
+ " border-color: transparent;\n",
1308
+ " border-left-color: var(--fill-color);\n",
1309
+ " border-top-color: var(--fill-color);\n",
1310
+ " border-right-color: var(--fill-color);\n",
1311
+ " }\n",
1312
+ " 40% {\n",
1313
+ " border-color: transparent;\n",
1314
+ " border-right-color: var(--fill-color);\n",
1315
+ " border-top-color: var(--fill-color);\n",
1316
+ " }\n",
1317
+ " 60% {\n",
1318
+ " border-color: transparent;\n",
1319
+ " border-right-color: var(--fill-color);\n",
1320
+ " }\n",
1321
+ " 80% {\n",
1322
+ " border-color: transparent;\n",
1323
+ " border-right-color: var(--fill-color);\n",
1324
+ " border-bottom-color: var(--fill-color);\n",
1325
+ " }\n",
1326
+ " 90% {\n",
1327
+ " border-color: transparent;\n",
1328
+ " border-bottom-color: var(--fill-color);\n",
1329
+ " }\n",
1330
+ " }\n",
1331
+ "</style>\n",
1332
+ "\n",
1333
+ " <script>\n",
1334
+ " async function quickchart(key) {\n",
1335
+ " const quickchartButtonEl =\n",
1336
+ " document.querySelector('#' + key + ' button');\n",
1337
+ " quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
1338
+ " quickchartButtonEl.classList.add('colab-df-spinner');\n",
1339
+ " try {\n",
1340
+ " const charts = await google.colab.kernel.invokeFunction(\n",
1341
+ " 'suggestCharts', [key], {});\n",
1342
+ " } catch (error) {\n",
1343
+ " console.error('Error during call to suggestCharts:', error);\n",
1344
+ " }\n",
1345
+ " quickchartButtonEl.classList.remove('colab-df-spinner');\n",
1346
+ " quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
1347
+ " }\n",
1348
+ " (() => {\n",
1349
+ " let quickchartButtonEl =\n",
1350
+ " document.querySelector('#df-137925ad-495d-405f-947c-b00844c0cbb0 button');\n",
1351
+ " quickchartButtonEl.style.display =\n",
1352
+ " google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
1353
+ " })();\n",
1354
+ " </script>\n",
1355
+ " </div>\n",
1356
+ "\n",
1357
+ " </div>\n",
1358
+ " </div>\n"
1359
+ ],
1360
+ "application/vnd.google.colaboratory.intrinsic+json": {
1361
+ "type": "dataframe",
1362
+ "variable_name": "df",
1363
+ "summary": "{\n \"name\": \"df\",\n \"rows\": 202,\n \"fields\": [\n {\n \"column\": \"input\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 202,\n \"samples\": [\n \"\\u09aa\\u09cd\\u09b0\\u09b6\\u09cd\\u09a8: \\u0995\\u09ab\\u09bf \\u09a1\\u09bf\\u0995\\u09cd\\u09af\\u09be\\u09ab?\",\n \"\\u09aa\\u09cd\\u09b0\\u09b6\\u09cd\\u09a8: \\u09aa\\u09b0\\u09bf\\u09b7\\u09cd\\u0995\\u09be\\u09b0 \\u09aa\\u09b0\\u09bf\\u099a\\u09cd\\u099b\\u09a8\\u09cd\\u09a8\\u09a4\\u09be \\u0995\\u09c7\\u09ae\\u09a8?\",\n \"\\u09aa\\u09cd\\u09b0\\u09b6\\u09cd\\u09a8: \\u0997\\u09cd\\u09b0\\u09c1\\u09aa \\u09ac\\u09c1\\u0995\\u09bf\\u0982 \\u098f\\u09b0 \\u09b8\\u09c1\\u09ac\\u09bf\\u09a7\\u09be \\u0986\\u099b\\u09c7?\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"target\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 202,\n \"samples\": [\n \"\\u09b9\\u09cd\\u09af\\u09be\\u0981, \\u09a1\\u09bf\\u0995\\u09cd\\u09af\\u09be\\u09ab \\u0995\\u09ab\\u09bf\\u09b0 \\u09ac\\u09cd\\u09af\\u09ac\\u09b8\\u09cd\\u09a5\\u09be \\u0986\\u099b\\u09c7\\u0964\",\n \"\\u0986\\u09ae\\u09b0\\u09be \\u09b8\\u09b0\\u09cd\\u09ac\\u09cb\\u099a\\u09cd\\u099a \\u09ae\\u09be\\u09a8\\u09c7\\u09b0 \\u09aa\\u09b0\\u09bf\\u09b7\\u09cd\\u0995\\u09be\\u09b0 \\u09aa\\u09b0\\u09bf\\u099a\\u09cd\\u099b\\u09a8\\u09cd\\u09a8\\u09a4\\u09be \\u09a8\\u09bf\\u09b6\\u09cd\\u099a\\u09bf\\u09a4 \\u0995\\u09b0\\u09bf\\u0964\",\n \"\\u09b9\\u09cd\\u09af\\u09be\\u0981, \\u09e7\\u09e6 \\u099c\\u09a8\\u09c7\\u09b0 \\u09ac\\u09c7\\u09b6\\u09bf \\u0997\\u09cd\\u09b0\\u09c1\\u09aa\\u09c7\\u09b0 \\u099c\\u09a8\\u09cd\\u09af \\u09aa\\u09cd\\u09b0\\u09bf-\\u09ac\\u09c1\\u0995\\u09bf\\u0982 \\u0995\\u09b0\\u09a4\\u09c7 \\u09b9\\u09ac\\u09c7\\u0964\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
1364
+ }
1365
+ },
1366
+ "metadata": {},
1367
+ "execution_count": 6
1368
+ }
1369
+ ]
1370
+ },
1371
+ {
1372
+ "cell_type": "code",
1373
+ "source": [
1374
+ "\n",
1375
+ "# 🔤 Tokenization\n",
1376
+ "from transformers import AutoTokenizer\n",
1377
+ "from datasets import Dataset\n",
1378
+ "\n",
1379
+ "model_name = \"google/mt5-small\"\n",
1380
+ "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
1381
+ "\n",
1382
+ "max_input_length = 128\n",
1383
+ "max_target_length = 128\n",
1384
+ "\n",
1385
+ "df['input'] = df['input'].apply(lambda x: \"<bn> \" + x)\n",
1386
+ "df['target'] = df['target'].apply(lambda x: \"<bn> \" + x)\n",
1387
+ "\n",
1388
+ "dataset = Dataset.from_pandas(df)\n",
1389
+ "\n",
1390
+ "def tokenize(batch):\n",
1391
+ " input_enc = tokenizer(batch[\"input\"], padding=\"max_length\", truncation=True, max_length=max_input_length)\n",
1392
+ " target_enc = tokenizer(batch[\"target\"], padding=\"max_length\", truncation=True, max_length=max_target_length)\n",
1393
+ "\n",
1394
+ " labels = target_enc[\"input_ids\"]\n",
1395
+ " labels = [[(token if token != tokenizer.pad_token_id else -100) for token in label] for label in labels]\n",
1396
+ "\n",
1397
+ " print(\"Sample labels:\", labels[0]) # Debug print\n",
1398
+ " input_enc[\"labels\"] = labels\n",
1399
+ " return input_enc\n",
1400
+ "\n",
1401
+ "\n",
1402
+ "tokenized_dataset = dataset.map(tokenize, batched=True)\n",
1403
+ "\n"
1404
+ ],
1405
+ "metadata": {
1406
+ "colab": {
1407
+ "base_uri": "https://localhost:8080/",
1408
+ "height": 121,
1409
+ "referenced_widgets": [
1410
+ "6731bf4c44b04418a3e92162bf339b4d",
1411
+ "7f292c9fb40a43cea617cb2af8cf6d16",
1412
+ "5cbd9bb2715d46e7ba22e559d5b75c96",
1413
+ "2ab10bd157ee4e568c10374e4720e676",
1414
+ "e7f1d0472dbc41ababc2d4153f13d2b4",
1415
+ "abb5f0f1c1694961957f32824d14ccf5",
1416
+ "acad57f3134c46e7b08af70d9d72ece9",
1417
+ "0fd2902a45474e09832d43da534e5540",
1418
+ "74f3cd48c2504464a2ef14f9c6f7730b",
1419
+ "0031511abb9b459a9b9e1cded6186287",
1420
+ "b30964047ced437aa46dd0b654270bbd"
1421
+ ]
1422
+ },
1423
+ "id": "otLZo4OK0sxK",
1424
+ "outputId": "f92c8307-055b-47ff-b9c3-77f6d5867e37"
1425
+ },
1426
+ "execution_count": 32,
1427
+ "outputs": [
1428
+ {
1429
+ "output_type": "stream",
1430
+ "name": "stderr",
1431
+ "text": [
1432
+ "/usr/local/lib/python3.11/dist-packages/transformers/convert_slow_tokenizer.py:564: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
1433
+ " warnings.warn(\n"
1434
+ ]
1435
+ },
1436
+ {
1437
+ "output_type": "display_data",
1438
+ "data": {
1439
+ "text/plain": [
1440
+ "Map: 0%| | 0/202 [00:00<?, ? examples/s]"
1441
+ ],
1442
+ "application/vnd.jupyter.widget-view+json": {
1443
+ "version_major": 2,
1444
+ "version_minor": 0,
1445
+ "model_id": "6731bf4c44b04418a3e92162bf339b4d"
1446
+ }
1447
+ },
1448
+ "metadata": {}
1449
+ },
1450
+ {
1451
+ "output_type": "stream",
1452
+ "name": "stdout",
1453
+ "text": [
1454
+ "Sample labels: [1042, 316, 272, 669, 1042, 316, 272, 669, 1042, 316, 272, 669, 140227, 8460, 261, 5998, 9977, 2326, 4402, 67283, 259, 1152, 22250, 994, 259, 40780, 1152, 2212, 2496, 1413, 68488, 16506, 96544, 259, 5399, 17275, 378, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]\n"
1455
+ ]
1456
+ }
1457
+ ]
1458
+ },
1459
+ {
1460
+ "cell_type": "code",
1461
+ "source": [
1462
+ "print(tokenized_dataset[0][\"labels\"])\n",
1463
+ "print(\"Number of valid tokens:\", sum(t != -100 for t in tokenized_dataset[0][\"labels\"]))\n"
1464
+ ],
1465
+ "metadata": {
1466
+ "colab": {
1467
+ "base_uri": "https://localhost:8080/"
1468
+ },
1469
+ "id": "PvVlr7u37yGu",
1470
+ "outputId": "b8b1dcb4-a613-4865-ee4f-c359fd021d41"
1471
+ },
1472
+ "execution_count": 33,
1473
+ "outputs": [
1474
+ {
1475
+ "output_type": "stream",
1476
+ "name": "stdout",
1477
+ "text": [
1478
+ "[1042, 316, 272, 669, 1042, 316, 272, 669, 1042, 316, 272, 669, 140227, 8460, 261, 5998, 9977, 2326, 4402, 67283, 259, 1152, 22250, 994, 259, 40780, 1152, 2212, 2496, 1413, 68488, 16506, 96544, 259, 5399, 17275, 378, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100]\n",
1479
+ "Number of valid tokens: 38\n"
1480
+ ]
1481
+ }
1482
+ ]
1483
+ },
1484
+ {
1485
+ "cell_type": "code",
1486
+ "source": [
1487
+ "\n",
1488
+ "from datasets import Dataset\n",
1489
+ "from datasets import load_metric\n",
1490
+ "\n",
1491
+ "# Load dataset from pandas df\n",
1492
+ "dataset = Dataset.from_pandas(df)\n",
1493
+ "\n",
1494
+ "# Split\n",
1495
+ "split_dataset = dataset.train_test_split(test_size=0.2, seed=42)\n",
1496
+ "train_dataset = split_dataset['train']\n",
1497
+ "eval_dataset = split_dataset['test']\n",
1498
+ "\n",
1499
+ "# Tokenize function as before\n",
1500
+ "def tokenize(batch):\n",
1501
+ " input_enc = tokenizer(batch['input'], padding=\"max_length\", truncation=True, max_length=64)\n",
1502
+ " target_enc = tokenizer(batch['target'], padding=\"max_length\", truncation=True, max_length=64)\n",
1503
+ " input_enc['labels'] = target_enc['input_ids']\n",
1504
+ " return input_enc\n",
1505
+ "\n",
1506
+ "train_dataset = train_dataset.map(tokenize, batched=True)\n",
1507
+ "eval_dataset = eval_dataset.map(tokenize, batched=True)\n",
1508
+ "\n",
1509
+ "\n",
1510
+ "\n"
1511
+ ],
1512
+ "metadata": {
1513
+ "colab": {
1514
+ "base_uri": "https://localhost:8080/",
1515
+ "height": 116,
1516
+ "referenced_widgets": [
1517
+ "707c911f3f284ca694434c66a5d32164",
1518
+ "5d09f1b981fe40408a510d10351beead",
1519
+ "ef27890e503a449f9583d960f244cfb8",
1520
+ "c626325b0dee40ee861315bb5fb176c7",
1521
+ "643bd0b694994321b4c83ac36448a020",
1522
+ "4531e52322314195bb3ab8ae68d3a99e",
1523
+ "81ecb5431f2b4d53811abc584b30de70",
1524
+ "2a8bea160baf4fdb94d36ec7b2e48abc",
1525
+ "ac0daaf6c7634a3ebe224a6ec0d53fed",
1526
+ "7a2e64f9ab664a0884e176ad6e126ce3",
1527
+ "b1eabce5ada443bf803773ba90f03d74",
1528
+ "24f4f3f3e2c54bc789d30d363a93095e",
1529
+ "b220dd31400349b1a09e1244574b8fd2",
1530
+ "cdce2e98b8584f12a533144e07387eaa",
1531
+ "db62ab11b6464508baa4799e7c1c1e16",
1532
+ "174904c1cbb3475193a99498f870ec21",
1533
+ "9fe9296adae642d380e09e71dc9bfce0",
1534
+ "3a9b29c6754b4929bc2849addf5470ab",
1535
+ "f44a5f3e7f8b4d2c947a5596e4915124",
1536
+ "56d13847df33476da1d463470ba84722",
1537
+ "4532aae1ae1842a19695c55bf13458c2",
1538
+ "66596e16abbf4561997ec9689b08fe01"
1539
+ ]
1540
+ },
1541
+ "id": "p80lltzD1-Rj",
1542
+ "outputId": "fcf0f192-e104-4ace-b963-533dcc6d453c"
1543
+ },
1544
+ "execution_count": 28,
1545
+ "outputs": [
1546
+ {
1547
+ "output_type": "display_data",
1548
+ "data": {
1549
+ "text/plain": [
1550
+ "Map: 0%| | 0/161 [00:00<?, ? examples/s]"
1551
+ ],
1552
+ "application/vnd.jupyter.widget-view+json": {
1553
+ "version_major": 2,
1554
+ "version_minor": 0,
1555
+ "model_id": "707c911f3f284ca694434c66a5d32164"
1556
+ }
1557
+ },
1558
+ "metadata": {}
1559
+ },
1560
+ {
1561
+ "output_type": "display_data",
1562
+ "data": {
1563
+ "text/plain": [
1564
+ "Map: 0%| | 0/41 [00:00<?, ? examples/s]"
1565
+ ],
1566
+ "application/vnd.jupyter.widget-view+json": {
1567
+ "version_major": 2,
1568
+ "version_minor": 0,
1569
+ "model_id": "24f4f3f3e2c54bc789d30d363a93095e"
1570
+ }
1571
+ },
1572
+ "metadata": {}
1573
+ },
1574
+ {
1575
+ "output_type": "stream",
1576
+ "name": "stderr",
1577
+ "text": [
1578
+ "/tmp/ipython-input-28-2680285985.py:22: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
1579
+ " trainer = Trainer(\n"
1580
+ ]
1581
+ }
1582
+ ]
1583
+ },
1584
+ {
1585
+ "cell_type": "code",
1586
+ "source": [
1587
+ "\n",
1588
+ "# 🧠 Load Pretrained Model\n",
1589
+ "from transformers import AutoModelForSeq2SeqLM\n",
1590
+ "\n",
1591
+ "model = AutoModelForSeq2SeqLM.from_pretrained(model_name)\n"
1592
+ ],
1593
+ "metadata": {
1594
+ "id": "cOzdYogx2Lvr"
1595
+ },
1596
+ "execution_count": 21,
1597
+ "outputs": []
1598
+ },
1599
+ {
1600
+ "cell_type": "code",
1601
+ "source": [
1602
+ "\n",
1603
+ "# ⚙️ Training Setup\n",
1604
+ "from transformers import TrainingArguments, Trainer\n",
1605
+ "\n",
1606
+ "training_args = TrainingArguments(\n",
1607
+ " output_dir=\"./bengali-mt5-chatbot\",\n",
1608
+ " per_device_train_batch_size=4,\n",
1609
+ " num_train_epochs=10,\n",
1610
+ " learning_rate=5e-5,\n",
1611
+ " logging_steps=10,\n",
1612
+ " save_total_limit=2,\n",
1613
+ " save_steps=50,\n",
1614
+ " eval_strategy=\"steps\",\n",
1615
+ " eval_steps=50,\n",
1616
+ " fp16=True,\n",
1617
+ ")\n",
1618
+ "\n",
1619
+ "trainer = Trainer(\n",
1620
+ " model=model,\n",
1621
+ " args=training_args,\n",
1622
+ " train_dataset=train_dataset,\n",
1623
+ " eval_dataset=eval_dataset,\n",
1624
+ " tokenizer=tokenizer\n",
1625
+ ")\n",
1626
+ "\n",
1627
+ "trainer.train()\n"
1628
+ ],
1629
+ "metadata": {
1630
+ "colab": {
1631
+ "base_uri": "https://localhost:8080/",
1632
+ "height": 396
1633
+ },
1634
+ "id": "TJUlJySX2Opk",
1635
+ "outputId": "14486d25-25a3-4537-a2e2-2fde681e92f9"
1636
+ },
1637
+ "execution_count": 30,
1638
+ "outputs": [
1639
+ {
1640
+ "output_type": "stream",
1641
+ "name": "stderr",
1642
+ "text": [
1643
+ "/tmp/ipython-input-30-1247104863.py:17: FutureWarning: `tokenizer` is deprecated and will be removed in version 5.0.0 for `Trainer.__init__`. Use `processing_class` instead.\n",
1644
+ " trainer = Trainer(\n"
1645
+ ]
1646
+ },
1647
+ {
1648
+ "output_type": "display_data",
1649
+ "data": {
1650
+ "text/plain": [
1651
+ "<IPython.core.display.HTML object>"
1652
+ ],
1653
+ "text/html": [
1654
+ "\n",
1655
+ " <div>\n",
1656
+ " \n",
1657
+ " <progress value='410' max='410' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
1658
+ " [410/410 06:44, Epoch 10/10]\n",
1659
+ " </div>\n",
1660
+ " <table border=\"1\" class=\"dataframe\">\n",
1661
+ " <thead>\n",
1662
+ " <tr style=\"text-align: left;\">\n",
1663
+ " <th>Step</th>\n",
1664
+ " <th>Training Loss</th>\n",
1665
+ " <th>Validation Loss</th>\n",
1666
+ " </tr>\n",
1667
+ " </thead>\n",
1668
+ " <tbody>\n",
1669
+ " <tr>\n",
1670
+ " <td>50</td>\n",
1671
+ " <td>0.000000</td>\n",
1672
+ " <td>nan</td>\n",
1673
+ " </tr>\n",
1674
+ " <tr>\n",
1675
+ " <td>100</td>\n",
1676
+ " <td>0.000000</td>\n",
1677
+ " <td>nan</td>\n",
1678
+ " </tr>\n",
1679
+ " <tr>\n",
1680
+ " <td>150</td>\n",
1681
+ " <td>0.000000</td>\n",
1682
+ " <td>nan</td>\n",
1683
+ " </tr>\n",
1684
+ " <tr>\n",
1685
+ " <td>200</td>\n",
1686
+ " <td>0.000000</td>\n",
1687
+ " <td>nan</td>\n",
1688
+ " </tr>\n",
1689
+ " <tr>\n",
1690
+ " <td>250</td>\n",
1691
+ " <td>0.000000</td>\n",
1692
+ " <td>nan</td>\n",
1693
+ " </tr>\n",
1694
+ " <tr>\n",
1695
+ " <td>300</td>\n",
1696
+ " <td>0.000000</td>\n",
1697
+ " <td>nan</td>\n",
1698
+ " </tr>\n",
1699
+ " <tr>\n",
1700
+ " <td>350</td>\n",
1701
+ " <td>0.000000</td>\n",
1702
+ " <td>nan</td>\n",
1703
+ " </tr>\n",
1704
+ " <tr>\n",
1705
+ " <td>400</td>\n",
1706
+ " <td>0.000000</td>\n",
1707
+ " <td>nan</td>\n",
1708
+ " </tr>\n",
1709
+ " </tbody>\n",
1710
+ "</table><p>"
1711
+ ]
1712
+ },
1713
+ "metadata": {}
1714
+ },
1715
+ {
1716
+ "output_type": "execute_result",
1717
+ "data": {
1718
+ "text/plain": [
1719
+ "TrainOutput(global_step=410, training_loss=0.0, metrics={'train_runtime': 403.0612, 'train_samples_per_second': 3.994, 'train_steps_per_second': 1.017, 'total_flos': 106410875289600.0, 'train_loss': 0.0, 'epoch': 10.0})"
1720
+ ]
1721
+ },
1722
+ "metadata": {},
1723
+ "execution_count": 30
1724
+ }
1725
+ ]
1726
+ },
1727
+ {
1728
+ "cell_type": "code",
1729
+ "source": [
1730
+ "\n",
1731
+ "# 💾 Save the fine-tuned model\n",
1732
+ "model.save_pretrained(\"./bengali-mt5-chatbot\")\n",
1733
+ "tokenizer.save_pretrained(\"./bengali-mt5-chatbot\")\n"
1734
+ ],
1735
+ "metadata": {
1736
+ "colab": {
1737
+ "base_uri": "https://localhost:8080/"
1738
+ },
1739
+ "id": "2-_xpFaY2Wia",
1740
+ "outputId": "a65d8f23-a5b5-452d-a0de-15f96f274ca1"
1741
+ },
1742
+ "execution_count": 34,
1743
+ "outputs": [
1744
+ {
1745
+ "output_type": "execute_result",
1746
+ "data": {
1747
+ "text/plain": [
1748
+ "('./bengali-mt5-chatbot/tokenizer_config.json',\n",
1749
+ " './bengali-mt5-chatbot/special_tokens_map.json',\n",
1750
+ " './bengali-mt5-chatbot/spiece.model',\n",
1751
+ " './bengali-mt5-chatbot/added_tokens.json',\n",
1752
+ " './bengali-mt5-chatbot/tokenizer.json')"
1753
+ ]
1754
+ },
1755
+ "metadata": {},
1756
+ "execution_count": 34
1757
+ }
1758
+ ]
1759
+ },
1760
+ {
1761
+ "cell_type": "code",
1762
+ "source": [
1763
+ "import torch\n",
1764
+ "\n",
1765
+ "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
1766
+ "model.to(device)\n",
1767
+ "model.eval()\n",
1768
+ "\n",
1769
+ "def answer_question(question):\n",
1770
+ " input_text = f\"প্রশ্ন: {question}\"\n",
1771
+ " inputs = tokenizer(input_text, return_tensors=\"pt\", truncation=True, padding=True, max_length=64)\n",
1772
+ " inputs = {k: v.to(device) for k, v in inputs.items()}\n",
1773
+ "\n",
1774
+ " output_ids = model.generate(\n",
1775
+ " **inputs,\n",
1776
+ " max_length=128,\n",
1777
+ " num_beams=4,\n",
1778
+ " early_stopping=True\n",
1779
+ " )\n",
1780
+ "\n",
1781
+ " decoded = tokenizer.decode(output_ids[0], skip_special_tokens=True)\n",
1782
+ " print(\"Generated tokens:\", output_ids[0].tolist())\n",
1783
+ " print(\"Decoded answer:\", decoded)\n",
1784
+ " return decoded\n",
1785
+ "\n",
1786
+ "answer_question(\"রেস্টুরেন্ট কখন খোলে?\")\n"
1787
+ ],
1788
+ "metadata": {
1789
+ "colab": {
1790
+ "base_uri": "https://localhost:8080/",
1791
+ "height": 70
1792
+ },
1793
+ "id": "BJ9XUng23QAr",
1794
+ "outputId": "0fcef38a-3068-47e5-f8d2-f050bc28a498"
1795
+ },
1796
+ "execution_count": 35,
1797
+ "outputs": [
1798
+ {
1799
+ "output_type": "stream",
1800
+ "name": "stdout",
1801
+ "text": [
1802
+ "Generated tokens: [0, 6]\n",
1803
+ "Decoded answer: <0x03>\n"
1804
+ ]
1805
+ },
1806
+ {
1807
+ "output_type": "execute_result",
1808
+ "data": {
1809
+ "text/plain": [
1810
+ "'<0x03>'"
1811
+ ],
1812
+ "application/vnd.google.colaboratory.intrinsic+json": {
1813
+ "type": "string"
1814
+ }
1815
+ },
1816
+ "metadata": {},
1817
+ "execution_count": 35
1818
+ }
1819
+ ]
1820
+ },
1821
+ {
1822
+ "cell_type": "code",
1823
+ "source": [],
1824
+ "metadata": {
1825
+ "id": "VfonlOE83VzF"
1826
+ },
1827
+ "execution_count": null,
1828
+ "outputs": []
1829
+ }
1830
+ ]
1831
+ }
special_tokens_map.json ADDED
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+ {
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+ "additional_special_tokens": [
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+ "<extra_id_0>",
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+ "<extra_id_1>",
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+ "<extra_id_2>",
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