--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_DistilBERT_5E results: - task: name: Text Classification type: text-classification dataset: name: yelp_review_full type: yelp_review_full config: yelp_review_full split: train args: yelp_review_full metrics: - name: Accuracy type: accuracy value: 0.9666666666666667 --- # YELP_DistilBERT_5E This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1557 - Accuracy: 0.9667 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6211 | 0.03 | 50 | 0.3873 | 0.8933 | | 0.3252 | 0.06 | 100 | 0.2181 | 0.92 | | 0.2241 | 0.1 | 150 | 0.1850 | 0.94 | | 0.2645 | 0.13 | 200 | 0.1514 | 0.9467 | | 0.2094 | 0.16 | 250 | 0.1850 | 0.92 | | 0.2693 | 0.19 | 300 | 0.1504 | 0.9467 | | 0.2524 | 0.22 | 350 | 0.1479 | 0.96 | | 0.2538 | 0.26 | 400 | 0.1375 | 0.94 | | 0.1937 | 0.29 | 450 | 0.1204 | 0.9467 | | 0.1692 | 0.32 | 500 | 0.1396 | 0.9533 | | 0.1987 | 0.35 | 550 | 0.1151 | 0.94 | | 0.207 | 0.38 | 600 | 0.1705 | 0.94 | | 0.2135 | 0.42 | 650 | 0.1189 | 0.9467 | | 0.1847 | 0.45 | 700 | 0.1315 | 0.9533 | | 0.169 | 0.48 | 750 | 0.1407 | 0.9533 | | 0.1767 | 0.51 | 800 | 0.1675 | 0.9333 | | 0.1899 | 0.54 | 850 | 0.0913 | 0.9467 | | 0.1641 | 0.58 | 900 | 0.0954 | 0.96 | | 0.1765 | 0.61 | 950 | 0.1237 | 0.9467 | | 0.1663 | 0.64 | 1000 | 0.1029 | 0.9533 | | 0.1238 | 0.67 | 1050 | 0.1267 | 0.96 | | 0.2087 | 0.7 | 1100 | 0.1111 | 0.96 | | 0.1354 | 0.74 | 1150 | 0.0916 | 0.9667 | | 0.1937 | 0.77 | 1200 | 0.1059 | 0.96 | | 0.2216 | 0.8 | 1250 | 0.1049 | 0.9467 | | 0.1788 | 0.83 | 1300 | 0.1472 | 0.94 | | 0.2138 | 0.86 | 1350 | 0.1234 | 0.9467 | | 0.1555 | 0.9 | 1400 | 0.1386 | 0.94 | | 0.1583 | 0.93 | 1450 | 0.1642 | 0.9467 | | 0.1525 | 0.96 | 1500 | 0.1571 | 0.94 | | 0.2049 | 0.99 | 1550 | 0.1257 | 0.9333 | | 0.1266 | 1.02 | 1600 | 0.1677 | 0.94 | | 0.1282 | 1.06 | 1650 | 0.1307 | 0.9533 | | 0.1007 | 1.09 | 1700 | 0.1375 | 0.9533 | | 0.0991 | 1.12 | 1750 | 0.1513 | 0.9533 | | 0.1211 | 1.15 | 1800 | 0.1229 | 0.9667 | | 0.1833 | 1.18 | 1850 | 0.1105 | 0.9733 | | 0.1596 | 1.22 | 1900 | 0.1279 | 0.9533 | | 0.1172 | 1.25 | 1950 | 0.1124 | 0.96 | | 0.1137 | 1.28 | 2000 | 0.1407 | 0.9467 | | 0.1135 | 1.31 | 2050 | 0.1377 | 0.96 | | 0.096 | 1.34 | 2100 | 0.1022 | 0.9667 | | 0.1203 | 1.38 | 2150 | 0.1719 | 0.9467 | | 0.1289 | 1.41 | 2200 | 0.1254 | 0.9667 | | 0.1392 | 1.44 | 2250 | 0.1086 | 0.9667 | | 0.1319 | 1.47 | 2300 | 0.1511 | 0.9467 | | 0.1161 | 1.5 | 2350 | 0.1758 | 0.9467 | | 0.1402 | 1.54 | 2400 | 0.1369 | 0.96 | | 0.1433 | 1.57 | 2450 | 0.1495 | 0.9667 | | 0.1882 | 1.6 | 2500 | 0.1186 | 0.9467 | | 0.1474 | 1.63 | 2550 | 0.1249 | 0.9533 | | 0.0937 | 1.66 | 2600 | 0.1390 | 0.96 | | 0.1231 | 1.7 | 2650 | 0.1467 | 0.96 | | 0.1485 | 1.73 | 2700 | 0.1602 | 0.9533 | | 0.1683 | 1.76 | 2750 | 0.1884 | 0.9533 | | 0.1141 | 1.79 | 2800 | 0.1634 | 0.96 | | 0.1351 | 1.82 | 2850 | 0.1212 | 0.9733 | | 0.1298 | 1.86 | 2900 | 0.1224 | 0.96 | | 0.1616 | 1.89 | 2950 | 0.1241 | 0.96 | | 0.1159 | 1.92 | 3000 | 0.1532 | 0.9533 | | 0.1101 | 1.95 | 3050 | 0.1105 | 0.96 | | 0.0779 | 1.98 | 3100 | 0.1334 | 0.9533 | | 0.1427 | 2.02 | 3150 | 0.1026 | 0.9733 | | 0.0673 | 2.05 | 3200 | 0.1231 | 0.96 | | 0.0901 | 2.08 | 3250 | 0.1077 | 0.9733 | | 0.0532 | 2.11 | 3300 | 0.1385 | 0.9467 | | 0.0984 | 2.14 | 3350 | 0.1432 | 0.9467 | | 0.1006 | 2.18 | 3400 | 0.1183 | 0.9667 | | 0.067 | 2.21 | 3450 | 0.1533 | 0.9533 | | 0.0901 | 2.24 | 3500 | 0.1314 | 0.9733 | | 0.0644 | 2.27 | 3550 | 0.1354 | 0.9667 | | 0.076 | 2.3 | 3600 | 0.1548 | 0.96 | | 0.0932 | 2.34 | 3650 | 0.1624 | 0.9667 | | 0.0777 | 2.37 | 3700 | 0.1878 | 0.9533 | | 0.106 | 2.4 | 3750 | 0.1721 | 0.96 | | 0.0621 | 2.43 | 3800 | 0.1470 | 0.9667 | | 0.0919 | 2.46 | 3850 | 0.1478 | 0.96 | | 0.091 | 2.5 | 3900 | 0.1371 | 0.9667 | | 0.0912 | 2.53 | 3950 | 0.1467 | 0.9667 | | 0.0775 | 2.56 | 4000 | 0.1289 | 0.9733 | | 0.1053 | 2.59 | 4050 | 0.1107 | 0.9733 | | 0.063 | 2.62 | 4100 | 0.1031 | 0.9733 | | 0.0859 | 2.66 | 4150 | 0.0953 | 0.98 | | 0.084 | 2.69 | 4200 | 0.1216 | 0.9733 | | 0.1215 | 2.72 | 4250 | 0.1025 | 0.9733 | | 0.0675 | 2.75 | 4300 | 0.0992 | 0.9667 | | 0.0608 | 2.78 | 4350 | 0.1288 | 0.96 | | 0.0965 | 2.82 | 4400 | 0.1179 | 0.9667 | | 0.061 | 2.85 | 4450 | 0.1178 | 0.9733 | | 0.0821 | 2.88 | 4500 | 0.1188 | 0.9733 | | 0.0802 | 2.91 | 4550 | 0.1423 | 0.9667 | | 0.0901 | 2.94 | 4600 | 0.1367 | 0.96 | | 0.1069 | 2.98 | 4650 | 0.1118 | 0.9733 | | 0.0653 | 3.01 | 4700 | 0.1359 | 0.9533 | | 0.0577 | 3.04 | 4750 | 0.1046 | 0.9667 | | 0.0467 | 3.07 | 4800 | 0.1366 | 0.96 | | 0.041 | 3.1 | 4850 | 0.1276 | 0.9667 | | 0.0585 | 3.13 | 4900 | 0.1426 | 0.9667 | | 0.0635 | 3.17 | 4950 | 0.1571 | 0.96 | | 0.0395 | 3.2 | 5000 | 0.1527 | 0.96 | | 0.034 | 3.23 | 5050 | 0.1323 | 0.9667 | | 0.0405 | 3.26 | 5100 | 0.1377 | 0.96 | | 0.0306 | 3.29 | 5150 | 0.1526 | 0.9667 | | 0.0471 | 3.33 | 5200 | 0.1419 | 0.9667 | | 0.0646 | 3.36 | 5250 | 0.1459 | 0.9667 | | 0.0508 | 3.39 | 5300 | 0.1312 | 0.9667 | | 0.0593 | 3.42 | 5350 | 0.1483 | 0.96 | | 0.05 | 3.45 | 5400 | 0.1076 | 0.9733 | | 0.0559 | 3.49 | 5450 | 0.1412 | 0.9667 | | 0.0614 | 3.52 | 5500 | 0.1597 | 0.9667 | | 0.0691 | 3.55 | 5550 | 0.1656 | 0.96 | | 0.0472 | 3.58 | 5600 | 0.1556 | 0.9667 | | 0.055 | 3.61 | 5650 | 0.1347 | 0.9667 | | 0.0564 | 3.65 | 5700 | 0.1424 | 0.96 | | 0.0567 | 3.68 | 5750 | 0.1448 | 0.9733 | | 0.0645 | 3.71 | 5800 | 0.1290 | 0.9667 | | 0.0361 | 3.74 | 5850 | 0.1367 | 0.9667 | | 0.0546 | 3.77 | 5900 | 0.1406 | 0.9667 | | 0.043 | 3.81 | 5950 | 0.1337 | 0.96 | | 0.0148 | 3.84 | 6000 | 0.1475 | 0.9533 | | 0.0922 | 3.87 | 6050 | 0.1318 | 0.9733 | | 0.0671 | 3.9 | 6100 | 0.1446 | 0.9733 | | 0.0295 | 3.93 | 6150 | 0.1217 | 0.9733 | | 0.0503 | 3.97 | 6200 | 0.1133 | 0.9733 | | 0.0457 | 4.0 | 6250 | 0.1145 | 0.9733 | | 0.0487 | 4.03 | 6300 | 0.1119 | 0.9733 | | 0.0491 | 4.06 | 6350 | 0.1274 | 0.9667 | | 0.0417 | 4.09 | 6400 | 0.1377 | 0.9733 | | 0.0595 | 4.13 | 6450 | 0.1271 | 0.9733 | | 0.035 | 4.16 | 6500 | 0.1183 | 0.9733 | | 0.0482 | 4.19 | 6550 | 0.1153 | 0.9733 | | 0.0196 | 4.22 | 6600 | 0.1388 | 0.9733 | | 0.028 | 4.25 | 6650 | 0.1310 | 0.9733 | | 0.0193 | 4.29 | 6700 | 0.1460 | 0.9667 | | 0.0233 | 4.32 | 6750 | 0.1233 | 0.9733 | | 0.0316 | 4.35 | 6800 | 0.1220 | 0.9667 | | 0.0132 | 4.38 | 6850 | 0.1350 | 0.9533 | | 0.0415 | 4.41 | 6900 | 0.1547 | 0.9667 | | 0.0157 | 4.45 | 6950 | 0.1562 | 0.9667 | | 0.0186 | 4.48 | 7000 | 0.1424 | 0.9667 | | 0.0012 | 4.51 | 7050 | 0.1421 | 0.9667 | | 0.0223 | 4.54 | 7100 | 0.1475 | 0.9733 | | 0.0455 | 4.57 | 7150 | 0.1457 | 0.96 | | 0.0571 | 4.61 | 7200 | 0.1559 | 0.9667 | | 0.0305 | 4.64 | 7250 | 0.1614 | 0.9667 | | 0.0457 | 4.67 | 7300 | 0.1691 | 0.9667 | | 0.022 | 4.7 | 7350 | 0.1622 | 0.9667 | | 0.0338 | 4.73 | 7400 | 0.1560 | 0.9667 | | 0.0365 | 4.77 | 7450 | 0.1553 | 0.9667 | | 0.025 | 4.8 | 7500 | 0.1512 | 0.9667 | | 0.0441 | 4.83 | 7550 | 0.1550 | 0.9667 | | 0.0363 | 4.86 | 7600 | 0.1564 | 0.9667 | | 0.0188 | 4.89 | 7650 | 0.1553 | 0.9667 | | 0.0427 | 4.93 | 7700 | 0.1572 | 0.9733 | | 0.0362 | 4.96 | 7750 | 0.1568 | 0.9667 | | 0.0115 | 4.99 | 7800 | 0.1557 | 0.9667 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.3.2 - Tokenizers 0.13.2