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IMDB_BERT_5E
This model is a fine-tuned version of bert-base-cased on the imdb dataset. It achieves the following results on the evaluation set:
- Loss: 0.2316
- Accuracy: 0.9533
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 |
---|---|---|---|---|
0.7094 | 0.03 | 50 | 0.6527 | 0.6467 |
0.5867 | 0.06 | 100 | 0.3681 | 0.8533 |
0.3441 | 0.1 | 150 | 0.2455 | 0.9 |
0.3052 | 0.13 | 200 | 0.3143 | 0.88 |
0.2991 | 0.16 | 250 | 0.1890 | 0.92 |
0.2954 | 0.19 | 300 | 0.2012 | 0.9267 |
0.2723 | 0.22 | 350 | 0.2178 | 0.9333 |
0.255 | 0.26 | 400 | 0.1740 | 0.9267 |
0.2675 | 0.29 | 450 | 0.1667 | 0.9467 |
0.3071 | 0.32 | 500 | 0.1766 | 0.9333 |
0.2498 | 0.35 | 550 | 0.1928 | 0.9267 |
0.2402 | 0.38 | 600 | 0.1334 | 0.94 |
0.2449 | 0.42 | 650 | 0.1332 | 0.9467 |
0.2298 | 0.45 | 700 | 0.1375 | 0.9333 |
0.2625 | 0.48 | 750 | 0.1529 | 0.9467 |
0.2459 | 0.51 | 800 | 0.1621 | 0.94 |
0.2499 | 0.54 | 850 | 0.1606 | 0.92 |
0.2405 | 0.58 | 900 | 0.1375 | 0.94 |
0.208 | 0.61 | 950 | 0.1697 | 0.94 |
0.2642 | 0.64 | 1000 | 0.1507 | 0.9467 |
0.2272 | 0.67 | 1050 | 0.1478 | 0.94 |
0.2769 | 0.7 | 1100 | 0.1423 | 0.9467 |
0.2293 | 0.74 | 1150 | 0.1434 | 0.9467 |
0.2212 | 0.77 | 1200 | 0.1371 | 0.9533 |
0.2176 | 0.8 | 1250 | 0.1380 | 0.9533 |
0.2269 | 0.83 | 1300 | 0.1453 | 0.9467 |
0.2422 | 0.86 | 1350 | 0.1450 | 0.9467 |
0.2141 | 0.9 | 1400 | 0.1775 | 0.9467 |
0.235 | 0.93 | 1450 | 0.1302 | 0.9467 |
0.2275 | 0.96 | 1500 | 0.1304 | 0.9467 |
0.2282 | 0.99 | 1550 | 0.1620 | 0.9533 |
0.1898 | 1.02 | 1600 | 0.1482 | 0.9333 |
0.1677 | 1.06 | 1650 | 0.1304 | 0.9533 |
0.1533 | 1.09 | 1700 | 0.1270 | 0.96 |
0.1915 | 1.12 | 1750 | 0.1601 | 0.9533 |
0.1687 | 1.15 | 1800 | 0.1515 | 0.9467 |
0.1605 | 1.18 | 1850 | 0.1729 | 0.9467 |
0.1731 | 1.22 | 1900 | 0.1529 | 0.94 |
0.1308 | 1.25 | 1950 | 0.1577 | 0.96 |
0.1792 | 1.28 | 2000 | 0.1668 | 0.9333 |
0.1987 | 1.31 | 2050 | 0.1613 | 0.9533 |
0.1782 | 1.34 | 2100 | 0.1542 | 0.96 |
0.199 | 1.38 | 2150 | 0.1437 | 0.9533 |
0.1224 | 1.41 | 2200 | 0.1674 | 0.96 |
0.1854 | 1.44 | 2250 | 0.1831 | 0.9533 |
0.1622 | 1.47 | 2300 | 0.1403 | 0.9533 |
0.1586 | 1.5 | 2350 | 0.1417 | 0.96 |
0.1375 | 1.54 | 2400 | 0.1409 | 0.9533 |
0.1401 | 1.57 | 2450 | 0.1759 | 0.96 |
0.1999 | 1.6 | 2500 | 0.1172 | 0.96 |
0.1746 | 1.63 | 2550 | 0.1479 | 0.96 |
0.1983 | 1.66 | 2600 | 0.1498 | 0.9467 |
0.1658 | 1.7 | 2650 | 0.1375 | 0.9533 |
0.1492 | 1.73 | 2700 | 0.1504 | 0.9667 |
0.1435 | 1.76 | 2750 | 0.1340 | 0.9667 |
0.1473 | 1.79 | 2800 | 0.1262 | 0.9667 |
0.1692 | 1.82 | 2850 | 0.1323 | 0.9533 |
0.1567 | 1.86 | 2900 | 0.1339 | 0.96 |
0.1615 | 1.89 | 2950 | 0.1204 | 0.9667 |
0.1677 | 1.92 | 3000 | 0.1202 | 0.9667 |
0.1426 | 1.95 | 3050 | 0.1310 | 0.96 |
0.1754 | 1.98 | 3100 | 0.1469 | 0.9533 |
0.1395 | 2.02 | 3150 | 0.1663 | 0.96 |
0.0702 | 2.05 | 3200 | 0.1399 | 0.9733 |
0.1351 | 2.08 | 3250 | 0.1520 | 0.9667 |
0.1194 | 2.11 | 3300 | 0.1410 | 0.9667 |
0.1087 | 2.14 | 3350 | 0.1361 | 0.9733 |
0.1245 | 2.18 | 3400 | 0.1490 | 0.9533 |
0.1285 | 2.21 | 3450 | 0.1799 | 0.96 |
0.0801 | 2.24 | 3500 | 0.1776 | 0.9533 |
0.117 | 2.27 | 3550 | 0.1756 | 0.9667 |
0.1105 | 2.3 | 3600 | 0.1749 | 0.9533 |
0.1359 | 2.34 | 3650 | 0.1750 | 0.96 |
0.1328 | 2.37 | 3700 | 0.1857 | 0.9533 |
0.1201 | 2.4 | 3750 | 0.1834 | 0.9533 |
0.1239 | 2.43 | 3800 | 0.1923 | 0.9533 |
0.0998 | 2.46 | 3850 | 0.1882 | 0.9533 |
0.0907 | 2.5 | 3900 | 0.1722 | 0.96 |
0.1214 | 2.53 | 3950 | 0.1787 | 0.96 |
0.0858 | 2.56 | 4000 | 0.1927 | 0.96 |
0.1384 | 2.59 | 4050 | 0.1312 | 0.96 |
0.0951 | 2.62 | 4100 | 0.1348 | 0.96 |
0.1325 | 2.66 | 4150 | 0.1652 | 0.9533 |
0.1429 | 2.69 | 4200 | 0.1603 | 0.9533 |
0.0923 | 2.72 | 4250 | 0.2141 | 0.94 |
0.1336 | 2.75 | 4300 | 0.1348 | 0.9733 |
0.0893 | 2.78 | 4350 | 0.1356 | 0.9667 |
0.1057 | 2.82 | 4400 | 0.1932 | 0.9533 |
0.0928 | 2.85 | 4450 | 0.1868 | 0.9533 |
0.0586 | 2.88 | 4500 | 0.1620 | 0.96 |
0.1426 | 2.91 | 4550 | 0.1944 | 0.9533 |
0.1394 | 2.94 | 4600 | 0.1630 | 0.96 |
0.0785 | 2.98 | 4650 | 0.1560 | 0.9667 |
0.0772 | 3.01 | 4700 | 0.2093 | 0.9467 |
0.0565 | 3.04 | 4750 | 0.1785 | 0.96 |
0.0771 | 3.07 | 4800 | 0.2361 | 0.9467 |
0.0634 | 3.1 | 4850 | 0.1809 | 0.96 |
0.0847 | 3.13 | 4900 | 0.1496 | 0.9733 |
0.0526 | 3.17 | 4950 | 0.1620 | 0.9667 |
0.0796 | 3.2 | 5000 | 0.1764 | 0.9667 |
0.0786 | 3.23 | 5050 | 0.1798 | 0.9667 |
0.0531 | 3.26 | 5100 | 0.1698 | 0.9667 |
0.0445 | 3.29 | 5150 | 0.2088 | 0.96 |
0.1212 | 3.33 | 5200 | 0.1842 | 0.9533 |
0.0825 | 3.36 | 5250 | 0.2016 | 0.9533 |
0.0782 | 3.39 | 5300 | 0.1775 | 0.9533 |
0.0627 | 3.42 | 5350 | 0.1656 | 0.96 |
0.0898 | 3.45 | 5400 | 0.2331 | 0.9533 |
0.0882 | 3.49 | 5450 | 0.2514 | 0.9467 |
0.0798 | 3.52 | 5500 | 0.2090 | 0.9533 |
0.0474 | 3.55 | 5550 | 0.2322 | 0.96 |
0.0773 | 3.58 | 5600 | 0.2023 | 0.96 |
0.0862 | 3.61 | 5650 | 0.2247 | 0.96 |
0.0723 | 3.65 | 5700 | 0.2001 | 0.96 |
0.0549 | 3.68 | 5750 | 0.2031 | 0.9533 |
0.044 | 3.71 | 5800 | 0.2133 | 0.96 |
0.0644 | 3.74 | 5850 | 0.1876 | 0.9667 |
0.0868 | 3.77 | 5900 | 0.2182 | 0.9533 |
0.072 | 3.81 | 5950 | 0.1856 | 0.9667 |
0.092 | 3.84 | 6000 | 0.2120 | 0.96 |
0.0806 | 3.87 | 6050 | 0.2006 | 0.9533 |
0.0627 | 3.9 | 6100 | 0.1900 | 0.9533 |
0.0738 | 3.93 | 6150 | 0.1869 | 0.96 |
0.0667 | 3.97 | 6200 | 0.2216 | 0.96 |
0.0551 | 4.0 | 6250 | 0.2147 | 0.9533 |
0.0271 | 4.03 | 6300 | 0.2038 | 0.96 |
0.0763 | 4.06 | 6350 | 0.2058 | 0.96 |
0.0612 | 4.09 | 6400 | 0.2037 | 0.9533 |
0.0351 | 4.13 | 6450 | 0.2081 | 0.96 |
0.0265 | 4.16 | 6500 | 0.2373 | 0.9533 |
0.0391 | 4.19 | 6550 | 0.2264 | 0.9533 |
0.0609 | 4.22 | 6600 | 0.2035 | 0.9533 |
0.0435 | 4.25 | 6650 | 0.1989 | 0.96 |
0.0309 | 4.29 | 6700 | 0.2096 | 0.9667 |
0.064 | 4.32 | 6750 | 0.2385 | 0.9533 |
0.0388 | 4.35 | 6800 | 0.2071 | 0.96 |
0.0267 | 4.38 | 6850 | 0.2336 | 0.96 |
0.0433 | 4.41 | 6900 | 0.2045 | 0.9667 |
0.0596 | 4.45 | 6950 | 0.2013 | 0.96 |
0.0273 | 4.48 | 7000 | 0.2122 | 0.96 |
0.0559 | 4.51 | 7050 | 0.2182 | 0.96 |
0.0504 | 4.54 | 7100 | 0.2172 | 0.96 |
0.0536 | 4.57 | 7150 | 0.2406 | 0.9533 |
0.0624 | 4.61 | 7200 | 0.2194 | 0.9533 |
0.0668 | 4.64 | 7250 | 0.2156 | 0.96 |
0.0208 | 4.67 | 7300 | 0.2150 | 0.96 |
0.0436 | 4.7 | 7350 | 0.2361 | 0.9533 |
0.0285 | 4.73 | 7400 | 0.2175 | 0.96 |
0.0604 | 4.77 | 7450 | 0.2241 | 0.9467 |
0.0502 | 4.8 | 7500 | 0.2201 | 0.96 |
0.0342 | 4.83 | 7550 | 0.2232 | 0.96 |
0.0467 | 4.86 | 7600 | 0.2247 | 0.9533 |
0.0615 | 4.89 | 7650 | 0.2235 | 0.96 |
0.0769 | 4.93 | 7700 | 0.2302 | 0.9533 |
0.0451 | 4.96 | 7750 | 0.2334 | 0.9467 |
0.0532 | 4.99 | 7800 | 0.2316 | 0.9533 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1