--- license: apache-2.0 tags: - generated_from_trainer datasets: - yelp_review_full metrics: - accuracy model-index: - name: YELP_ALBERT_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: 0.9733333333333334 --- # YELP_ALBERT_5E This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the yelp_review_full dataset. It achieves the following results on the evaluation set: - Loss: 0.1394 - Accuracy: 0.9733 ## 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: 3e-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 | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.4967 | 0.03 | 50 | 0.1667 | 0.9467 | | 0.3268 | 0.06 | 100 | 0.2106 | 0.9133 | | 0.3413 | 0.1 | 150 | 0.2107 | 0.9667 | | 0.3172 | 0.13 | 200 | 0.1906 | 0.94 | | 0.2804 | 0.16 | 250 | 0.2588 | 0.9 | | 0.2604 | 0.19 | 300 | 0.2023 | 0.94 | | 0.2532 | 0.22 | 350 | 0.1263 | 0.9533 | | 0.2103 | 0.26 | 400 | 0.1233 | 0.96 | | 0.212 | 0.29 | 450 | 0.2019 | 0.9267 | | 0.2669 | 0.32 | 500 | 0.1110 | 0.9667 | | 0.2187 | 0.35 | 550 | 0.1542 | 0.96 | | 0.2203 | 0.38 | 600 | 0.0879 | 0.9733 | | 0.2699 | 0.42 | 650 | 0.0971 | 0.9667 | | 0.2107 | 0.45 | 700 | 0.0863 | 0.9667 | | 0.2443 | 0.48 | 750 | 0.0823 | 0.9733 | | 0.1987 | 0.51 | 800 | 0.1207 | 0.9733 | | 0.2326 | 0.54 | 850 | 0.1368 | 0.9667 | | 0.1787 | 0.58 | 900 | 0.1027 | 0.9667 | | 0.2159 | 0.61 | 950 | 0.2443 | 0.9333 | | 0.1316 | 0.64 | 1000 | 0.2035 | 0.9467 | | 0.2416 | 0.67 | 1050 | 0.0882 | 0.9733 | | 0.2008 | 0.7 | 1100 | 0.1709 | 0.9533 | | 0.2065 | 0.74 | 1150 | 0.1098 | 0.9667 | | 0.2391 | 0.77 | 1200 | 0.1055 | 0.9667 | | 0.1533 | 0.8 | 1250 | 0.1997 | 0.94 | | 0.2016 | 0.83 | 1300 | 0.0899 | 0.96 | | 0.2016 | 0.86 | 1350 | 0.0957 | 0.9733 | | 0.2316 | 0.9 | 1400 | 0.0784 | 0.98 | | 0.1839 | 0.93 | 1450 | 0.0784 | 0.9733 | | 0.2121 | 0.96 | 1500 | 0.1150 | 0.9733 | | 0.1307 | 0.99 | 1550 | 0.0969 | 0.9733 | | 0.1271 | 1.02 | 1600 | 0.2326 | 0.9467 | | 0.1736 | 1.06 | 1650 | 0.0979 | 0.9667 | | 0.1357 | 1.09 | 1700 | 0.0862 | 0.98 | | 0.1871 | 1.12 | 1750 | 0.1419 | 0.9667 | | 0.1411 | 1.15 | 1800 | 0.1301 | 0.96 | | 0.1317 | 1.18 | 1850 | 0.1602 | 0.9533 | | 0.1432 | 1.22 | 1900 | 0.1885 | 0.9533 | | 0.1793 | 1.25 | 1950 | 0.0776 | 0.9667 | | 0.1322 | 1.28 | 2000 | 0.0822 | 0.9733 | | 0.1416 | 1.31 | 2050 | 0.0920 | 0.9733 | | 0.1524 | 1.34 | 2100 | 0.0673 | 0.98 | | 0.1338 | 1.38 | 2150 | 0.0602 | 0.98 | | 0.152 | 1.41 | 2200 | 0.0916 | 0.98 | | 0.1192 | 1.44 | 2250 | 0.0559 | 0.98 | | 0.1471 | 1.47 | 2300 | 0.1096 | 0.9667 | | 0.1267 | 1.5 | 2350 | 0.0695 | 0.9733 | | 0.1776 | 1.54 | 2400 | 0.1363 | 0.96 | | 0.1495 | 1.57 | 2450 | 0.0818 | 0.98 | | 0.1158 | 1.6 | 2500 | 0.1282 | 0.9667 | | 0.1772 | 1.63 | 2550 | 0.0682 | 0.9733 | | 0.1187 | 1.66 | 2600 | 0.1032 | 0.9733 | | 0.136 | 1.7 | 2650 | 0.1071 | 0.9667 | | 0.1829 | 1.73 | 2700 | 0.0753 | 0.9667 | | 0.1147 | 1.76 | 2750 | 0.1071 | 0.9733 | | 0.1174 | 1.79 | 2800 | 0.1441 | 0.9667 | | 0.0707 | 1.82 | 2850 | 0.1362 | 0.9667 | | 0.1372 | 1.86 | 2900 | 0.1861 | 0.9533 | | 0.2108 | 1.89 | 2950 | 0.0770 | 0.9733 | | 0.2014 | 1.92 | 3000 | 0.1114 | 0.9667 | | 0.1373 | 1.95 | 3050 | 0.1244 | 0.9667 | | 0.1242 | 1.98 | 3100 | 0.1220 | 0.96 | | 0.1267 | 2.02 | 3150 | 0.1139 | 0.9733 | | 0.1021 | 2.05 | 3200 | 0.2013 | 0.9533 | | 0.1091 | 2.08 | 3250 | 0.1027 | 0.9733 | | 0.0648 | 2.11 | 3300 | 0.1464 | 0.9733 | | 0.1207 | 2.14 | 3350 | 0.1255 | 0.9733 | | 0.0833 | 2.18 | 3400 | 0.0708 | 0.98 | | 0.0796 | 2.21 | 3450 | 0.1608 | 0.96 | | 0.0624 | 2.24 | 3500 | 0.0827 | 0.98 | | 0.0518 | 2.27 | 3550 | 0.0602 | 0.98 | | 0.1242 | 2.3 | 3600 | 0.0752 | 0.9733 | | 0.0422 | 2.34 | 3650 | 0.1000 | 0.9733 | | 0.0748 | 2.37 | 3700 | 0.1171 | 0.9667 | | 0.0839 | 2.4 | 3750 | 0.1341 | 0.9667 | | 0.1033 | 2.43 | 3800 | 0.0744 | 0.98 | | 0.0567 | 2.46 | 3850 | 0.0869 | 0.98 | | 0.0756 | 2.5 | 3900 | 0.0745 | 0.98 | | 0.0768 | 2.53 | 3950 | 0.0895 | 0.9733 | | 0.0878 | 2.56 | 4000 | 0.0703 | 0.98 | | 0.1023 | 2.59 | 4050 | 0.0806 | 0.98 | | 0.0807 | 2.62 | 4100 | 0.0338 | 0.9867 | | 0.0868 | 2.66 | 4150 | 0.0892 | 0.9667 | | 0.0648 | 2.69 | 4200 | 0.1637 | 0.9533 | | 0.0535 | 2.72 | 4250 | 0.1622 | 0.9667 | | 0.0675 | 2.75 | 4300 | 0.1354 | 0.9733 | | 0.1121 | 2.78 | 4350 | 0.1440 | 0.9533 | | 0.0714 | 2.82 | 4400 | 0.1022 | 0.9467 | | 0.0786 | 2.85 | 4450 | 0.1110 | 0.9733 | | 0.0822 | 2.88 | 4500 | 0.1218 | 0.9733 | | 0.1075 | 2.91 | 4550 | 0.1041 | 0.9733 | | 0.0783 | 2.94 | 4600 | 0.0992 | 0.9733 | | 0.1059 | 2.98 | 4650 | 0.1187 | 0.9733 | | 0.067 | 3.01 | 4700 | 0.0931 | 0.9733 | | 0.0425 | 3.04 | 4750 | 0.1252 | 0.9733 | | 0.0539 | 3.07 | 4800 | 0.1152 | 0.9733 | | 0.0419 | 3.1 | 4850 | 0.1534 | 0.9667 | | 0.0462 | 3.13 | 4900 | 0.1398 | 0.9733 | | 0.0435 | 3.17 | 4950 | 0.1168 | 0.98 | | 0.0144 | 3.2 | 5000 | 0.1489 | 0.9667 | | 0.0367 | 3.23 | 5050 | 0.1293 | 0.9733 | | 0.0336 | 3.26 | 5100 | 0.1353 | 0.9733 | | 0.0246 | 3.29 | 5150 | 0.0958 | 0.98 | | 0.0181 | 3.33 | 5200 | 0.1294 | 0.9733 | | 0.0357 | 3.36 | 5250 | 0.1209 | 0.9733 | | 0.0683 | 3.39 | 5300 | 0.1748 | 0.96 | | 0.0353 | 3.42 | 5350 | 0.2159 | 0.9533 | | 0.0415 | 3.45 | 5400 | 0.1723 | 0.96 | | 0.0336 | 3.49 | 5450 | 0.1031 | 0.98 | | 0.0475 | 3.52 | 5500 | 0.0959 | 0.98 | | 0.0393 | 3.55 | 5550 | 0.2163 | 0.96 | | 0.0337 | 3.58 | 5600 | 0.1097 | 0.9733 | | 0.0415 | 3.61 | 5650 | 0.1365 | 0.98 | | 0.035 | 3.65 | 5700 | 0.1175 | 0.98 | | 0.0448 | 3.68 | 5750 | 0.1543 | 0.9667 | | 0.0445 | 3.71 | 5800 | 0.2005 | 0.96 | | 0.0211 | 3.74 | 5850 | 0.1179 | 0.98 | | 0.0198 | 3.77 | 5900 | 0.1298 | 0.9733 | | 0.026 | 3.81 | 5950 | 0.2167 | 0.9667 | | 0.0412 | 3.84 | 6000 | 0.1224 | 0.98 | | 0.0446 | 3.87 | 6050 | 0.0798 | 0.98 | | 0.0174 | 3.9 | 6100 | 0.0577 | 0.9933 | | 0.0535 | 3.93 | 6150 | 0.1482 | 0.9667 | | 0.0495 | 3.97 | 6200 | 0.0862 | 0.98 | | 0.0267 | 4.0 | 6250 | 0.1190 | 0.98 | | 0.0087 | 4.03 | 6300 | 0.0747 | 0.98 | | 0.0102 | 4.06 | 6350 | 0.0753 | 0.9867 | | 0.0178 | 4.09 | 6400 | 0.1812 | 0.9667 | | 0.0088 | 4.13 | 6450 | 0.0817 | 0.98 | | 0.0144 | 4.16 | 6500 | 0.0805 | 0.98 | | 0.014 | 4.19 | 6550 | 0.0862 | 0.9867 | | 0.0002 | 4.22 | 6600 | 0.0894 | 0.98 | | 0.0112 | 4.25 | 6650 | 0.1004 | 0.9733 | | 0.0054 | 4.29 | 6700 | 0.0832 | 0.9867 | | 0.0001 | 4.32 | 6750 | 0.0812 | 0.9867 | | 0.0202 | 4.35 | 6800 | 0.1828 | 0.9667 | | 0.009 | 4.38 | 6850 | 0.1114 | 0.98 | | 0.0001 | 4.41 | 6900 | 0.1295 | 0.98 | | 0.0077 | 4.45 | 6950 | 0.1610 | 0.9733 | | 0.0082 | 4.48 | 7000 | 0.1787 | 0.9667 | | 0.0198 | 4.51 | 7050 | 0.1485 | 0.9733 | | 0.0017 | 4.54 | 7100 | 0.1774 | 0.9733 | | 0.0115 | 4.57 | 7150 | 0.1567 | 0.9733 | | 0.0001 | 4.61 | 7200 | 0.1534 | 0.9733 | | 0.0247 | 4.64 | 7250 | 0.2020 | 0.9667 | | 0.0059 | 4.67 | 7300 | 0.1918 | 0.9667 | | 0.0052 | 4.7 | 7350 | 0.1315 | 0.98 | | 0.0076 | 4.73 | 7400 | 0.1289 | 0.98 | | 0.0218 | 4.77 | 7450 | 0.1610 | 0.9733 | | 0.0077 | 4.8 | 7500 | 0.1355 | 0.98 | | 0.0096 | 4.83 | 7550 | 0.1378 | 0.9733 | | 0.008 | 4.86 | 7600 | 0.1568 | 0.9733 | | 0.0103 | 4.89 | 7650 | 0.1388 | 0.9733 | | 0.0009 | 4.93 | 7700 | 0.1221 | 0.98 | | 0.0287 | 4.96 | 7750 | 0.1448 | 0.9733 | | 0.01 | 4.99 | 7800 | 0.1394 | 0.9733 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0 - Datasets 2.7.1 - Tokenizers 0.13.2