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roberta-tiny-10M

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- license: creativeml-openrail-m
 
 
 
 
 
 
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+ tags:
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+ - generated_from_trainer
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+ metrics:
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+ - accuracy
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+ model-index:
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+ - name: roberta-tiny-10M
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+ results: []
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  ---
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+
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+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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+ should probably proofread and complete it, then remove this comment. -->
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+
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+ # roberta-tiny-10M
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+
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+ This model was trained from scratch on an unknown dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 6.4832
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+ - Accuracy: 0.1379
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 2e-05
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+ - train_batch_size: 32
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+ - eval_batch_size: 64
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+ - seed: 42
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+ - gradient_accumulation_steps: 4
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+ - total_train_batch_size: 128
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+ - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
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+ - lr_scheduler_type: constant_with_warmup
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+ - lr_scheduler_warmup_steps: 50
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+ - num_epochs: 40.0
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | 10.5287 | 0.26 | 50 | 10.4478 | 0.0481 |
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+ | 10.0073 | 0.51 | 100 | 9.9550 | 0.0488 |
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+ | 9.6268 | 0.77 | 150 | 9.5865 | 0.0488 |
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+ | 9.9837 | 1.03 | 200 | 9.2502 | 0.0471 |
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+ | 8.9701 | 1.29 | 250 | 8.9370 | 0.0466 |
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+ | 8.6689 | 1.54 | 300 | 8.6447 | 0.0473 |
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+ | 8.3893 | 1.8 | 350 | 8.3794 | 0.0473 |
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+ | 8.1697 | 2.06 | 400 | 8.1342 | 0.0506 |
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+ | 7.926 | 2.32 | 450 | 7.9221 | 0.0617 |
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+ | 7.7329 | 2.58 | 500 | 7.7398 | 0.0627 |
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+ | 7.582 | 2.83 | 550 | 7.5844 | 0.0691 |
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+ | 7.4419 | 3.09 | 600 | 7.4620 | 0.0729 |
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+ | 7.3658 | 3.35 | 650 | 7.3735 | 0.0781 |
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+ | 7.2857 | 3.61 | 700 | 7.3049 | 0.0801 |
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+ | 7.224 | 3.86 | 750 | 7.2554 | 0.0831 |
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+ | 7.1851 | 4.12 | 800 | 7.2082 | 0.0853 |
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+ | 7.1327 | 4.38 | 850 | 7.1678 | 0.0878 |
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+ | 7.0947 | 4.64 | 900 | 7.1326 | 0.0909 |
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+ | 7.0761 | 4.89 | 950 | 7.1069 | 0.0919 |
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+ | 7.0551 | 5.15 | 1000 | 7.0806 | 0.0943 |
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+ | 7.0389 | 5.41 | 1050 | 7.0588 | 0.0952 |
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+ | 7.0226 | 5.67 | 1100 | 7.0379 | 0.0964 |
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+ | 6.9992 | 5.92 | 1150 | 7.0142 | 0.0975 |
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+ | 6.9382 | 6.18 | 1200 | 6.9979 | 0.0986 |
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+ | 6.956 | 6.44 | 1250 | 6.9828 | 0.0987 |
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+ | 6.9425 | 6.7 | 1300 | 6.9619 | 0.1008 |
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+ | 6.8872 | 6.96 | 1350 | 6.9468 | 0.1014 |
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+ | 6.8848 | 7.22 | 1400 | 6.9320 | 0.1024 |
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+ | 6.8578 | 7.47 | 1450 | 6.9190 | 0.1039 |
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+ | 6.8699 | 7.73 | 1500 | 6.9022 | 0.1050 |
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+ | 6.8402 | 7.99 | 1550 | 6.8910 | 0.1057 |
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+ | 6.8172 | 8.25 | 1600 | 6.8730 | 0.1069 |
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+ | 6.823 | 8.5 | 1650 | 6.8662 | 0.1073 |
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+ | 6.8028 | 8.76 | 1700 | 6.8487 | 0.1082 |
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+ | 7.3146 | 9.02 | 1750 | 6.8400 | 0.1083 |
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+ | 6.8014 | 9.28 | 1800 | 6.8303 | 0.1092 |
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+ | 6.8028 | 9.53 | 1850 | 6.8226 | 0.1088 |
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+ | 6.7817 | 9.79 | 1900 | 6.8079 | 0.1107 |
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+ | 7.28 | 10.05 | 1950 | 6.8021 | 0.1115 |
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+ | 6.7624 | 10.31 | 2000 | 6.7930 | 0.1118 |
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+ | 6.7416 | 10.56 | 2050 | 6.7868 | 0.1124 |
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+ | 6.7288 | 10.82 | 2100 | 6.7805 | 0.1133 |
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+ | 6.7468 | 11.08 | 2150 | 6.7720 | 0.1123 |
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+ | 6.7387 | 11.34 | 2200 | 6.7636 | 0.1135 |
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+ | 6.7242 | 11.6 | 2250 | 6.7557 | 0.1134 |
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+ | 6.702 | 11.85 | 2300 | 6.7496 | 0.1141 |
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+ | 6.6662 | 12.11 | 2350 | 6.7433 | 0.1150 |
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+ | 6.6781 | 12.37 | 2400 | 6.7362 | 0.1148 |
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+ | 6.6743 | 12.63 | 2450 | 6.7275 | 0.1161 |
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+ | 6.6843 | 12.88 | 2500 | 6.7247 | 0.1165 |
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+ | 6.6726 | 13.14 | 2550 | 6.7127 | 0.1173 |
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+ | 6.6656 | 13.4 | 2600 | 6.7098 | 0.1170 |
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+ | 6.6428 | 13.66 | 2650 | 6.7019 | 0.1185 |
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+ | 6.6355 | 13.91 | 2700 | 6.6979 | 0.1175 |
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+ | 6.6521 | 14.17 | 2750 | 6.6923 | 0.1188 |
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+ | 6.6735 | 14.43 | 2800 | 6.6842 | 0.1186 |
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+ | 6.6151 | 14.69 | 2850 | 6.6791 | 0.1195 |
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+ | 6.6248 | 14.94 | 2900 | 6.6752 | 0.1198 |
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+ | 6.6427 | 15.21 | 2950 | 6.6665 | 0.1207 |
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+ | 6.5947 | 15.46 | 3000 | 6.6639 | 0.1207 |
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+ | 6.6199 | 15.72 | 3050 | 6.6598 | 0.1217 |
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+ | 6.6127 | 15.98 | 3100 | 6.6593 | 0.1219 |
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+ | 6.6031 | 16.24 | 3150 | 6.6512 | 0.1226 |
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+ | 6.5742 | 16.49 | 3200 | 6.6485 | 0.1227 |
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+ | 6.621 | 16.75 | 3250 | 6.6472 | 0.1221 |
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+ | 7.0655 | 17.01 | 3300 | 6.6369 | 0.1232 |
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+ | 6.5866 | 17.27 | 3350 | 6.6376 | 0.1234 |
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+ | 6.6098 | 17.52 | 3400 | 6.6313 | 0.1252 |
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+ | 6.5676 | 17.78 | 3450 | 6.6254 | 0.1248 |
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+ | 7.0636 | 18.04 | 3500 | 6.6226 | 0.1256 |
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+ | 6.5444 | 18.3 | 3550 | 6.6164 | 0.1253 |
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+ | 6.561 | 18.55 | 3600 | 6.6157 | 0.1254 |
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+ | 6.5882 | 18.81 | 3650 | 6.6072 | 0.1257 |
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+ | 6.5518 | 19.07 | 3700 | 6.6064 | 0.1267 |
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+ | 6.5599 | 19.33 | 3750 | 6.6055 | 0.1271 |
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+ | 6.5407 | 19.59 | 3800 | 6.5987 | 0.1274 |
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+ | 6.5373 | 19.84 | 3850 | 6.5954 | 0.1280 |
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+ | 6.5381 | 20.1 | 3900 | 6.5899 | 0.1282 |
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+ | 6.5517 | 20.36 | 3950 | 6.5888 | 0.1283 |
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+ | 6.5371 | 20.62 | 4000 | 6.5854 | 0.1295 |
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+ | 6.5819 | 20.87 | 4050 | 6.5825 | 0.1282 |
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+ | 6.5425 | 21.13 | 4100 | 6.5794 | 0.1289 |
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+ | 6.5372 | 21.39 | 4150 | 6.5760 | 0.1300 |
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+ | 6.544 | 21.65 | 4200 | 6.5718 | 0.1303 |
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+ | 6.5129 | 21.9 | 4250 | 6.5660 | 0.1310 |
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+ | 6.4798 | 22.16 | 4300 | 6.5682 | 0.1305 |
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+ | 6.5556 | 22.42 | 4350 | 6.5619 | 0.1315 |
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+ | 6.4946 | 22.68 | 4400 | 6.5589 | 0.1314 |
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+ | 6.5212 | 22.93 | 4450 | 6.5593 | 0.1318 |
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+ | 6.5055 | 23.2 | 4500 | 6.5552 | 0.1311 |
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+ | 6.4693 | 23.45 | 4550 | 6.5481 | 0.1325 |
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+ | 6.4706 | 23.71 | 4600 | 6.5469 | 0.1317 |
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+ | 6.495 | 23.97 | 4650 | 6.5462 | 0.1324 |
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+ | 6.4901 | 24.23 | 4700 | 6.5414 | 0.1328 |
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+ | 6.4936 | 24.48 | 4750 | 6.5385 | 0.1334 |
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+ | 6.481 | 24.74 | 4800 | 6.5362 | 0.1331 |
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+ | 6.5186 | 25.0 | 4850 | 6.5357 | 0.1335 |
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+ | 6.4711 | 25.26 | 4900 | 6.5309 | 0.1339 |
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+ | 6.4513 | 25.51 | 4950 | 6.5284 | 0.1337 |
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+ | 6.4652 | 25.77 | 5000 | 6.5242 | 0.1343 |
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+ | 6.9335 | 26.03 | 5050 | 6.5217 | 0.1345 |
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+ | 6.4747 | 26.29 | 5100 | 6.5206 | 0.1345 |
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+ | 6.4702 | 26.54 | 5150 | 6.5201 | 0.1350 |
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+ | 6.4524 | 26.8 | 5200 | 6.5156 | 0.1352 |
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+ | 6.4225 | 27.06 | 5250 | 6.5150 | 0.1349 |
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+ | 6.4599 | 27.32 | 5300 | 6.5116 | 0.1355 |
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+ | 6.4591 | 27.58 | 5350 | 6.5098 | 0.1358 |
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+ | 6.4184 | 27.83 | 5400 | 6.5096 | 0.1353 |
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+ | 6.43 | 28.09 | 5450 | 6.5074 | 0.1361 |
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+ | 6.4604 | 28.35 | 5500 | 6.4999 | 0.1367 |
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+ | 6.4593 | 28.61 | 5550 | 6.4994 | 0.1359 |
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+ | 6.4648 | 28.86 | 5600 | 6.4981 | 0.1356 |
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+ | 6.4453 | 29.12 | 5650 | 6.4949 | 0.1374 |
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+ | 6.4275 | 29.38 | 5700 | 6.4954 | 0.1362 |
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+ | 6.4165 | 29.64 | 5750 | 6.4938 | 0.1369 |
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+ | 6.4211 | 29.89 | 5800 | 6.4911 | 0.1376 |
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+ | 6.4188 | 30.15 | 5850 | 6.4860 | 0.1374 |
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+ | 6.4337 | 30.41 | 5900 | 6.4807 | 0.1380 |
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+ | 6.4228 | 30.67 | 5950 | 6.4876 | 0.1375 |
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+ | 6.3841 | 30.92 | 6000 | 6.4811 | 0.1376 |
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+ | 6.4383 | 31.18 | 6050 | 6.4832 | 0.1379 |
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+
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+
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+ ### Framework versions
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+
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+ - Transformers 4.24.0
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+ - Pytorch 1.11.0+cu113
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+ - Datasets 2.6.1
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+ - Tokenizers 0.12.1