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TUF_ELECTRA_5E
This model is a fine-tuned version of google/electra-small-discriminator on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.1195
- Accuracy: 0.94
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 |
---|---|---|---|---|
0.6021 | 0.1 | 50 | 0.5770 | 0.7533 |
0.5301 | 0.2 | 100 | 0.5460 | 0.7533 |
0.4958 | 0.3 | 150 | 0.4943 | 0.7533 |
0.4347 | 0.4 | 200 | 0.4112 | 0.8467 |
0.3565 | 0.5 | 250 | 0.3601 | 0.88 |
0.3515 | 0.59 | 300 | 0.3465 | 0.9 |
0.301 | 0.69 | 350 | 0.3214 | 0.9067 |
0.2963 | 0.79 | 400 | 0.2996 | 0.9 |
0.2848 | 0.89 | 450 | 0.2511 | 0.9267 |
0.2548 | 0.99 | 500 | 0.2493 | 0.8933 |
0.2527 | 1.09 | 550 | 0.2381 | 0.9333 |
0.2484 | 1.19 | 600 | 0.2099 | 0.9333 |
0.2267 | 1.29 | 650 | 0.1834 | 0.9333 |
0.2147 | 1.39 | 700 | 0.1919 | 0.94 |
0.1961 | 1.49 | 750 | 0.1751 | 0.9333 |
0.1868 | 1.58 | 800 | 0.1772 | 0.9267 |
0.2393 | 1.68 | 850 | 0.1726 | 0.92 |
0.1747 | 1.78 | 900 | 0.1509 | 0.9467 |
0.2236 | 1.88 | 950 | 0.1532 | 0.94 |
0.174 | 1.98 | 1000 | 0.1752 | 0.9267 |
0.1983 | 2.08 | 1050 | 0.1563 | 0.94 |
0.2015 | 2.18 | 1100 | 0.1494 | 0.9467 |
0.1563 | 2.28 | 1150 | 0.1876 | 0.9333 |
0.168 | 2.38 | 1200 | 0.1802 | 0.9333 |
0.2074 | 2.48 | 1250 | 0.1669 | 0.94 |
0.1726 | 2.57 | 1300 | 0.1348 | 0.9533 |
0.1373 | 2.67 | 1350 | 0.1549 | 0.9533 |
0.1694 | 2.77 | 1400 | 0.1339 | 0.96 |
0.1782 | 2.87 | 1450 | 0.1417 | 0.9533 |
0.1771 | 2.97 | 1500 | 0.1228 | 0.96 |
0.1886 | 3.07 | 1550 | 0.1415 | 0.9533 |
0.1507 | 3.17 | 1600 | 0.1350 | 0.9467 |
0.1435 | 3.27 | 1650 | 0.1294 | 0.9467 |
0.1548 | 3.37 | 1700 | 0.1316 | 0.96 |
0.1475 | 3.47 | 1750 | 0.1314 | 0.9333 |
0.1764 | 3.56 | 1800 | 0.1195 | 0.94 |
0.1668 | 3.66 | 1850 | 0.1199 | 0.94 |
0.1336 | 3.76 | 1900 | 0.1210 | 0.9467 |
0.1452 | 3.86 | 1950 | 0.1259 | 0.9467 |
0.206 | 3.96 | 2000 | 0.1247 | 0.96 |
0.1704 | 4.06 | 2050 | 0.1253 | 0.9533 |
0.1489 | 4.16 | 2100 | 0.1194 | 0.94 |
0.1766 | 4.26 | 2150 | 0.1278 | 0.96 |
0.1387 | 4.36 | 2200 | 0.1179 | 0.94 |
0.1269 | 4.46 | 2250 | 0.1270 | 0.96 |
0.154 | 4.55 | 2300 | 0.1208 | 0.94 |
0.1481 | 4.65 | 2350 | 0.1210 | 0.94 |
0.1676 | 4.75 | 2400 | 0.1196 | 0.94 |
0.1202 | 4.85 | 2450 | 0.1194 | 0.94 |
0.1323 | 4.95 | 2500 | 0.1195 | 0.94 |
Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.7.1
- Tokenizers 0.13.2