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2_6e-3_1_0.1
This model is a fine-tuned version of bert-large-uncased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.5859
- Accuracy: 0.7254
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: 0.006
- train_batch_size: 16
- eval_batch_size: 8
- seed: 11
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 60.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
1.0992 | 1.0 | 590 | 1.0242 | 0.3783 |
0.8881 | 2.0 | 1180 | 0.9820 | 0.3817 |
0.8638 | 3.0 | 1770 | 0.9819 | 0.3783 |
0.8712 | 4.0 | 2360 | 0.8440 | 0.3789 |
0.8299 | 5.0 | 2950 | 0.7281 | 0.6217 |
0.8746 | 6.0 | 3540 | 0.6816 | 0.6049 |
0.9153 | 7.0 | 4130 | 0.6879 | 0.5281 |
0.8459 | 8.0 | 4720 | 0.6251 | 0.6333 |
0.7986 | 9.0 | 5310 | 1.0586 | 0.6217 |
0.8116 | 10.0 | 5900 | 0.6938 | 0.6434 |
0.789 | 11.0 | 6490 | 0.7268 | 0.6511 |
0.7792 | 12.0 | 7080 | 0.6182 | 0.6593 |
0.7814 | 13.0 | 7670 | 1.2212 | 0.4502 |
0.7899 | 14.0 | 8260 | 0.6923 | 0.6621 |
0.7264 | 15.0 | 8850 | 0.6417 | 0.6706 |
0.7226 | 16.0 | 9440 | 0.7098 | 0.5881 |
0.7009 | 17.0 | 10030 | 0.5964 | 0.6673 |
0.7149 | 18.0 | 10620 | 0.7206 | 0.6141 |
0.6615 | 19.0 | 11210 | 0.6004 | 0.6850 |
0.6847 | 20.0 | 11800 | 0.9306 | 0.6575 |
0.6563 | 21.0 | 12390 | 0.7185 | 0.6823 |
0.643 | 22.0 | 12980 | 0.6512 | 0.6502 |
0.6407 | 23.0 | 13570 | 0.6875 | 0.6832 |
0.6207 | 24.0 | 14160 | 0.6471 | 0.6593 |
0.5944 | 25.0 | 14750 | 0.6547 | 0.7080 |
0.6082 | 26.0 | 15340 | 0.6463 | 0.6532 |
0.6005 | 27.0 | 15930 | 0.5753 | 0.7018 |
0.5711 | 28.0 | 16520 | 0.5725 | 0.7119 |
0.5729 | 29.0 | 17110 | 0.5858 | 0.7223 |
0.556 | 30.0 | 17700 | 0.5890 | 0.7245 |
0.5549 | 31.0 | 18290 | 0.5599 | 0.7138 |
0.5355 | 32.0 | 18880 | 0.7710 | 0.6945 |
0.5358 | 33.0 | 19470 | 0.5839 | 0.7144 |
0.503 | 34.0 | 20060 | 0.6080 | 0.7324 |
0.5149 | 35.0 | 20650 | 0.6178 | 0.7107 |
0.5099 | 36.0 | 21240 | 0.5268 | 0.7275 |
0.5114 | 37.0 | 21830 | 0.5852 | 0.7269 |
0.4823 | 38.0 | 22420 | 0.5647 | 0.7229 |
0.4736 | 39.0 | 23010 | 0.6011 | 0.7339 |
0.4757 | 40.0 | 23600 | 0.7783 | 0.7208 |
0.4761 | 41.0 | 24190 | 0.5780 | 0.7294 |
0.464 | 42.0 | 24780 | 0.6204 | 0.7312 |
0.4545 | 43.0 | 25370 | 0.5590 | 0.7214 |
0.45 | 44.0 | 25960 | 0.6851 | 0.7156 |
0.4424 | 45.0 | 26550 | 0.6311 | 0.7095 |
0.4276 | 46.0 | 27140 | 0.5536 | 0.7211 |
0.4401 | 47.0 | 27730 | 0.5773 | 0.7269 |
0.4319 | 48.0 | 28320 | 0.5876 | 0.7269 |
0.4211 | 49.0 | 28910 | 0.5829 | 0.7312 |
0.4126 | 50.0 | 29500 | 0.6142 | 0.7232 |
0.4183 | 51.0 | 30090 | 0.5985 | 0.7251 |
0.4045 | 52.0 | 30680 | 0.6185 | 0.7211 |
0.4058 | 53.0 | 31270 | 0.6073 | 0.7336 |
0.402 | 54.0 | 31860 | 0.6035 | 0.7232 |
0.4031 | 55.0 | 32450 | 0.6014 | 0.7284 |
0.3964 | 56.0 | 33040 | 0.5933 | 0.7300 |
0.3932 | 57.0 | 33630 | 0.5683 | 0.7263 |
0.3954 | 58.0 | 34220 | 0.5942 | 0.7254 |
0.3898 | 59.0 | 34810 | 0.5832 | 0.7294 |
0.3842 | 60.0 | 35400 | 0.5859 | 0.7254 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3