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20230903015507
This model is a fine-tuned version of bert-large-cased on the super_glue dataset. It achieves the following results on the evaluation set:
- Loss: 0.8747
- Accuracy: 0.6505
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: 0.0002
- 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: 80.0
Training results
Training Loss | Epoch | Step | Validation Loss | Accuracy |
---|---|---|---|---|
No log | 1.0 | 340 | 0.6715 | 0.5172 |
0.6923 | 2.0 | 680 | 0.6802 | 0.5 |
0.6863 | 3.0 | 1020 | 0.6721 | 0.5 |
0.6863 | 4.0 | 1360 | 0.7046 | 0.5 |
0.6843 | 5.0 | 1700 | 0.6757 | 0.5 |
0.6885 | 6.0 | 2040 | 0.6788 | 0.5 |
0.6885 | 7.0 | 2380 | 0.6702 | 0.5 |
0.686 | 8.0 | 2720 | 0.6763 | 0.5 |
0.6858 | 9.0 | 3060 | 0.6777 | 0.5 |
0.6858 | 10.0 | 3400 | 0.6804 | 0.5 |
0.6868 | 11.0 | 3740 | 0.6711 | 0.5 |
0.6817 | 12.0 | 4080 | 0.6777 | 0.5 |
0.6817 | 13.0 | 4420 | 0.6960 | 0.5 |
0.6805 | 14.0 | 4760 | 0.6901 | 0.5 |
0.6823 | 15.0 | 5100 | 0.6715 | 0.5 |
0.6823 | 16.0 | 5440 | 0.6738 | 0.5016 |
0.6776 | 17.0 | 5780 | 0.6813 | 0.5 |
0.676 | 18.0 | 6120 | 0.6718 | 0.5 |
0.676 | 19.0 | 6460 | 0.6727 | 0.5 |
0.6762 | 20.0 | 6800 | 0.6742 | 0.4984 |
0.6748 | 21.0 | 7140 | 0.6699 | 0.5282 |
0.6748 | 22.0 | 7480 | 0.6624 | 0.5141 |
0.6749 | 23.0 | 7820 | 0.7549 | 0.5705 |
0.6441 | 24.0 | 8160 | 0.6447 | 0.6238 |
0.6189 | 25.0 | 8500 | 0.6692 | 0.6113 |
0.6189 | 26.0 | 8840 | 0.6171 | 0.6771 |
0.582 | 27.0 | 9180 | 0.7757 | 0.5831 |
0.5622 | 28.0 | 9520 | 0.8074 | 0.6050 |
0.5622 | 29.0 | 9860 | 0.6636 | 0.6614 |
0.5303 | 30.0 | 10200 | 0.7353 | 0.6458 |
0.5188 | 31.0 | 10540 | 0.6546 | 0.6536 |
0.5188 | 32.0 | 10880 | 0.8451 | 0.6082 |
0.5007 | 33.0 | 11220 | 0.7618 | 0.6442 |
0.4847 | 34.0 | 11560 | 0.6832 | 0.6583 |
0.4847 | 35.0 | 11900 | 0.7070 | 0.6442 |
0.4719 | 36.0 | 12240 | 0.6991 | 0.6536 |
0.4523 | 37.0 | 12580 | 0.7525 | 0.6661 |
0.4523 | 38.0 | 12920 | 0.7912 | 0.6348 |
0.4447 | 39.0 | 13260 | 0.7760 | 0.6536 |
0.439 | 40.0 | 13600 | 0.8018 | 0.6458 |
0.439 | 41.0 | 13940 | 0.7104 | 0.6708 |
0.4248 | 42.0 | 14280 | 0.7607 | 0.6599 |
0.4063 | 43.0 | 14620 | 0.6979 | 0.6803 |
0.4063 | 44.0 | 14960 | 0.7796 | 0.6614 |
0.4123 | 45.0 | 15300 | 0.7394 | 0.6708 |
0.3984 | 46.0 | 15640 | 0.7791 | 0.6599 |
0.3984 | 47.0 | 15980 | 0.7433 | 0.6614 |
0.3871 | 48.0 | 16320 | 0.7870 | 0.6442 |
0.3787 | 49.0 | 16660 | 0.7256 | 0.6755 |
0.3884 | 50.0 | 17000 | 0.8035 | 0.6536 |
0.3884 | 51.0 | 17340 | 0.7809 | 0.6489 |
0.373 | 52.0 | 17680 | 0.7920 | 0.6567 |
0.3704 | 53.0 | 18020 | 0.8107 | 0.6661 |
0.3704 | 54.0 | 18360 | 0.8759 | 0.6113 |
0.3628 | 55.0 | 18700 | 0.8727 | 0.6332 |
0.3518 | 56.0 | 19040 | 0.8756 | 0.6254 |
0.3518 | 57.0 | 19380 | 0.8555 | 0.6317 |
0.3536 | 58.0 | 19720 | 0.8082 | 0.6254 |
0.3504 | 59.0 | 20060 | 0.7880 | 0.6614 |
0.3504 | 60.0 | 20400 | 0.9100 | 0.6301 |
0.3466 | 61.0 | 20740 | 0.8614 | 0.6207 |
0.3425 | 62.0 | 21080 | 0.8712 | 0.6301 |
0.3425 | 63.0 | 21420 | 0.8285 | 0.6614 |
0.339 | 64.0 | 21760 | 0.9010 | 0.6599 |
0.3339 | 65.0 | 22100 | 0.9055 | 0.6426 |
0.3339 | 66.0 | 22440 | 0.8365 | 0.6646 |
0.3294 | 67.0 | 22780 | 0.8333 | 0.6505 |
0.3365 | 68.0 | 23120 | 0.8414 | 0.6426 |
0.3365 | 69.0 | 23460 | 0.8855 | 0.6395 |
0.332 | 70.0 | 23800 | 0.9028 | 0.6364 |
0.3171 | 71.0 | 24140 | 0.8584 | 0.6364 |
0.3171 | 72.0 | 24480 | 0.8482 | 0.6536 |
0.3204 | 73.0 | 24820 | 0.8713 | 0.6426 |
0.3289 | 74.0 | 25160 | 0.8881 | 0.6473 |
0.3139 | 75.0 | 25500 | 0.8588 | 0.6473 |
0.3139 | 76.0 | 25840 | 0.8772 | 0.6473 |
0.3159 | 77.0 | 26180 | 0.9019 | 0.6536 |
0.306 | 78.0 | 26520 | 0.8819 | 0.6505 |
0.306 | 79.0 | 26860 | 0.8837 | 0.6473 |
0.3091 | 80.0 | 27200 | 0.8747 | 0.6505 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3