20230903230355
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: 0.6499
- Accuracy: 0.5
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 | 0.6198 | 0.5 |
0.6345 | 2.0 | 680 | 0.6217 | 0.5 |
0.6271 | 3.0 | 1020 | 0.6081 | 0.5 |
0.6271 | 4.0 | 1360 | 0.6146 | 0.5 |
0.6166 | 5.0 | 1700 | 0.6180 | 0.5 |
0.619 | 6.0 | 2040 | 0.6220 | 0.5 |
0.619 | 7.0 | 2380 | 0.6023 | 0.5 |
0.605 | 8.0 | 2720 | 0.5987 | 0.5 |
0.5863 | 9.0 | 3060 | 0.6086 | 0.5016 |
0.5863 | 10.0 | 3400 | 0.6292 | 0.5047 |
0.5789 | 11.0 | 3740 | 0.6150 | 0.5016 |
0.5716 | 12.0 | 4080 | 0.5969 | 0.5 |
0.5716 | 13.0 | 4420 | 0.6045 | 0.5 |
0.5599 | 14.0 | 4760 | 0.6281 | 0.4969 |
0.5555 | 15.0 | 5100 | 0.6021 | 0.5 |
0.5555 | 16.0 | 5440 | 0.6161 | 0.5 |
0.553 | 17.0 | 5780 | 0.6050 | 0.5 |
0.5412 | 18.0 | 6120 | 0.6483 | 0.4984 |
0.5412 | 19.0 | 6460 | 0.6169 | 0.5 |
0.5403 | 20.0 | 6800 | 0.6287 | 0.5 |
0.5349 | 21.0 | 7140 | 0.6369 | 0.5 |
0.5349 | 22.0 | 7480 | 0.6163 | 0.5 |
0.5341 | 23.0 | 7820 | 0.6180 | 0.4984 |
0.5264 | 24.0 | 8160 | 0.6171 | 0.5 |
0.5265 | 25.0 | 8500 | 0.6289 | 0.5 |
0.5265 | 26.0 | 8840 | 0.6161 | 0.5 |
0.5218 | 27.0 | 9180 | 0.6542 | 0.4984 |
0.5204 | 28.0 | 9520 | 0.6246 | 0.5 |
0.5204 | 29.0 | 9860 | 0.6192 | 0.5 |
0.5164 | 30.0 | 10200 | 0.6213 | 0.5 |
0.5136 | 31.0 | 10540 | 0.6256 | 0.5 |
0.5136 | 32.0 | 10880 | 0.6605 | 0.5 |
0.5113 | 33.0 | 11220 | 0.6310 | 0.5 |
0.5101 | 34.0 | 11560 | 0.6348 | 0.5 |
0.5101 | 35.0 | 11900 | 0.6392 | 0.5 |
0.5095 | 36.0 | 12240 | 0.6291 | 0.5 |
0.5058 | 37.0 | 12580 | 0.6399 | 0.5 |
0.5058 | 38.0 | 12920 | 0.6546 | 0.5 |
0.5022 | 39.0 | 13260 | 0.6294 | 0.5 |
0.5009 | 40.0 | 13600 | 0.6348 | 0.5 |
0.5009 | 41.0 | 13940 | 0.6261 | 0.5 |
0.5005 | 42.0 | 14280 | 0.6442 | 0.5 |
0.4952 | 43.0 | 14620 | 0.6338 | 0.5 |
0.4952 | 44.0 | 14960 | 0.6358 | 0.5 |
0.5019 | 45.0 | 15300 | 0.6387 | 0.5 |
0.4968 | 46.0 | 15640 | 0.6383 | 0.5 |
0.4968 | 47.0 | 15980 | 0.6361 | 0.5 |
0.4972 | 48.0 | 16320 | 0.6428 | 0.4984 |
0.4947 | 49.0 | 16660 | 0.6308 | 0.5 |
0.4958 | 50.0 | 17000 | 0.6443 | 0.5 |
0.4958 | 51.0 | 17340 | 0.6520 | 0.5 |
0.4926 | 52.0 | 17680 | 0.6491 | 0.5 |
0.4942 | 53.0 | 18020 | 0.6400 | 0.5 |
0.4942 | 54.0 | 18360 | 0.6373 | 0.5 |
0.4895 | 55.0 | 18700 | 0.6579 | 0.5 |
0.4908 | 56.0 | 19040 | 0.6611 | 0.5 |
0.4908 | 57.0 | 19380 | 0.6474 | 0.5 |
0.4916 | 58.0 | 19720 | 0.6537 | 0.5 |
0.492 | 59.0 | 20060 | 0.6507 | 0.5 |
0.492 | 60.0 | 20400 | 0.6582 | 0.5 |
0.4855 | 61.0 | 20740 | 0.6578 | 0.5 |
0.4874 | 62.0 | 21080 | 0.6498 | 0.5 |
0.4874 | 63.0 | 21420 | 0.6445 | 0.5 |
0.485 | 64.0 | 21760 | 0.6470 | 0.5 |
0.4889 | 65.0 | 22100 | 0.6483 | 0.5 |
0.4889 | 66.0 | 22440 | 0.6412 | 0.5 |
0.4778 | 67.0 | 22780 | 0.6437 | 0.5 |
0.4862 | 68.0 | 23120 | 0.6509 | 0.5 |
0.4862 | 69.0 | 23460 | 0.6491 | 0.5 |
0.4834 | 70.0 | 23800 | 0.6485 | 0.5 |
0.4802 | 71.0 | 24140 | 0.6444 | 0.5 |
0.4802 | 72.0 | 24480 | 0.6460 | 0.5 |
0.4818 | 73.0 | 24820 | 0.6500 | 0.5 |
0.4815 | 74.0 | 25160 | 0.6549 | 0.5 |
0.4804 | 75.0 | 25500 | 0.6577 | 0.5 |
0.4804 | 76.0 | 25840 | 0.6533 | 0.5 |
0.4812 | 77.0 | 26180 | 0.6516 | 0.5 |
0.4801 | 78.0 | 26520 | 0.6513 | 0.5 |
0.4801 | 79.0 | 26860 | 0.6519 | 0.5 |
0.48 | 80.0 | 27200 | 0.6499 | 0.5 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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