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bert-large-cased-sigir-support-refute-no-label-40
This model is a fine-tuned version of bert-large-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.8371
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: 4e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40.0
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
2.4511 | 1.0 | 252 | 2.0790 |
2.0373 | 2.0 | 504 | 1.8538 |
1.8052 | 3.0 | 756 | 1.6633 |
1.6663 | 4.0 | 1008 | 1.5591 |
1.5556 | 5.0 | 1260 | 1.4441 |
1.4505 | 6.0 | 1512 | 1.3836 |
1.3619 | 7.0 | 1764 | 1.3255 |
1.2968 | 8.0 | 2016 | 1.2505 |
1.2332 | 9.0 | 2268 | 1.2165 |
1.1788 | 10.0 | 2520 | 1.1517 |
1.1408 | 11.0 | 2772 | 1.1446 |
1.0992 | 12.0 | 3024 | 1.1512 |
1.0578 | 13.0 | 3276 | 1.1058 |
1.0277 | 14.0 | 3528 | 1.0662 |
1.0036 | 15.0 | 3780 | 1.0270 |
0.9655 | 16.0 | 4032 | 1.0207 |
0.9364 | 17.0 | 4284 | 1.0220 |
0.9085 | 18.0 | 4536 | 0.9874 |
0.8897 | 19.0 | 4788 | 0.9658 |
0.8661 | 20.0 | 5040 | 0.9603 |
0.8434 | 21.0 | 5292 | 0.9754 |
0.8248 | 22.0 | 5544 | 0.9406 |
0.8052 | 23.0 | 5796 | 0.9154 |
0.7975 | 24.0 | 6048 | 0.8760 |
0.7854 | 25.0 | 6300 | 0.8688 |
0.7673 | 26.0 | 6552 | 0.8536 |
0.7463 | 27.0 | 6804 | 0.8544 |
0.7412 | 28.0 | 7056 | 0.8514 |
0.7319 | 29.0 | 7308 | 0.8356 |
0.7143 | 30.0 | 7560 | 0.8832 |
0.7081 | 31.0 | 7812 | 0.8421 |
0.7026 | 32.0 | 8064 | 0.8295 |
0.687 | 33.0 | 8316 | 0.8401 |
0.6882 | 34.0 | 8568 | 0.8053 |
0.679 | 35.0 | 8820 | 0.8438 |
0.6672 | 36.0 | 9072 | 0.8450 |
0.6669 | 37.0 | 9324 | 0.8231 |
0.6665 | 38.0 | 9576 | 0.8410 |
0.6596 | 39.0 | 9828 | 0.7909 |
0.6556 | 40.0 | 10080 | 0.8019 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2