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20230822185017
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: 0.3476
- Accuracy: 0.7076
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: 0.003
- train_batch_size: 8
- 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 |
---|---|---|---|---|
No log | 1.0 | 312 | 0.3644 | 0.5271 |
0.5253 | 2.0 | 624 | 0.3757 | 0.5632 |
0.5253 | 3.0 | 936 | 0.3595 | 0.4874 |
0.4289 | 4.0 | 1248 | 0.4613 | 0.5415 |
0.4182 | 5.0 | 1560 | 0.3427 | 0.6137 |
0.4182 | 6.0 | 1872 | 0.3880 | 0.4874 |
0.4027 | 7.0 | 2184 | 0.4778 | 0.5487 |
0.4027 | 8.0 | 2496 | 0.3335 | 0.6715 |
0.4009 | 9.0 | 2808 | 0.4011 | 0.5523 |
0.3781 | 10.0 | 3120 | 0.3286 | 0.7040 |
0.3781 | 11.0 | 3432 | 0.4135 | 0.6101 |
0.3679 | 12.0 | 3744 | 0.3368 | 0.6787 |
0.3774 | 13.0 | 4056 | 0.3311 | 0.6787 |
0.3774 | 14.0 | 4368 | 0.3223 | 0.6859 |
0.3457 | 15.0 | 4680 | 0.3293 | 0.7076 |
0.3457 | 16.0 | 4992 | 0.4108 | 0.5812 |
0.3607 | 17.0 | 5304 | 0.3682 | 0.6534 |
0.3436 | 18.0 | 5616 | 0.3374 | 0.6498 |
0.3436 | 19.0 | 5928 | 0.3248 | 0.7148 |
0.3236 | 20.0 | 6240 | 0.3447 | 0.7184 |
0.3022 | 21.0 | 6552 | 0.3444 | 0.7148 |
0.3022 | 22.0 | 6864 | 0.3790 | 0.6643 |
0.2938 | 23.0 | 7176 | 0.3575 | 0.6968 |
0.2938 | 24.0 | 7488 | 0.3321 | 0.7112 |
0.2837 | 25.0 | 7800 | 0.3570 | 0.7076 |
0.2783 | 26.0 | 8112 | 0.3716 | 0.6426 |
0.2783 | 27.0 | 8424 | 0.3534 | 0.7040 |
0.2693 | 28.0 | 8736 | 0.3435 | 0.7004 |
0.2654 | 29.0 | 9048 | 0.3371 | 0.6968 |
0.2654 | 30.0 | 9360 | 0.3610 | 0.6787 |
0.2598 | 31.0 | 9672 | 0.3277 | 0.7220 |
0.2598 | 32.0 | 9984 | 0.3412 | 0.7076 |
0.257 | 33.0 | 10296 | 0.3389 | 0.7040 |
0.2484 | 34.0 | 10608 | 0.3424 | 0.6968 |
0.2484 | 35.0 | 10920 | 0.3671 | 0.7112 |
0.2446 | 36.0 | 11232 | 0.3492 | 0.7148 |
0.2449 | 37.0 | 11544 | 0.3485 | 0.7148 |
0.2449 | 38.0 | 11856 | 0.3413 | 0.7148 |
0.2414 | 39.0 | 12168 | 0.3373 | 0.7004 |
0.2414 | 40.0 | 12480 | 0.3415 | 0.7220 |
0.2377 | 41.0 | 12792 | 0.3434 | 0.6931 |
0.2353 | 42.0 | 13104 | 0.3612 | 0.7040 |
0.2353 | 43.0 | 13416 | 0.3516 | 0.7112 |
0.2347 | 44.0 | 13728 | 0.3430 | 0.7112 |
0.2357 | 45.0 | 14040 | 0.3455 | 0.7004 |
0.2357 | 46.0 | 14352 | 0.3480 | 0.7040 |
0.2306 | 47.0 | 14664 | 0.3580 | 0.7112 |
0.2306 | 48.0 | 14976 | 0.3636 | 0.7040 |
0.2304 | 49.0 | 15288 | 0.3483 | 0.7112 |
0.2295 | 50.0 | 15600 | 0.3529 | 0.7004 |
0.2295 | 51.0 | 15912 | 0.3498 | 0.7040 |
0.2296 | 52.0 | 16224 | 0.3501 | 0.7220 |
0.2285 | 53.0 | 16536 | 0.3474 | 0.7076 |
0.2285 | 54.0 | 16848 | 0.3444 | 0.7076 |
0.2276 | 55.0 | 17160 | 0.3404 | 0.7004 |
0.2276 | 56.0 | 17472 | 0.3500 | 0.6895 |
0.2278 | 57.0 | 17784 | 0.3507 | 0.7040 |
0.2264 | 58.0 | 18096 | 0.3468 | 0.7040 |
0.2264 | 59.0 | 18408 | 0.3522 | 0.7040 |
0.2265 | 60.0 | 18720 | 0.3476 | 0.7076 |
Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3