<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
20230819211604
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: 0.3362
- Accuracy: 0.7473
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: 0.004
- 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 | 0.4002 | 0.5307 |
0.545 | 2.0 | 624 | 0.4058 | 0.5379 |
0.545 | 3.0 | 936 | 0.3972 | 0.5379 |
0.4698 | 4.0 | 1248 | 0.4360 | 0.4729 |
0.4785 | 5.0 | 1560 | 0.3494 | 0.5090 |
0.4785 | 6.0 | 1872 | 0.4100 | 0.4729 |
0.4322 | 7.0 | 2184 | 0.5717 | 0.5307 |
0.4322 | 8.0 | 2496 | 0.4078 | 0.5379 |
0.3946 | 9.0 | 2808 | 0.3304 | 0.6570 |
0.36 | 10.0 | 3120 | 0.3318 | 0.6426 |
0.36 | 11.0 | 3432 | 0.3275 | 0.6931 |
0.3478 | 12.0 | 3744 | 0.3314 | 0.7148 |
0.3359 | 13.0 | 4056 | 0.3277 | 0.7112 |
0.3359 | 14.0 | 4368 | 0.3307 | 0.7148 |
0.3249 | 15.0 | 4680 | 0.3245 | 0.6968 |
0.3249 | 16.0 | 4992 | 0.3626 | 0.6498 |
0.3253 | 17.0 | 5304 | 0.3567 | 0.6859 |
0.3155 | 18.0 | 5616 | 0.3279 | 0.7112 |
0.3155 | 19.0 | 5928 | 0.3257 | 0.7256 |
0.3145 | 20.0 | 6240 | 0.3337 | 0.7112 |
0.3051 | 21.0 | 6552 | 0.3289 | 0.7365 |
0.3051 | 22.0 | 6864 | 0.3523 | 0.6931 |
0.3015 | 23.0 | 7176 | 0.3459 | 0.7040 |
0.3015 | 24.0 | 7488 | 0.3323 | 0.7076 |
0.2952 | 25.0 | 7800 | 0.3445 | 0.7329 |
0.289 | 26.0 | 8112 | 0.3554 | 0.7329 |
0.289 | 27.0 | 8424 | 0.3210 | 0.7292 |
0.2876 | 28.0 | 8736 | 0.3204 | 0.7365 |
0.2862 | 29.0 | 9048 | 0.3374 | 0.7509 |
0.2862 | 30.0 | 9360 | 0.3778 | 0.7112 |
0.2814 | 31.0 | 9672 | 0.3352 | 0.7401 |
0.2814 | 32.0 | 9984 | 0.3251 | 0.7256 |
0.2777 | 33.0 | 10296 | 0.3574 | 0.7617 |
0.2698 | 34.0 | 10608 | 0.3330 | 0.7292 |
0.2698 | 35.0 | 10920 | 0.3388 | 0.7220 |
0.2714 | 36.0 | 11232 | 0.3222 | 0.7329 |
0.2695 | 37.0 | 11544 | 0.3482 | 0.7473 |
0.2695 | 38.0 | 11856 | 0.3447 | 0.7437 |
0.2637 | 39.0 | 12168 | 0.3394 | 0.7401 |
0.2637 | 40.0 | 12480 | 0.3264 | 0.7401 |
0.2646 | 41.0 | 12792 | 0.3311 | 0.7401 |
0.2613 | 42.0 | 13104 | 0.3322 | 0.7365 |
0.2613 | 43.0 | 13416 | 0.3411 | 0.7473 |
0.2539 | 44.0 | 13728 | 0.3298 | 0.7581 |
0.2543 | 45.0 | 14040 | 0.3442 | 0.7437 |
0.2543 | 46.0 | 14352 | 0.3399 | 0.7545 |
0.2516 | 47.0 | 14664 | 0.3330 | 0.7473 |
0.2516 | 48.0 | 14976 | 0.3299 | 0.7473 |
0.2509 | 49.0 | 15288 | 0.3407 | 0.7401 |
0.2484 | 50.0 | 15600 | 0.3268 | 0.7581 |
0.2484 | 51.0 | 15912 | 0.3386 | 0.7509 |
0.2491 | 52.0 | 16224 | 0.3323 | 0.7581 |
0.2483 | 53.0 | 16536 | 0.3448 | 0.7473 |
0.2483 | 54.0 | 16848 | 0.3339 | 0.7545 |
0.2452 | 55.0 | 17160 | 0.3343 | 0.7473 |
0.2452 | 56.0 | 17472 | 0.3408 | 0.7509 |
0.2456 | 57.0 | 17784 | 0.3374 | 0.7545 |
0.2429 | 58.0 | 18096 | 0.3360 | 0.7473 |
0.2429 | 59.0 | 18408 | 0.3345 | 0.7545 |
0.2436 | 60.0 | 18720 | 0.3362 | 0.7473 |
Framework versions
- Transformers 4.30.0
- Pytorch 2.0.1
- Datasets 2.14.4
- Tokenizers 0.13.3