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t5-small_winobias_finetuned
This model is a fine-tuned version of t5-small on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2473
- Accuracy: 0.5278
- Tp: 0.5
- Tn: 0.0278
- Fp: 0.4722
- Fn: 0.0
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: 0.0003
- 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: 30
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Tp | Tn | Fp | Fn |
|---|---|---|---|---|---|---|---|---|
| 0.6334 | 0.8 | 20 | 0.3622 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.4058 | 1.6 | 40 | 0.3510 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3923 | 2.4 | 60 | 0.3511 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.376 | 3.2 | 80 | 0.3509 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3749 | 4.0 | 100 | 0.3502 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3895 | 4.8 | 120 | 0.3505 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3624 | 5.6 | 140 | 0.3508 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3754 | 6.4 | 160 | 0.3501 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3702 | 7.2 | 180 | 0.3576 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3748 | 8.0 | 200 | 0.3499 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3715 | 8.8 | 220 | 0.3482 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3576 | 9.6 | 240 | 0.3489 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3659 | 10.4 | 260 | 0.3510 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3565 | 11.2 | 280 | 0.3464 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.353 | 12.0 | 300 | 0.3474 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3614 | 12.8 | 320 | 0.3450 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3625 | 13.6 | 340 | 0.3458 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.36 | 14.4 | 360 | 0.3494 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3585 | 15.2 | 380 | 0.3435 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3541 | 16.0 | 400 | 0.3431 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3564 | 16.8 | 420 | 0.3414 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3462 | 17.6 | 440 | 0.3413 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3541 | 18.4 | 460 | 0.3382 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3579 | 19.2 | 480 | 0.3399 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3466 | 20.0 | 500 | 0.3317 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3314 | 20.8 | 520 | 0.3303 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.33 | 21.6 | 540 | 0.3246 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3279 | 22.4 | 560 | 0.3154 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3234 | 23.2 | 580 | 0.3050 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3193 | 24.0 | 600 | 0.2947 | 0.5 | 0.5 | 0.0 | 0.5 | 0.0 |
| 0.3086 | 24.8 | 620 | 0.2849 | 0.5013 | 0.5 | 0.0013 | 0.4987 | 0.0 |
| 0.2912 | 25.6 | 640 | 0.2748 | 0.5013 | 0.5 | 0.0013 | 0.4987 | 0.0 |
| 0.2787 | 26.4 | 660 | 0.2655 | 0.5107 | 0.5 | 0.0107 | 0.4893 | 0.0 |
| 0.2779 | 27.2 | 680 | 0.2581 | 0.5177 | 0.5 | 0.0177 | 0.4823 | 0.0 |
| 0.2697 | 28.0 | 700 | 0.2527 | 0.5170 | 0.5 | 0.0170 | 0.4830 | 0.0 |
| 0.2669 | 28.8 | 720 | 0.2495 | 0.5259 | 0.5 | 0.0259 | 0.4741 | 0.0 |
| 0.2654 | 29.6 | 740 | 0.2473 | 0.5278 | 0.5 | 0.0278 | 0.4722 | 0.0 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1
- Datasets 2.10.1
- Tokenizers 0.13.2
