metadata
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ASAP_FineTuningBERT_UnAugV5_k1_task1_organization_fold0
results: []
ASAP_FineTuningBERT_UnAugV5_k1_task1_organization_fold0
This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.6550
- Qwk: 0.6278
- Mse: 0.6550
- Rmse: 0.8094
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: 2e-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: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|---|---|---|---|---|---|---|
| No log | 2.0 | 2 | 10.5351 | 0.0061 | 10.5351 | 3.2458 |
| No log | 4.0 | 4 | 8.2198 | 0.0054 | 8.2198 | 2.8670 |
| No log | 6.0 | 6 | 6.3796 | 0.0029 | 6.3796 | 2.5258 |
| No log | 8.0 | 8 | 4.9785 | 0.0139 | 4.9785 | 2.2313 |
| 9.3005 | 10.0 | 10 | 3.7548 | 0.0115 | 3.7548 | 1.9377 |
| 9.3005 | 12.0 | 12 | 2.7153 | 0.0077 | 2.7153 | 1.6478 |
| 9.3005 | 14.0 | 14 | 1.8897 | 0.0520 | 1.8897 | 1.3747 |
| 9.3005 | 16.0 | 16 | 1.3712 | 0.0520 | 1.3712 | 1.1710 |
| 9.3005 | 18.0 | 18 | 1.0739 | 0.0520 | 1.0739 | 1.0363 |
| 3.4599 | 20.0 | 20 | 0.8739 | 0.0664 | 0.8739 | 0.9348 |
| 3.4599 | 22.0 | 22 | 0.7690 | 0.3702 | 0.7690 | 0.8769 |
| 3.4599 | 24.0 | 24 | 0.6434 | 0.4429 | 0.6434 | 0.8021 |
| 3.4599 | 26.0 | 26 | 0.6024 | 0.4529 | 0.6024 | 0.7761 |
| 3.4599 | 28.0 | 28 | 0.5723 | 0.4703 | 0.5723 | 0.7565 |
| 1.4411 | 30.0 | 30 | 0.5559 | 0.5397 | 0.5559 | 0.7456 |
| 1.4411 | 32.0 | 32 | 0.5948 | 0.4984 | 0.5948 | 0.7713 |
| 1.4411 | 34.0 | 34 | 0.5494 | 0.5483 | 0.5494 | 0.7412 |
| 1.4411 | 36.0 | 36 | 0.6670 | 0.5671 | 0.6670 | 0.8167 |
| 1.4411 | 38.0 | 38 | 0.5804 | 0.6215 | 0.5804 | 0.7618 |
| 0.6198 | 40.0 | 40 | 0.7679 | 0.5270 | 0.7679 | 0.8763 |
| 0.6198 | 42.0 | 42 | 0.6548 | 0.5980 | 0.6548 | 0.8092 |
| 0.6198 | 44.0 | 44 | 0.5776 | 0.6053 | 0.5776 | 0.7600 |
| 0.6198 | 46.0 | 46 | 1.0709 | 0.4333 | 1.0709 | 1.0349 |
| 0.6198 | 48.0 | 48 | 1.0767 | 0.4461 | 1.0767 | 1.0376 |
| 0.3311 | 50.0 | 50 | 0.6082 | 0.6208 | 0.6082 | 0.7799 |
| 0.3311 | 52.0 | 52 | 0.6632 | 0.6153 | 0.6632 | 0.8144 |
| 0.3311 | 54.0 | 54 | 0.8101 | 0.5604 | 0.8101 | 0.9000 |
| 0.3311 | 56.0 | 56 | 0.6507 | 0.6362 | 0.6507 | 0.8067 |
| 0.3311 | 58.0 | 58 | 0.7474 | 0.5957 | 0.7474 | 0.8645 |
| 0.1608 | 60.0 | 60 | 0.7510 | 0.5770 | 0.7510 | 0.8666 |
| 0.1608 | 62.0 | 62 | 0.6489 | 0.6265 | 0.6489 | 0.8056 |
| 0.1608 | 64.0 | 64 | 0.8040 | 0.5634 | 0.8040 | 0.8966 |
| 0.1608 | 66.0 | 66 | 0.6306 | 0.6342 | 0.6306 | 0.7941 |
| 0.1608 | 68.0 | 68 | 0.6491 | 0.6393 | 0.6491 | 0.8056 |
| 0.0968 | 70.0 | 70 | 0.8924 | 0.5310 | 0.8924 | 0.9447 |
| 0.0968 | 72.0 | 72 | 0.8359 | 0.5514 | 0.8359 | 0.9143 |
| 0.0968 | 74.0 | 74 | 0.6026 | 0.6239 | 0.6026 | 0.7763 |
| 0.0968 | 76.0 | 76 | 0.5893 | 0.6221 | 0.5893 | 0.7676 |
| 0.0968 | 78.0 | 78 | 0.6519 | 0.6201 | 0.6519 | 0.8074 |
| 0.073 | 80.0 | 80 | 0.9077 | 0.5460 | 0.9077 | 0.9527 |
| 0.073 | 82.0 | 82 | 0.9019 | 0.5462 | 0.9019 | 0.9497 |
| 0.073 | 84.0 | 84 | 0.7039 | 0.6093 | 0.7039 | 0.8390 |
| 0.073 | 86.0 | 86 | 0.6333 | 0.6300 | 0.6333 | 0.7958 |
| 0.073 | 88.0 | 88 | 0.6698 | 0.6237 | 0.6698 | 0.8184 |
| 0.0569 | 90.0 | 90 | 0.7536 | 0.5817 | 0.7536 | 0.8681 |
| 0.0569 | 92.0 | 92 | 0.8272 | 0.5526 | 0.8272 | 0.9095 |
| 0.0569 | 94.0 | 94 | 0.7834 | 0.5585 | 0.7834 | 0.8851 |
| 0.0569 | 96.0 | 96 | 0.7058 | 0.6116 | 0.7058 | 0.8401 |
| 0.0569 | 98.0 | 98 | 0.6605 | 0.6294 | 0.6605 | 0.8127 |
| 0.0526 | 100.0 | 100 | 0.6550 | 0.6278 | 0.6550 | 0.8094 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1