| --- |
| license: mit |
| base_model: hyunwoongko/kobart |
| tags: |
| - generated_from_trainer |
| model-index: |
| - name: qa_kor_math_2 |
| results: [] |
| --- |
| |
| <!-- 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. --> |
|
|
| # qa_kor_math_2 |
| |
| This model is a fine-tuned version of [hyunwoongko/kobart](https://huggingface.co/hyunwoongko/kobart) on an unknown dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.1234 |
| |
| ## 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: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 16 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 400 |
| - num_epochs: 20 |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | |
| |:-------------:|:-----:|:----:|:---------------:| |
| | No log | 0.56 | 100 | 3.2887 | |
| | No log | 1.13 | 200 | 0.8359 | |
| | No log | 1.69 | 300 | 0.4944 | |
| | No log | 2.26 | 400 | 0.3843 | |
| | 2.4704 | 2.82 | 500 | 0.3349 | |
| | 2.4704 | 3.39 | 600 | 0.3005 | |
| | 2.4704 | 3.95 | 700 | 0.2768 | |
| | 2.4704 | 4.52 | 800 | 0.2641 | |
| | 2.4704 | 5.08 | 900 | 0.2479 | |
| | 0.3213 | 5.65 | 1000 | 0.2335 | |
| | 0.3213 | 6.21 | 1100 | 0.2208 | |
| | 0.3213 | 6.78 | 1200 | 0.2117 | |
| | 0.3213 | 7.34 | 1300 | 0.2041 | |
| | 0.3213 | 7.91 | 1400 | 0.1964 | |
| | 0.2503 | 8.47 | 1500 | 0.1876 | |
| | 0.2503 | 9.04 | 1600 | 0.1790 | |
| | 0.2503 | 9.6 | 1700 | 0.1745 | |
| | 0.2503 | 10.17 | 1800 | 0.1673 | |
| | 0.2503 | 10.73 | 1900 | 0.1623 | |
| | 0.2141 | 11.3 | 2000 | 0.1579 | |
| | 0.2141 | 11.86 | 2100 | 0.1527 | |
| | 0.2141 | 12.43 | 2200 | 0.1494 | |
| | 0.2141 | 12.99 | 2300 | 0.1438 | |
| | 0.2141 | 13.56 | 2400 | 0.1427 | |
| | 0.1873 | 14.12 | 2500 | 0.1386 | |
| | 0.1873 | 14.69 | 2600 | 0.1347 | |
| | 0.1873 | 15.25 | 2700 | 0.1334 | |
| | 0.1873 | 15.82 | 2800 | 0.1321 | |
| | 0.1873 | 16.38 | 2900 | 0.1295 | |
| | 0.1718 | 16.95 | 3000 | 0.1276 | |
| | 0.1718 | 17.51 | 3100 | 0.1263 | |
| | 0.1718 | 18.08 | 3200 | 0.1255 | |
| | 0.1718 | 18.64 | 3300 | 0.1244 | |
| | 0.1718 | 19.21 | 3400 | 0.1240 | |
| | 0.1628 | 19.77 | 3500 | 0.1234 | |
|
|
|
|
| ### Framework versions |
|
|
| - Transformers 4.38.2 |
| - Pytorch 2.2.1+cu121 |
| - Datasets 2.18.0 |
| - Tokenizers 0.15.2 |
|
|
