<!-- 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. -->
scenario-kd-from-post-finetune-gold-silver-div-6-6000-data-smsa-model-haryoaw-sc
This model is a fine-tuned version of haryoaw/scenario-normal-finetune-clf-data-smsa-model-xlm-roberta-base on the smsa dataset. It achieves the following results on the evaluation set:
- Loss: 1.6482
- Accuracy: 0.8937
- F1: 0.8587
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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 0.53 | 100 | 4.4646 | 0.7690 | 0.5226 |
| No log | 1.06 | 200 | 3.1571 | 0.8310 | 0.7511 |
| No log | 1.6 | 300 | 2.6080 | 0.8611 | 0.8199 |
| No log | 2.13 | 400 | 2.6827 | 0.8571 | 0.8156 |
| 3.6041 | 2.66 | 500 | 2.3719 | 0.8683 | 0.8262 |
| 3.6041 | 3.19 | 600 | 2.1546 | 0.8746 | 0.8407 |
| 3.6041 | 3.72 | 700 | 2.3281 | 0.8619 | 0.8241 |
| 3.6041 | 4.26 | 800 | 2.0646 | 0.8746 | 0.8333 |
| 3.6041 | 4.79 | 900 | 2.1933 | 0.8690 | 0.8271 |
| 1.4666 | 5.32 | 1000 | 2.1517 | 0.8762 | 0.8361 |
| 1.4666 | 5.85 | 1100 | 2.0496 | 0.8786 | 0.8367 |
| 1.4666 | 6.38 | 1200 | 2.0516 | 0.8738 | 0.8338 |
| 1.4666 | 6.91 | 1300 | 2.0529 | 0.8817 | 0.8433 |
| 1.4666 | 7.45 | 1400 | 1.8446 | 0.8841 | 0.8407 |
| 0.9173 | 7.98 | 1500 | 2.1299 | 0.8762 | 0.8295 |
| 0.9173 | 8.51 | 1600 | 1.9250 | 0.8921 | 0.8550 |
| 0.9173 | 9.04 | 1700 | 1.8133 | 0.8881 | 0.8384 |
| 0.9173 | 9.57 | 1800 | 1.8966 | 0.8873 | 0.8524 |
| 0.9173 | 10.11 | 1900 | 2.0855 | 0.8857 | 0.8501 |
| 0.6805 | 10.64 | 2000 | 1.8984 | 0.8881 | 0.8498 |
| 0.6805 | 11.17 | 2100 | 1.9069 | 0.8921 | 0.8504 |
| 0.6805 | 11.7 | 2200 | 1.9060 | 0.8865 | 0.8493 |
| 0.6805 | 12.23 | 2300 | 1.7894 | 0.8944 | 0.8600 |
| 0.6805 | 12.77 | 2400 | 1.9083 | 0.8825 | 0.8403 |
| 0.5396 | 13.3 | 2500 | 1.7880 | 0.8913 | 0.8471 |
| 0.5396 | 13.83 | 2600 | 1.9085 | 0.8849 | 0.8437 |
| 0.5396 | 14.36 | 2700 | 1.8370 | 0.8841 | 0.8405 |
| 0.5396 | 14.89 | 2800 | 1.8247 | 0.8817 | 0.8411 |
| 0.5396 | 15.43 | 2900 | 1.8115 | 0.8897 | 0.8458 |
| 0.495 | 15.96 | 3000 | 1.8270 | 0.8881 | 0.8561 |
| 0.495 | 16.49 | 3100 | 1.7943 | 0.8905 | 0.8470 |
| 0.495 | 17.02 | 3200 | 1.6322 | 0.8937 | 0.8550 |
| 0.495 | 17.55 | 3300 | 1.7213 | 0.8889 | 0.8490 |
| 0.495 | 18.09 | 3400 | 1.7923 | 0.8841 | 0.8344 |
| 0.4303 | 18.62 | 3500 | 1.7254 | 0.8905 | 0.8518 |
| 0.4303 | 19.15 | 3600 | 1.7596 | 0.8889 | 0.8481 |
| 0.4303 | 19.68 | 3700 | 1.6798 | 0.8865 | 0.8499 |
| 0.4303 | 20.21 | 3800 | 1.6482 | 0.8937 | 0.8587 |
Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
