| --- |
| license: mit |
| base_model: roberta-large |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| - accuracy |
| model-index: |
| - name: RewardModelSmallerQuestionWithTwoLabelsLengthJustified |
| 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. --> |
|
|
| # RewardModelSmallerQuestionWithTwoLabelsLengthJustified |
|
|
| This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.5248 |
| - F1: 0.7539 |
| - Roc Auc: 0.7508 |
| - Accuracy: 0.7380 |
|
|
| ## 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: 0.0001 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: constant |
| - lr_scheduler_warmup_ratio: 0.1 |
| - num_epochs: 30 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy | |
| |:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:| |
| | 0.7105 | 1.0 | 145 | 0.6814 | 0.5260 | 0.5192 | 0.5048 | |
| | 0.6899 | 2.0 | 290 | 0.6530 | 0.6090 | 0.6102 | 0.6038 | |
| | 0.6703 | 3.0 | 435 | 0.6318 | 0.6387 | 0.6565 | 0.6070 | |
| | 0.6432 | 4.0 | 580 | 0.6098 | 0.6961 | 0.7029 | 0.6805 | |
| | 0.6273 | 5.0 | 725 | 0.5909 | 0.7118 | 0.7141 | 0.7061 | |
| | 0.64 | 6.0 | 870 | 0.5837 | 0.7038 | 0.7029 | 0.6965 | |
| | 0.6178 | 7.0 | 1015 | 0.5829 | 0.7005 | 0.6981 | 0.6869 | |
| | 0.6342 | 8.0 | 1160 | 0.5855 | 0.6785 | 0.6805 | 0.6741 | |
| | 0.583 | 9.0 | 1305 | 0.5549 | 0.7310 | 0.7284 | 0.7188 | |
| | 0.5801 | 10.0 | 1450 | 0.5805 | 0.6710 | 0.6773 | 0.6581 | |
| | 0.6279 | 11.0 | 1595 | 0.6581 | 0.6003 | 0.6022 | 0.5974 | |
| | 0.6112 | 12.0 | 1740 | 0.5382 | 0.7372 | 0.7380 | 0.7348 | |
| | 0.5967 | 13.0 | 1885 | 0.6305 | 0.6443 | 0.6438 | 0.6422 | |
| | 0.5927 | 14.0 | 2030 | 0.6144 | 0.6613 | 0.6645 | 0.6550 | |
| | 0.5968 | 15.0 | 2175 | 0.5825 | 0.6901 | 0.6901 | 0.6901 | |
| | 0.6122 | 16.0 | 2320 | 0.5858 | 0.6815 | 0.6805 | 0.6773 | |
| | 0.5941 | 17.0 | 2465 | 0.5719 | 0.6979 | 0.7013 | 0.6901 | |
| | 0.5977 | 18.0 | 2610 | 0.6043 | 0.6699 | 0.6709 | 0.6677 | |
| | 0.59 | 19.0 | 2755 | 0.5465 | 0.7203 | 0.7220 | 0.7157 | |
| | 0.5871 | 20.0 | 2900 | 0.6474 | 0.6262 | 0.6262 | 0.6262 | |
| | 0.5932 | 21.0 | 3045 | 0.5701 | 0.6945 | 0.6965 | 0.6901 | |
| | 0.5966 | 22.0 | 3190 | 0.5281 | 0.7387 | 0.7412 | 0.7316 | |
| | 0.6006 | 23.0 | 3335 | 0.5713 | 0.6945 | 0.6965 | 0.6869 | |
| | 0.5696 | 24.0 | 3480 | 0.6498 | 0.6242 | 0.6230 | 0.6198 | |
| | 0.5921 | 25.0 | 3625 | 0.6453 | 0.6359 | 0.6342 | 0.6294 | |
| | 0.5761 | 26.0 | 3770 | 0.5226 | 0.7528 | 0.7524 | 0.7508 | |
| | 0.5504 | 27.0 | 3915 | 0.5793 | 0.6751 | 0.6725 | 0.6645 | |
| | 0.5891 | 28.0 | 4060 | 0.5248 | 0.7539 | 0.7508 | 0.7380 | |
| | 0.5757 | 29.0 | 4205 | 0.5983 | 0.6699 | 0.6693 | 0.6677 | |
| | 0.5631 | 30.0 | 4350 | 0.6187 | 0.6454 | 0.6454 | 0.6454 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.35.2 |
| - Pytorch 2.1.0+cu118 |
| - Datasets 2.15.0 |
| - Tokenizers 0.15.0 |
| |
