---
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