README.md

---
library_name: transformers
license: mit
base_model: intfloat/e5-small-v2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: RewardModel_deberta-v3-large
  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. -->

# RewardModel_deberta-v3-large

This model is a fine-tuned version of [intfloat/e5-small-v2](https://huggingface.co/intfloat/e5-small-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5170
- Accuracy: 1.0
- F1: 1.0
- Roc Auc: 1.0

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:-------:|
| No log        | 1.0   | 20   | 0.6583          | 0.56     | 0.4544 | 0.56    |
| No log        | 2.0   | 40   | 0.5170          | 1.0      | 1.0    | 1.0     |
| No log        | 3.0   | 60   | 0.3450          | 1.0      | 1.0    | 1.0     |
| No log        | 4.0   | 80   | 0.2474          | 1.0      | 1.0    | 1.0     |
| 0.4644        | 5.0   | 100  | 0.2256          | 1.0      | 1.0    | 1.0     |


### Framework versions

- Transformers 4.54.0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2