---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- hdfs_rlhf_log_summary_dataset
model-index:
- name: log_sage_reward_model
  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. -->

# log_sage_reward_model

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the hdfs_rlhf_log_summary_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0005

## 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: 1.41e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log        | 1.0   | 11   | 0.0022          |
| No log        | 2.0   | 22   | 0.0049          |
| No log        | 3.0   | 33   | 0.0006          |
| No log        | 4.0   | 44   | 0.0006          |
| No log        | 5.0   | 55   | 0.0008          |
| No log        | 6.0   | 66   | 0.0003          |
| No log        | 7.0   | 77   | 0.0005          |
| No log        | 8.0   | 88   | 0.0010          |
| No log        | 9.0   | 99   | 0.0008          |
| No log        | 10.0  | 110  | 0.0007          |
| No log        | 11.0  | 121  | 0.0007          |
| No log        | 12.0  | 132  | 0.0006          |
| No log        | 13.0  | 143  | 0.0006          |
| No log        | 14.0  | 154  | 0.0004          |
| No log        | 15.0  | 165  | 0.0007          |
| No log        | 16.0  | 176  | 0.0007          |
| No log        | 17.0  | 187  | 0.0006          |
| No log        | 18.0  | 198  | 0.0004          |
| No log        | 19.0  | 209  | 0.0005          |
| No log        | 20.0  | 220  | 0.0006          |
| No log        | 21.0  | 231  | 0.0006          |
| No log        | 22.0  | 242  | 0.0006          |
| No log        | 23.0  | 253  | 0.0009          |
| No log        | 24.0  | 264  | 0.0006          |
| No log        | 25.0  | 275  | 0.0007          |
| No log        | 26.0  | 286  | 0.0005          |
| No log        | 27.0  | 297  | 0.0005          |
| No log        | 28.0  | 308  | 0.0004          |
| No log        | 29.0  | 319  | 0.0004          |
| No log        | 30.0  | 330  | 0.0005          |
| No log        | 31.0  | 341  | 0.0005          |
| No log        | 32.0  | 352  | 0.0005          |
| No log        | 33.0  | 363  | 0.0005          |
| No log        | 34.0  | 374  | 0.0004          |
| No log        | 35.0  | 385  | 0.0004          |
| No log        | 36.0  | 396  | 0.0005          |
| No log        | 37.0  | 407  | 0.0005          |
| No log        | 38.0  | 418  | 0.0005          |
| No log        | 39.0  | 429  | 0.0005          |
| No log        | 40.0  | 440  | 0.0005          |


### Framework versions

- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2