---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- trl
- reward-trainer
- generated_from_trainer
datasets:
- hdfs_rlhf_log_summary_dataset
metrics:
- accuracy
model-index:
- name: log_sage_reward_model
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hdfs_rlhf_log_summary_dataset
      type: hdfs_rlhf_log_summary_dataset
      config: default
      split: None
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9
---

<!-- 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: 0.5838
- Accuracy: 0.9

## 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: 16
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 1    | 0.6933          | 0.6      |
| No log        | 2.0   | 3    | 0.6923          | 0.9      |
| No log        | 3.0   | 4    | 0.6920          | 0.9      |
| No log        | 4.0   | 6    | 0.6912          | 0.9      |
| No log        | 5.0   | 8    | 0.6902          | 0.9      |
| No log        | 6.0   | 9    | 0.6898          | 0.9      |
| 0.2745        | 7.0   | 11   | 0.6885          | 0.9      |
| 0.2745        | 8.0   | 12   | 0.6876          | 0.9      |
| 0.2745        | 9.0   | 13   | 0.6862          | 0.9      |
| 0.2745        | 10.0  | 15   | 0.6830          | 0.9      |
| 0.2745        | 11.0  | 16   | 0.6813          | 0.9      |
| 0.2745        | 12.0  | 18   | 0.6768          | 0.8      |
| 0.2705        | 13.0  | 20   | 0.6707          | 0.8      |
| 0.2705        | 14.0  | 21   | 0.6665          | 0.8      |
| 0.2705        | 15.0  | 23   | 0.6576          | 0.8      |
| 0.2705        | 16.0  | 24   | 0.6521          | 0.8      |
| 0.2705        | 17.0  | 25   | 0.6457          | 0.8      |
| 0.2705        | 18.0  | 27   | 0.6334          | 0.9      |
| 0.2705        | 19.0  | 28   | 0.6273          | 0.9      |
| 0.2555        | 20.0  | 30   | 0.6165          | 0.9      |
| 0.2555        | 21.0  | 32   | 0.6063          | 0.9      |
| 0.2555        | 22.0  | 33   | 0.6017          | 0.9      |
| 0.2555        | 23.0  | 35   | 0.5942          | 0.9      |
| 0.2555        | 24.0  | 36   | 0.5911          | 0.9      |
| 0.2555        | 25.0  | 37   | 0.5882          | 0.9      |
| 0.2555        | 26.0  | 39   | 0.5846          | 0.9      |
| 0.2245        | 27.0  | 40   | 0.5838          | 0.9      |


### Framework versions

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