---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- trl
- reward-trainer
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_social_reasoning_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. -->

# distilbert_social_reasoning_reward_model

This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6309
- Accuracy: 0.6958

## 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6618        | 0.24  | 10   | 0.6505          | 0.6725   |
| 0.6357        | 0.48  | 20   | 0.6373          | 0.6497   |
| 0.6457        | 0.72  | 30   | 0.6226          | 0.6725   |
| 0.646         | 0.96  | 40   | 0.6437          | 0.6778   |
| 0.6448        | 1.2   | 50   | 0.7565          | 0.6287   |
| 0.6339        | 1.44  | 60   | 0.6365          | 0.6655   |
| 0.6207        | 1.68  | 70   | 0.6694          | 0.6778   |
| 0.6217        | 1.92  | 80   | 0.6351          | 0.6340   |
| 0.5928        | 2.16  | 90   | 0.7245          | 0.6497   |
| 0.5938        | 2.4   | 100  | 0.6739          | 0.6497   |
| 0.5873        | 2.63  | 110  | 0.6811          | 0.6357   |
| 0.5442        | 2.87  | 120  | 0.6774          | 0.6375   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2