README.md

---
license: apache-2.0
datasets:
- nicholasKluge/reward-aira-dataset
language:
- en
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-classification
tags:
- reward model
- alignment
- preference model
- RLHF
widget:
  - text: "Why is AI Ethics important? [SEP] Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do."
    example_title: "Bad Response"
  - text: "Why is AI Ethics important? [SEP] The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior."
    example_title: "Good Response"
---
# RewardModel

The `RewardModel` is a [BERT](https://huggingface.co/bert-base-cased) model that can be used to score the quality of a completion for a given prompt.

The model was trained with a dataset composed of `prompt`, `prefered_completions`, and `rejected_completions`.

These prompt + completions are samples of intruction datasets created via the [Self-Instruct](https://github.com/yizhongw/self-instruct) framework.

## Details

- **Size:** 109,038,209 parameters
- **Dataset:** [Reward-Aira Dataset](https://huggingface.co/datasets/nicholasKluge/reward-aira-dataset)
- **Language:** English
- **Number of Epochs:** 5
- **Batch size:** 42
- **Optimizer:** `torch.optim.AdamW`
- **Learning Rate:** 5e-5
- **GPU:** 1 NVIDIA A100-SXM4-40GB
- **Emissions:** 0.17 KgCO2 (United States of America)
- **Total Energy Consumption:** 0.48 kWh

| Step|Training Loss|Validation Loss|Accuracy|
|---|---|---|---|
| 200 |0.080300|0.037106|0.987499|
| 400 |0.039300|0.036421|0.988433|
| 600 |0.037200|0.041799|0.986447|
| 800 |0.011400|0.039411|0.989602|
| 1000 |0.013800|0.039781|0.989718|
| 1200 |0.012700|0.034337|0.990887|
| 1400 |0.005200|0.037403|0.991120|
| 1600 |0.001800|0.047661|0.990653|
| 1800 |0.000900|0.051354|0.991237|
| 2000 |0.001000|0.046224|0.990419|
| 2200 |0.000200|0.046582|0.991120|
| 2400 |0.000600|0.046632|0.990536|
| 2600 |0.000100|0.051437|0.990770|
| 2800 |0.000500|0.049085|0.990887|
| 3000 |0.000400|0.049938|0.991004|

This repository has the notebook used to train this model.

## Usage

Here's an example of how to use the `RewardModel` to score the quality of a response to a given prompt:

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained("nicholasKluge/RewardModel")
rewardModel = AutoModelForSequenceClassification.from_pretrained("nicholasKluge/RewardModel")

rewardModel.eval()
rewardModel.to(device)

# Define the question and response
prompt = "Why is AI Ethics important?"
response_good = "The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior."
response_bad = "Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do."

# Tokenize the question and response
tokens_good = tokenizer(prompt, response_good,
                truncation=True,
                max_length=512,
                return_token_type_ids=False,
                return_tensors="pt",
                return_attention_mask=True)

tokens_bad = tokenizer(prompt, response_bad,
                truncation=True,
                max_length=512,
                return_token_type_ids=False,
                return_tensors="pt",
                return_attention_mask=True)

tokens_good.to(device)
tokens_bad.to(device)

score_good = rewardModel(**tokens_good)[0].item()
score_bad = rewardModel(**tokens_bad)[0].item()

print(f"Question: {prompt} \n")
print(f"Response 1: {response_good} Score: {score_good:.3f}")
print(f"Response 2: {response_bad} Score: {score_bad:.3f}")
```

This will output the following:

```markdown
>>> Question: Why is AI Ethics important?

>>>Response 1: The field of AI Ethics delves deeply into the intricate ethical considerations that arise with respect to AI systems. This includes the role of humanity in creating and deploying these systems, as well as the conduct of machines themselves. Broadly speaking, AI Ethics can be divided into two major categories : concerns surrounding the morality of human actions in relation to creating and using AI, and concerns regarding the moral implications of machine behavior. Score: 4.777
>>>Response 2: Who cares about AI Ethics? It's just a bunch of whining about humans making and using AI and bitching about what the machines do. Score: -11.582
```

## Performance

| Acc  | [WebGPT](https://huggingface.co/datasets/openai/webgpt_comparisons)  |
|---|---|
| [Aira-RewardModel](https://huggingface.co/nicholasKluge/RewardModel)  | 52.61%*  |

* *Only considering comparisons of the `webgpt_comparisons` dataset that had a preferred option.

## License

The `RewardModel` is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for more details.