Model Card for kz919/mistral-7b-clf-open-orca-flan-50k-synthetic-5-models

Model Overview

The Pseudo Sequence Classifier is designed as a reward model to differentiate between ground-truth completions and responses generated by five different models from the dataset "kz919/open-orca-flan-50k-synthetic-5-models." This model operates by assigning a scalar reward score to a given input sequence, which consists of a prompt and its completion.

Model Architecture

The model architecture is inspired by discriminators used in Generative Adversarial Networks (GANs). It evaluates the authenticity of a completion in the context of the provided prompt. The output is a single scalar score that indicates the likelihood of the completion being a ground-truth response.

Training Procedure

• Objective: To maximize the reward score for sequences consisting of a prompt and a real (ground-truth) response, while minimizing the reward score for sequences with a prompt and a synthetic (fake) response.
• Training Data: The model is trained on data from "kz919/open-orca-flan-50k-synthetic-5-models," which includes real and synthetic responses.
• Methodology: The training follows a procedure similar to discriminator training in GANs. This involves alternating between training on real and synthetic data to improve the model's ability to discriminate between the two.

Model Output

• Output: A single scalar score per sequence.
• Interpretation: Higher scores indicate a higher likelihood of the completion being a ground-truth response relative to the prompt.

Use Cases

This model is particularly useful in scenarios where it is necessary to evaluate the authenticity or quality of text completions, such as in natural language generation tasks, chatbots, or content moderation systems.

Limitations

• The model's effectiveness is heavily dependent on the diversity and representativeness of the training data.
• It may have biases inherent in the training data, affecting its judgment on certain types of prompts or completions.
• The scalar score does not provide detailed insights into the nature or reason for the perceived authenticity of a completion.

Ethical Considerations

Users should be aware of potential biases in the model's decisions, especially when used in sensitive or high-stakes scenarios. Regular audits and updates are recommended to ensure fairness and accuracy.

Maintenance and Updates

The model will undergo periodic reviews and updates to improve its performance and reduce biases, with a focus on expanding its training data to cover a wider range of scenarios and responses.