User Story GPT-2 Model

This is a fine-tuned GPT-2 model trained on user stories and acceptance criteria.It generates acceptance criteria for a given user story in natural language.

User Story GPT-2 Model

This is a fine-tuned GPT-2 model trained on user stories and acceptance criteria. It generates acceptance criteria for a given user story in natural language. This model can be used in claims processing applications or for any other task that requires generating user stories and corresponding acceptance criteria.

Model Overview

The model is based on the GPT-2 architecture and has been fine-tuned specifically to generate user stories and acceptance criteria in the following format:

User Story: Describes a feature or requirement from the user's perspective.
Acceptance Criteria: Specifies the conditions that need to be met for the user story to be considered complete.

How to Use the Model

Example Code:

You can use the model in your Python applications using the Hugging Face Transformers library. Here’s an example to get started:

from transformers import pipeline

# Load the fine-tuned model
generator = pipeline('text-generation', model='USACCAI/USACCAIV1')

# Define a user story prompt
prompt = "As a claims processor, when I check if a treatment occurred after the person has died, I need to show a message."

# Generate acceptance criteria
result = generator(prompt, max_length=100)

# Print the generated output
print(result)