AndGG/my-emoji-gpt2-model

Model Card for my-emoji-gpt2-model: This is a fine-tuned GPT-2 model designed to associate task-like sentences with relevant emojis. It was fine-tuned on a custom dataset of task-like questions and emoji answers.

Model Details- Model Description: This model is a fine-tuned version of the GPT-2 language model. Its primary function is to generate appropriate emojis in response to task-like natural language inputs. Based on testing, the model achieves approximately 75% success in associating task-like sentences with emojis.

The Model is perfectly capable of associating emojis with task-like sentences related to: Housing, Family, Self-care, Nature and Basic Professional Tasks. The next update, expected in 2 weeks, will introduce the following new topics to the Model: Visiting countries, watching/playing sports, learning things(languages, math etc.), Various types of food, Videogames and more.

Developed by: Grebla Andrei Laurentiu Can also find me on: SO(https://stackoverflow.com/users/29003408/grebla-andrei), or Github(https://github.com/AndGG1), or LeetCode(https://leetcode.com/u/RubikSolve/).

Downstream Use: This model can be integrated into applications requiring automated emoji responses for task-related queries, such as task management tools, chatbots, or productivity apps.

Out-of-Scope Use: This model is specifically trained for task-like sentences and emoji association. It is not intended for general conversational AI, generatingยาว text, or other tasks outside of its specific training domain. Using it for unrelated tasks may result in poor performance or irrelevant outputs.

Bias, Risks, and Limitations The model's performance is limited by the size and diversity of the dataset it was fine-tuned on. It may not perform well on task-like sentences that are significantly different from those in the training data. The reported 75% success rate indicates that it will not always provide the correct or desired emoji.

Recommendations Users should be aware of the model's limitations and the approximate 75% success rate. It is recommended to test the model with a diverse set of task-like sentences relevant to the intended application to evaluate its performance and identify potential areas for improvement. Consider implementing a mechanism for user feedback to correct or improve emoji associations over time.

For example, it can associate relevant emojis for sentences like: "Read a book", "Make homework", "Sleep", "Go outsie", "Play with dog" etc. It can't associate emojis for complicated and reserved cases, as: "Build a server", "Take care of patients" etc. (those are reserved task cases that don't cover a large group of people).