task_categories:text-generation task_categories:conversational size_categories:100K<n<1M license:apache-2.0 region:us
---
license: apache-2.0
task_categories:
- text-generation
- conversational
pretty_name: Funcion Calling Like A Champ
size_categories:
- 100K<n<1M
---
# Function Recommendation Dataset Readme

## Description

This dataset is based on the "glaiveai/glaive-function-calling" repository and has been customized to suit my specific requirements. It is designed for fine-tuning a Large Language Model (LLM) on the task of generating function recommendations in a conversational context. The dataset contains 158,738 snippets of conversations between me and my virtual assistant named J.O.S.I.E. (Just an Outstandingly Smart Intelligent Entity), who only assists me with various tasks by recommending functions and providing relevant information.

Each snippet in the dataset represents a conversation between the user and J.O.S.I.E. The conversations typically follow this structure:

1. The conversation begins with an introduction of J.O.S.I.E. and its capabilities.

2. The user asks a question or requests assistance related to a specific task.

3. J.O.S.I.E. responds by recommending a function call that can help fulfill the user's request.

4. The user may ask follow-up questions or request additional recommendations.

5. J.O.S.I.E. continues to provide function recommendations and responses as needed.

## Snippet Example

Here is an example snippet from the dataset:

```markdown
### SYSTEM: You are Gökdeniz Gülmez's private assistant named J.O.S.I.E. (Just an Outstandingly Smart Intelligent Entity) who has access to the following functions to exclusively help Gökdeniz Gülmez, you can use the functions if needed-
{
    "name": "recommend_movies",
    "description": "Recommend movies based on user preferences",
    "parameters": {
        "type": "object",
        "properties": {
            "genre": {
                "type": "string",
                "description": "The preferred movie genre"
            },
            "year_range": {
                "type": "object",
                "properties": {
                    "start_year": {
                        "type": "integer",
                        "description": "The start year of the movie release"
                    },
                    "end_year": {
                        "type": "integer",
                        "description": "The end year of the movie release"
                    }
                }
            }
        }
    }
}
### Gökdeniz Gülmez: Can you recommend some movies for me?
### J.O.S.I.E.: Sure! I can recommend movies based on your preferences. Please provide me with your preferred movie genre and the year range of the movie release.
### Gökdeniz Gülmez: I enjoy action movies from the 2000s.
### J.O.S.I.E.: <functioncall> {"name": "recommend_movies", "arguments": '{
  "genre": "action",
  "year_range": {
    "start_year": 2000,
    "end_year": 2009
  }
}'}
### FUNCTION RESPONSE: {"movies": ["The Dark Knight", "Gladiator", "The Bourne Identity", "Kill Bill: Volume 1", "The Matrix"]}
### J.O.S.I.E.: Based on your preferences, I recommend the following movies: "The Dark Knight", "Gladiator", "The Bourne Identity", "Kill Bill: Volume 1", and "The Matrix".
```

In this example, the user asks J.O.S.I.E. to recommend action movies from the 2000s. J.O.S.I.E. responds with a function call to the "recommend_movies" function and provides a list of recommended movies as a response.

## Dataset Usage

This dataset can be used for training and fine-tuning Large Language Models (LLMs) such as GPT-3.5 on the task of generating function recommendations in a conversational context. Researchers and developers can use this data to build virtual assistants or chatbots capable of recommending functions and providing relevant information to users based on their requests.

## Citation

If you use this dataset in your research or applications, please cite it as follows:

```
@dataset{your citation here,
  title = {Private Function Calling},
  author = {Gökdeniz Gülmez},
  year = {2023},
  publisher = {Gökdeniz Gülmez},
  url = {https://huggingface.co/datasets/Isaak-Carter/Function_Calling_Private_GG/tree/main},
}
```