Datasets:
File size: 3,457 Bytes
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---
language: en
license: mit
task_categories:
- text-generation
- text-classification
tags:
- llm
- conversations
- llama
- finetuning
- privacy-policies
- dataset
datasets:
- CodeHima/APP_350_LLM_Formatted
metrics:
- accuracy
- f1
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
dataset_info:
features:
- name: conversations
dtype: string
- name: __index_level_0__
dtype: int64
splits:
- name: train
num_bytes: 8446123
num_examples: 12405
- name: validation
num_bytes: 1059045
num_examples: 1551
- name: test
num_bytes: 1075320
num_examples: 1551
download_size: 3915927
dataset_size: 10580488
---
# APP-350 Formatted Dataset for LLM Fine-tuning
## Dataset Summary
The **APP-350** dataset consists of structured conversation pairs formatted for fine-tuning Large Language Models (LLMs) like LLaMA. This dataset includes questions and responses between users and an AI assistant. The dataset is particularly designed for privacy policy analysis and fairness evaluation, allowing models to learn from annotated interactions regarding privacy practices.
The conversations are organized into the following structure:
- **User Prompt:** The user initiates the conversation with a question or request.
- **Assistant Response:** The AI assistant provides a detailed response, including an assessment of the privacy policy clause.
## Intended Use
This dataset is ideal for training and fine-tuning conversational models, particularly those aimed at:
- Privacy policy analysis
- Legal document interpretation
- Fairness evaluation in legal and compliance documents
The dataset can also be used to develop models that specialize in understanding privacy-related practices and enhancing LLM performance in this domain.
## Dataset Structure
Each entry in the dataset is structured as a conversation between a **user** and an **assistant**:
```json
[
{
"content": "Analyze the following clause from a privacy policy and determine if it's fair or unfair...",
"role": "user"
},
{
"content": "This clause is fair. The privacy practices mentioned are: nan.",
"role": "assistant"
}
]
```
Each record contains:
- **content**: The text of the prompt or response.
- **role**: Specifies whether the content is from the 'user' or the 'assistant'.
## Example Entry
```json
{
"content": "How do astronomers determine the original wavelength of light emitted by a celestial body at rest...",
"role": "user"
},
{
"content": "Astronomers make use of the unique spectral fingerprints of elements found in stars...",
"role": "assistant"
}
```
## Collection Process
This dataset was collected from various privacy policy clauses and conversations annotated with fairness labels. The dataset has been structured to reflect user-assistant interactions, making it suitable for training conversational AI systems.
## Licensing
The dataset is made available under the **MIT License**, which allows for flexible use, modification, and distribution of the dataset.
## Citation
If you use this dataset, please cite it as follows:
```
@dataset{app350_llm_formatted,
title = {APP-350 Formatted Dataset for LLM Fine-tuning},
author = {Himanshu Mohanty},
year = {2024},
url = {https://huggingface.co/datasets/CodeHima/APP_350_LLM_Formatted},
license = {MIT}
}
``` |