task_categories:text-generation task_categories:feature-extraction size_categories:10K<n<100K language:en license:mit region:us
---
license: mit
task_categories:
- text-generation
- feature-extraction
language:
- en
size_categories:
- 10K<n<100K
---

# Dataset Card for Research Publications (Alpaca Format)

This dataset card describes the structured data points encompassing research titles, summaries, and publication dates in the realm of artificial intelligence (AI), machine learning (ML), computer vision and pattern recognition, and neural and evolutionary computing. The data spans research published from early 2022 to October 2023.

## Dataset Details

### Dataset Description

This dataset provides structured data points, capturing research titles, summaries, and publication dates in areas of artificial intelligence, machine learning, computer vision and pattern recognition, and neural and evolutionary computing. The dataset spans publications from early 2022 to October 2023.

- **Curated by:** Jack W.
- **Funded by:** Self
- **Language(s) (NLP):** English
- **License:** MIT

## Uses

### Direct Use

This dataset is designed for fine-tuning machine learning models, specifically in the Llama2 (LoRa) context. The data can be utilized for understanding and summarizing research articles within the mentioned categories, aiding researchers in quickly obtaining insights.

### Out-of-Scope Use

The dataset is not intended for general natural language processing tasks unrelated to the specific research topics covered.

## Dataset Structure

The dataset uses the Alpaca format suitable for Llama2 finetuning. Each data entry is a JSON object containing fields: `instruction`, `input`, and `output`.

## Dataset Creation

### Curation Rationale

The dataset was created to augment a researcher's ability to sift through vast amounts of research data efficiently, providing insights, summaries, and overviews of research topics.

### Source Data

#### Data Collection and Processing

The data was collected from various research publications in the realm of AI, ML, computer vision, and neural computing from early 2022 to October 2023 - all information comes from Arxiv API.

Thank you to arXiv for use of its open access interoperability.

#### Who are the source data producers?

Research institutions and researchers produce articles in the specified domains.

### Annotations

Annotations were not provided as part of this dataset.

## Bias, Risks, and Limitations

The dataset may have biases inherent to the selection and summarization of research articles. It might not cover all research in the specified domains or time frame.

### Recommendations

Users should be aware of potential biases and ensure they use the dataset in contexts relevant to the research domains covered.

## Citation

**Arxiv:** https://arxiv.org/ 


## Glossary

- **Alpaca Format:** A data structure format suitable for Llama2 finetuning.
- **Llama2 (LoRa):** Reference to the machine learning model or platform being used.

## More Information

https://github.com/j-webtek 

## Dataset Card Authors

Jack W.

## Dataset Card Contact

**TBD**