license:apache-2.0 size_categories:1K<n<10K format:json modality:text library:datasets library:pandas library:mlcroissant library:polars region:us
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license: apache-2.0
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# **Data Science Fine-Tuning Dataset**

This dataset was created by extracting content from 17 academic textbooks in the field of data science. The content was cleaned using **Grobid** and structured into nodes using a **Semantic Node Splitter**, which organizes the data based on semantic similarity. This dataset is specifically designed to help fine-tune embedding models for data science applications, incorporating knowledge from various domains such as machine learning, deep learning, time series analysis, reinforcement learning, and more.

## **Textbooks Used for Dataset Creation**

1. Aßenmacher, Matthias. *Multimodal Deep Learning* (2023).
2. Bertsekas, Dimitri P. *A Course in Reinforcement Learning*.
3. Boykis, Vicki. *What are Embeddings* (2023).
4. Bruce, Peter & Andrew Bruce. *Practical Statistics for Data Scientists* (2017).
5. Daumé III, Hal. *A Course in Machine Learning*.
6. Deisenroth, Marc Peter et al. *Mathematics for Machine Learning* (2020).
7. Devlin, Hannah et al. *Seeing Theory*.
8. Gutmann, Michael U. *Pen & Paper: Exercises in Machine Learning*.
9. Jung, Alexander. *Machine Learning: The Basics* (2022).
10. Langr, Jakub & Vladimir Bok. *Deep Learning with Generative Adversarial Networks* (2019).
11. MacKay, David J.C. *Information Theory, Inference, and Learning Algorithms* (2003).
12. Montgomery, Douglas C. et al. *Introduction to Time Series Analysis and Forecasting* (2015).
13. Nilsson, Nils J. *Introduction to Machine Learning* (1996).
14. Prince, Simon J.D. *Understanding Deep Learning* (2024).
15. Shashua, Amnon. *Introduction to Machine Learning* (2008).
16. Sutton, Richard S. & Andrew G. Barto. *Reinforcement Learning: An Introduction* (2018).
17. Alpaydin, Ethem. *Introduction to Machine Learning* (2014).

## **Dataset Overview**

- **Data Cleaning**: The original textbook content was processed using **Grobid** to clean the text and extract meaningful data.
- **Node Creation**: Using a **Semantic Node Splitter**, the content was segmented into smaller nodes based on semantic similarity to ensure high relevancy for each query and document pair.
- **Embedding Model Fine-Tuning**: This dataset is ideal for those looking to fine-tune open-source embedding models for data science-specific applications, enriching them with knowledge from key literature.

## **Applications**

This dataset covers multiple data science fields, including but not limited to:
- Machine Learning
- Deep Learning
- Time Series Modeling
- Reinforcement Learning
- Data Preprocessing
- Feature Engineering
- Synthetic Data Generation

## **Use Cases**

This dataset is particularly useful for anyone aiming to fine-tune open-source embedding models like **sentence-transformers** for data science-specific tasks. By using this dataset, you can enhance the performance of your model in understanding, retrieving, and generating domain-relevant embeddings.

## **Access Full Code**

For full code, including the creation of the fine-tuning dataset and the fine-tuning process itself, visit the following link:

[Full Code for Fine-Tuning Embedding Models](https://github.com/Ahmetyasin/DS-Fine-Tuning-Embedding/tree/main)

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This dataset is designed to enable better embeddings for domain-specific tasks, particularly in the data science field. Feel free to explore and use it for your fine-tuning experiments!
