DigitalAsocial/ds-tb-17-g-sns

Dataset Card: Processed 17 Scientific Books Sentence Pairs using Semantic Node Splitter (SNS)

Dataset Description

Language: English (scientific/technical domain)

License: Research use only (derived from copyrighted books, full texts not redistributed)

Dataset Summary:
This dataset contains sentence pairs extracted from 17 scientific and technical books.
The raw PDFs were processed using GROBID to obtain structured text.
Sentences were segmented with NLTK and paired using a sliding window approach (window size = 3), ensuring both consecutive and slightly distant pairs are included.
To maintain balance across sources, a maximum of 6,000 sentences per book was included.
The dataset is intended for training and evaluating sentence-transformer models on semantic similarity tasks.

Dataset Structure

Data Instances

Each entry is a pair of sentences:

{
  "sentence_0": "Some of the generated samples that had been achieved with this architecture already in 2014 can be seen in Figure 3.14.",
  "sentence_1": "Conditioning on Text: So far, only image generation has been covered, completely ignoring textual input."
}

Data Splits

All data is in the "train" split.

Total size: ~127,058 sentence pairs.

Balanced across 17 books (≤6000 sentences per book).

Dataset Creation

Curation Rationale

The dataset was created to provide high-quality sentence pairs for training and evaluating sentence-transformer models in the scientific domain. Limiting to 6000 sentences per book ensures balanced representation and reduces copyright risks.

Source Data

-> Books: 17 scientific/technical books (copyrighted, not redistributed).

-> Extraction: PDFs processed with GROBID → structured text → sentence segmentation (NLTK).

-> Pairs: Constructed from consecutive sentences and curated positive/negative examples.

Data Extraction Logic

-> Raw PDFs processed with GROBID.

-> Sentences segmented with NLTK.

-> Maximum 6000 sentences per book included.

-> Sentence pairs generated for semantic similarity training.

-> Sentence pairs were generated using a sliding window approach (window size = 3) over semantic nodes extracted with GROBID. This ensures both consecutive and slightly distant pairs are included.

Sources

1. Aßenmacher, Matthias. Multimodal Deep Learning. Self-published, 2023.
2. Bertsekas, Dimitri P. A Course in Reinforcement Learning. Arizona State University.
3. Boykis, Vicki. What are Embeddings. Self-published, 2023.
4. Bruce, Peter, and Andrew Bruce. Practical Statistics for Data Scientists: 50 Essential Concepts. O’Reilly Media, 2017.
5. Daumé III, Hal. A Course in Machine Learning. Self-published.
6. Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong. Mathematics for Machine Learning. Cambridge University Press, 2020.
7. Devlin, Hannah, Guo Kunin, Xiang Tian. Seeing Theory. Self-published.
8. Gutmann, Michael U. Pen & Paper: Exercises in Machine Learning. Self-published.
9. Jung, Alexander. Machine Learning: The Basics. Springer, 2022.
10. Langr, Jakub, and Vladimir Bok. Deep Learning with Generative Adversarial Networks. Manning Publications, 2019.
11. MacKay, David J.C. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
12. Montgomery, Douglas C., Cheryl L. Jennings, and Murat Kulahci. Introduction to Time Series Analysis and Forecasting. 2nd Edition, Wiley, 2015.
13. Nilsson, Nils J. Introduction to Machine Learning: An Early Draft of a Proposed Textbook. Stanford University, 1996.
14. Prince, Simon J.D. Understanding Deep Learning. Draft Edition, 2024.
15. Shashua, Amnon. Introduction to Machine Learning. The Hebrew University of Jerusalem, 2008.
16. Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. 2nd Edition, MIT Press, 2018.
17. Alpaydin, Ethem. Introduction to Machine Learning. 3rd Edition, MIT Press, 2014.

⚠️ Note: Due to copyright restrictions, the full text of these books is not included in this repository. Only the fine-tuned model weights are shared.

Additional Information

Citation

If you use this dataset, please cite:

@misc{aghakhani2025synergsticrag,
  author       = {Danial Aghakhani Zadeh},
  title        = {Processed 17 Scientific Books Sentence Pairs Using SNS},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/DigitalAsocial/ds-tb-17-g-sns}}
}

Personal and Sensitive Information

-> The dataset consists of scientific/technical text.

-> No personal or sensitive information is included.

Bias, Risks, and Limitations

-> Texts reflect the style and biases of their original authors.

-> Dataset is domain-specific (scientific books) and may not generalize to everyday language.

-> Full copyrighted texts are not included; only derived sentence pairs are shared.

Notice and Takedown Policy

If you believe this dataset contains material that infringes copyright, please contact us with:

-> Your contact information

-> Reference to the original work

-> Identification of the material claimed to be infringing

We will comply with legitimate requests by removing affected sources from future releases.

Dataset Curators

Created by Danial.A for research on sentence-transformers and semantic similarity.

License

Derived from copyrighted books.

Shared under Research Use Only license.

Full texts are not redistributed.

Contact

For questions, feedback, or collaboration requests regarding this dataset/model, please contact:

• Name: Danial Aghakhani Zadeh
• Email: [danialaghakhanizadeh@gmail.com]
• GitHub: https://github.com/digitalasocial
• Hugging Face Profile: https://huggingface.co/DigitalAsocial