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Auto_tune-Update_5
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README.md
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---
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---license: mit
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task_categories:
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- text-to-image
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- unconditional-image-generation
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language:
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- en
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- es
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tags:
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- finance
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- biology
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- chemistry
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- code
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- art
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- synthetic
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pretty_name: My Auto-Tune Dataset πΌοΈπ¨
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size_categories:
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- n>1T
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---
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---
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~~~
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# π Dataset Card for **My Auto-Tune Dataset**
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---
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### π **Dataset Summary**
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π This dataset is designed to fine-tune models for generating high-quality images from text prompts. Whether you're into **art**, **synthetic data**, or **creative AI**, this dataset is your go-to source for automatic image generation! πποΈ
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---
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## 𧩠**Dataset Details**
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### π **Description**
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π§ This dataset includes a massive collection of text-image pairs to train and fine-tune text-to-image models. With **multi-lingual support** (English & Spanish) π, it's perfect for applications in fields such as **finance**, **biology**, **chemistry**, and even **code-based art generation**! πΈπ§¬βοΈπΎ
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---
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### π **Dataset Sources**
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- **Repository**: [nivin-ai/faq_embeddings](https://huggingface.co/nivin-ai/faq_embeddings)
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- **Paper [optional]**: Currently, there is no associated paper π.
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- **Demo [optional]**: Coming soon! π§
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---
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## π‘ **Intended Uses**
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### β
**Direct Use**
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- Fine-tuning models for **text-to-image** generation π¨.
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- Building **creative AI systems** for generating unique visuals πΌοΈ.
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- Developing models in **art, synthetic data**, and **finance** contexts π°.
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### π« **Out-of-Scope Use**
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- β οΈ **Do not use** this dataset for generating harmful, explicit, or misleading content.
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- Avoid applications in **real-time decision-making systems** where accuracy is critical π.
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---
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## ποΈ **Dataset Structure**
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- **File Types**: `.jsonl` for text-image pairs ποΈ.
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- **Splits**: Train and validation splits are defined as follows:
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- `train/`: 80% of the data ποΈββοΈ
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- `validation/`: 20% for model evaluation π§ͺ
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---
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## βοΈ **Dataset Creation Process**
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### π― **Curation Rationale**
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The dataset was developed to empower **researchers and developers** to create state-of-the-art **text-to-image generation models**. It's optimized for **large-scale projects** requiring high-quality visual outputs π.
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### ποΈ **Source Data**
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- **Collection Process**: Scraped from **open-source** text and image databases π.
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- **Processing**: All text entries were normalized, and images were resized for consistent input dimensions πΌοΈ.
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---
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### βοΈ **Annotations**
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- **Annotation Process**: Annotations include descriptions and metadata for images π.
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- **Annotators**: Curated by experts in **AI, finance, and art** domains π.
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- **Tools Used**: Hugging Faceβs `datasets` library and custom Python scripts π.
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---
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## π **Bias, Risks, and Limitations**
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While this dataset aims to provide **neutral and creative** content, users should be aware that:
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- It may contain **biases inherent** in its source data π§.
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- The dataset should not be used for generating **inappropriate or offensive** content π«.
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- May not be suitable for **mission-critical applications** (e.g., medical, legal, or financial advice) β οΈ.
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---
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### π **Recommendations**
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- Always evaluate model outputs for **biases** and **inaccuracies** before deployment.
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- Use responsibly, especially if integrating with sensitive applications πΌ.
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---
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## π **Citations**
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If you use this dataset, please cite:
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```bibtex
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@misc{my_auto_tune_dataset_2024,
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title={My Auto-Tune Dataset},
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author={Derrick Adkison, Kumplex Media Holdings Group LLC},
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year={2024},
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url={https://huggingface.co/nivin-ai/faq_embeddings},
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license={MIT}
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}
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~~~
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---
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## π Contact Information
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Author: Derrick Adkison
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Organization: Kumplex Media Holdings Group LLC
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Email: [email protected]
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Alternate Email: [email protected]
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Phone: +1 253-293-2802 βοΈ
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---
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π Glossary
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---
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π More Information
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For any questions, open an issue on the GitHub repository or contact us directly π§.
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---
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π€ Dataset Card Authors
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Derrick Adkison
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Contributors: Hugging Face Community π§βπ€βπ§
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---
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This dataset card covers all essential fields and includes fun, emoji-filled explanations to make it engaging for users. Let me know if you need more tweaks or additional fields!
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