---
dataset_name: dignity045/Collective-Corpus
license: apache-2.0
language: multilingual
size_categories: 500B+ tokens
task_categories:
- text-generation
- fill-mask
- text-classification
- summarization
- question-answering
pretty_name: Collective Corpus
tags:
- pretraining
- finetuning
- large-language-model
- code
- math
- instructions
---

# 🧠 Collective Corpus — Universal Pretraining + Finetuning Dataset (500B+ Tokens)

[![Hugging Face](https://img.shields.io/badge/🤗-Dataset-yellow)](https://huggingface.co/datasets/dignity045/Collective-Corpus)
[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://www.apache.org/licenses/LICENSE-2.0)
[![Status](https://img.shields.io/badge/Status-In%20Progress-orange)](#-current-status)

**`Collective-Corpus`** is a massive-scale, **multi-domain** dataset designed to train Transformer-based language models **from scratch** and **finetune** them across a wide variety of domains — all in one place.

## 📚 Dataset Scope

This dataset aims to **cover the full LLM lifecycle**, from raw pretraining to domain-specialized finetuning.

### 1. Pretraining Corpus
- Large-scale, diverse multilingual text sources
- Cleaned, deduplicated, and filtered for quality
- Inspired by datasets like [C4](https://huggingface.co/datasets/c4) and [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)

### 2. Domain-Specific Finetuning
- **Instruction Following & Dialogue** — Chatbots, multi-turn conversations
- **Code** — Python, JavaScript, Java, C++, and more
- **Math & Logical Reasoning**
- **Specialized Fields** — Research papers, technical documentation

---

## 📊 Scale

- **Total Tokens**: **500B+**
- **Estimated Text Samples**: **700M+**
- **Target Model Size**: Suitable for training large models **from scratch**
- Covers **general-purpose** and **domain-specific** training needs

---

## 🎯 Goals

1. Build a **unified corpus** for full-stack LLM development.
2. Enable **open and reproducible** large-scale language model research.
3. Support **finetuning for high-impact domains** like code, math, and dialogue.

---

## 🚧 Current Status

- **Model Pretraining**: Currently training a Transformer model from scratch on the full **500B+ token** dataset.
- **Public Release**: Planned **after model training completes**.

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## 🤝 Collaboration

We are **actively seeking open-source collaborators** to:
- Contribute to dataset cleaning, filtering, and deduplication
- Assist in large-scale model training and evaluation
- Provide expertise for **specialized domain corpora**

We also **offer free guidance** on:
- Dataset curation best practices
- Efficient large-scale LLM training pipelines
- Transformer architecture optimization

---

## 💼 Open for Collaboration

I’m actively looking to connect with researchers, engineers, and organizations passionate about **dataset engineering**, **large-scale model training**, and **applied NLP**.  
Whether it’s open-source projects, research collaborations, or large-scale AI initiatives — let’s build something impactful together.

🔗 **GitHub**: [Dhiraj309](https://github.com/Dhiraj309)  
🔗 **LinkedIn**: [Dhiraj Patil](https://www.linkedin.com/in/dhiraj-patil-b42262323)

---

## 📅 Release Timeline

| Stage                  | Status           |
|------------------------|------------------|
| Data Curation          | 🚧 In Progress   |
| Model Pretraining      | 🚧 In Progress   |
| Dataset Public Release | ⏳ Post-training |

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## 📜 License

Released under the **Apache License 2.0** — you are free to use, modify, and distribute this dataset in compliance with the [full license text](https://www.apache.org/licenses/LICENSE-2.0).

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### 🌍 Let’s build the next generation of **open-source LLMs** — together.