Create readme.md

readme.md

@@ -0,0 +1,66 @@
+# Common Crawl WET Dataset - c2
+
+This repository contains a large-scale filtered dataset derived from the WET files of the Common Crawl project. The data is cleaned and aggregated to facilitate large-scale natural language processing tasks, especially the pretraining of large language models (LLMs).
+
+## Dataset Description
+
+- **Source:** Common Crawl CC-MAIN-2025-38, September 2025 crawl.
+- **Data Type:** Extracted plaintext from web crawl WET files with aggressive metadata and boilerplate filtering.
+- **File Size:** Large combined files (~15GB each) to balance upload size and storage constraints.
+- **Preprocessing:** Streamed extraction, metadata removal, filtering out boilerplate and duplicate content.
+- **Purpose:** Primarily designed for pretraining foundation models and LLMs requiring diverse, massive-scale natural language corpora.
+
+## Features
+
+- **Optimized for Pretraining:**  
+  The dataset is curated and filtered to be suitable for training large language models. It contains clean, high-quality textual data ideal for unsupervised pretraining tasks like masked language modeling or autoregressive modeling.
+
+- **Large Scale:**  
+  Contains processed data amounting to multiple terabytes, allowing training on a broad, diverse text corpus representing a wide range of domains.
+
+- **Streaming Processing:**  
+  The data was processed in a memory-efficient, streaming manner to support large-scale data handling without requiring excessive resources.
+
+- **Metadata Cleaning:**  
+  Extensive removal of WARC, HTTP headers, and other metadata ensures minimal noise in the text used for training.
+
+- **Resume and Verify:**  
+  Processing is checkpointed for fault tolerance. Uploaded files are verified on Hugging Face to avoid duplicates.
+
+- **Immediate Uploads:**  
+  Files are uploaded to Hugging Face immediately after hitting the 15GB size limit to respect limited storage constraints.
+
+
+## Usage
+
+Load the dataset using Hugging Face's `datasets` library:
+
+from datasets import load_dataset
+
+dataset = load_dataset("blue-blue/c2")
+
+After loading, you can iterate over text samples for pretraining models like GPT, BERT, or other large language architectures.
+
+## Pretraining Applications
+
+- **Foundation Model Development:**  
+  Provides diverse, large-scale text data crucial for training high-quality foundation LLMs.
+
+- **Language Modeling Tasks:**  
+  Suitable for autoregressive or masked language model pretraining due to extensive scale and quality.
+
+- **Downstream Adaptation:**  
+  Can be combined with other specialized datasets for fine-tuning or adaptation tasks.
+
+- **Research & Benchmarking:**  
+  Acts as a standard large-scale corpus for benchmarking NLP algorithms and analyzing language model behavior.
+
+## Contact
+
+For questions, support, or collaboration:
+
+[hello@bluesminds.com](mailto:hello@bluesminds.com)
+
+---
+
+Thank you for exploring the **c2** dataset — a foundational resource for large-scale language modeling and NLP research.