README.md

metadata

tags:
  - pretraining
  - web

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 easily using the datasets library:

from datasets import load_dataset

dataset = load_dataset("blue-blue/c2")

# Example: Access the first sample
print(dataset["train"][0])

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:

[email protected]

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Thank you for exploring the c2 dataset — a foundational resource for large-scale language modeling and NLP research.

⚠️ Note

This dataset is in update mode — it is continuously expanding and improving as new Common Crawl snapshots are processed and added.
Expect regular additions, refinements, and enhanced cleaning over time.