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---
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:
```python
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:
[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.
## ⚠️ 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. |