|
|
--- |
|
|
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: |
|
|
|
|
|
[[email protected]](mailto:[email protected]) |
|
|
|
|
|
--- |
|
|
|
|
|
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. |
