Download Data

Load text data from ArXiv, Common Crawl, Wikipedia, and custom sources using Curator.

Curator provides a task-centric pipeline for downloading and processing large-scale public text datasets. It runs on Ray and converts raw formats like Common Crawl’s .warc.gz into JSONL.

How it Works

Curator uses a 4-step pipeline pattern where data flows through stages as tasks. Each step uses a ProcessingStage that transforms tasks according to Curator’s pipeline-based architecture .

Data sources provide composite stages that combine these steps into complete download-and-extract pipelines, producing DocumentBatch tasks for further processing.

Python

1 from nemo_curator.core.client import RayClient
2 from nemo_curator.pipeline import Pipeline
3 from nemo_curator.stages.text.download import CommonCrawlDownloadExtractStage
4 from nemo_curator.stages.text.io.writer import JsonlWriter
5 
6 # Initialize Ray client
7 ray_client = RayClient()
8 ray_client.start()
9 
10 # Create a pipeline for downloading Common Crawl data
11 pipeline = Pipeline(
12     name="common_crawl_download",
13     description="Download and process Common Crawl web archives"
14 )
15 
16 # Add data loading stage
17 cc_stage = CommonCrawlDownloadExtractStage(
18     start_snapshot="2020-50",
19     end_snapshot="2020-50",
20     download_dir="/tmp/cc_downloads",
21     crawl_type="main",
22     url_limit=10  # Limit for testing
23 )
24 pipeline.add_stage(cc_stage)
25 
26 # Add writer stage to save as JSONL
27 writer = JsonlWriter(path="/output/folder")
28 pipeline.add_stage(writer)
29 
30 # Execute pipeline
31 results = pipeline.run()
32 
33 # Stop Ray client
34 ray_client.stop()

Data Sources & File Formats

Load data from public datasets and custom data sources using Curator stages.

Read Existing Data

Read existing JSONL and Parquet datasets using Curator’s reader stages jsonl parquet

Common Crawl

Download and extract web archive data from Common Crawl web-data warc html-extraction

Wikipedia

Download and extract Wikipedia articles from Wikipedia dumps articles multilingual xml-dumps

Custom Data Sources

Implement a download and extract pipeline for a custom data source jsonl parquet file-partitioning

Load text data from ArXiv, Common Crawl, Wikipedia, and custom sources using Curator.

Curator provides a task-centric pipeline for downloading and processing large-scale public text datasets. It runs on Ray and converts raw formats like Common Crawl’s .warc.gz into JSONL.

How it Works

Curator uses a 4-step pipeline pattern where data flows through stages as tasks. Each step uses a ProcessingStage that transforms tasks according to Curator’s pipeline-based architecture .

Data sources provide composite stages that combine these steps into complete download-and-extract pipelines, producing DocumentBatch tasks for further processing.

Python

1 from nemo_curator.core.client import RayClient
2 from nemo_curator.pipeline import Pipeline
3 from nemo_curator.stages.text.download import CommonCrawlDownloadExtractStage
4 from nemo_curator.stages.text.io.writer import JsonlWriter
5 
6 # Initialize Ray client
7 ray_client = RayClient()
8 ray_client.start()
9 
10 # Create a pipeline for downloading Common Crawl data
11 pipeline = Pipeline(
12     name="common_crawl_download",
13     description="Download and process Common Crawl web archives"
14 )
15 
16 # Add data loading stage
17 cc_stage = CommonCrawlDownloadExtractStage(
18     start_snapshot="2020-50",
19     end_snapshot="2020-50",
20     download_dir="/tmp/cc_downloads",
21     crawl_type="main",
22     url_limit=10  # Limit for testing
23 )
24 pipeline.add_stage(cc_stage)
25 
26 # Add writer stage to save as JSONL
27 writer = JsonlWriter(path="/output/folder")
28 pipeline.add_stage(writer)
29 
30 # Execute pipeline
31 results = pipeline.run()
32 
33 # Stop Ray client
34 ray_client.stop()

Data Sources & File Formats

Load data from public datasets and custom data sources using Curator stages.

Read Existing Data

Read existing JSONL and Parquet datasets using Curator’s reader stages jsonl parquet

Common Crawl

Download and extract web archive data from Common Crawl web-data warc html-extraction

Wikipedia

Download and extract Wikipedia articles from Wikipedia dumps articles multilingual xml-dumps

Custom Data Sources

Implement a download and extract pipeline for a custom data source jsonl parquet file-partitioning

1	from nemo_curator.core.client import RayClient
2	from nemo_curator.pipeline import Pipeline
3	from nemo_curator.stages.text.download import CommonCrawlDownloadExtractStage
4	from nemo_curator.stages.text.io.writer import JsonlWriter
5
6	# Initialize Ray client
7	ray_client = RayClient()
8	ray_client.start()
9
10	# Create a pipeline for downloading Common Crawl data
11	pipeline = Pipeline(
12	name="common_crawl_download",
13	description="Download and process Common Crawl web archives"
14	)
15
16	# Add data loading stage
17	cc_stage = CommonCrawlDownloadExtractStage(
18	start_snapshot="2020-50",
19	end_snapshot="2020-50",
20	download_dir="/tmp/cc_downloads",
21	crawl_type="main",
22	url_limit=10 # Limit for testing
23	)
24	pipeline.add_stage(cc_stage)
25
26	# Add writer stage to save as JSONL
27	writer = JsonlWriter(path="/output/folder")
28	pipeline.add_stage(writer)
29
30	# Execute pipeline
31	results = pipeline.run()
32
33	# Stop Ray client
34	ray_client.stop()