Distributed Data Classification#

NVIDIA NeMo Curator provides a module for performing distributed classification on large text datasets using GPU acceleration. This enables the categorization and filtering of text documents based on multiple dimensions such as domain, quality, safety, educational value, content type, and more. These classifications can enhance the quality of training data for large language models by identifying high-value content and removing problematic material.

How It Works#

The distributed data classification in NeMo Curator works by:

Parallel Processing: Chunking datasets across multiple computing nodes and GPUs to accelerate classification
Pre-trained Models: Using specialized models for different classification tasks
Batched Inference: Optimizing throughput with intelligent batching
Consistent API: Providing a unified interface through the DistributedDataClassifier base class

The DistributedDataClassifier is designed to run on GPU clusters with minimal code changes regardless of which specific classifier you’re using. All classifiers support filtering based on classification results and storing prediction scores as metadata.

Note

Distributed classification requires GPU acceleration and is not supported for CPU-only processing. As long as GPU resources are available and NeMo Curator is correctly installed, GPU acceleration is handled automatically.

Tip

Running the tutorial notebooks: The classification tutorial notebooks require the text_cuda12 or all installation extra to include all relevant dependencies. If you encounter ModuleNotFoundError, reinstall with the appropriate extra:

uv pip install "nemo-curator[text_cuda12]"

When using classifiers that download from Hugging Face (such as Aegis and InstructionDataGuard), set your HF_TOKEN environment variable to avoid rate limiting:

export HF_TOKEN="your_token_here"

Usage#

NVIDIA NeMo Curator provides a base class DistributedDataClassifier that can be extended to fit your specific model. The only requirement is that the model can fit on a single GPU. This module operates on the GPU and works within the pipeline framework using DocumentBatch processing.

Classifier Comparison#

Classifier	Purpose	Model Location	Key Parameters	Requirements
DomainClassifier	Assigns one of 26 domain labels (such as “Sports,” “Science,” “News”) to English text	nvidia/domain-classifier	`filter_by`, `text_field`	None
MultilingualDomainClassifier	Assigns domain labels to text in 52 languages; same labels as DomainClassifier	nvidia/multilingual-domain-classifier	`filter_by`, `text_field`	None
QualityClassifier	Rates document quality as “Low,” “Medium,” or “High” using a DeBERTa model	nvidia/quality-classifier-deberta	`filter_by`, `text_field`	None
AegisClassifier	Detects unsafe content across 13 risk categories (violence, hate speech, and others) using LlamaGuard	nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0	`aegis_variant`, `filter_by`	HuggingFace token
InstructionDataGuardClassifier	Identifies LLM poisoning attacks in instruction-response pairs	nvidia/instruction-data-guard	`text_field`, `label_field`	HuggingFace token
FineWebEduClassifier	Scores educational value from 0 to 5 (0=spam, 5=scholarly) for training data selection	HuggingFaceFW/fineweb-edu-classifier	`label_field`, `int_field`	None
FineWebMixtralEduClassifier	Scores educational value from 0 to 5 using Mixtral 8x22B annotation data	nvidia/nemocurator-fineweb-mixtral-edu-classifier	`label_field`, `int_field`, `model_inference_batch_size=1024`	None
FineWebNemotronEduClassifier	Scores educational value from 0 to 5 using Nemotron-4-340B annotation data	nvidia/nemocurator-fineweb-nemotron-4-edu-classifier	`label_field`, `int_field`, `model_inference_batch_size=1024`	None
ContentTypeClassifier	Categorizes text into 11 speech types (such as “Blogs,” “News,” “Academic”)	nvidia/content-type-classifier-deberta	`filter_by`, `text_field`	None
PromptTaskComplexityClassifier	Labels prompts by task type (such as QA and summarization) and complexity dimensions	nvidia/prompt-task-and-complexity-classifier	`text_field`	None

Domain Classifier#

The Domain Classifier categorizes English text documents into specific domains or subject areas.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import DomainClassifier

# Create pipeline
pipeline = Pipeline(name="domain_classification")

# Load dataset
reader = JsonlReader(
    file_paths="books_dataset/",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the classifier, filtering for specific domains
domain_classifier = DomainClassifier(filter_by=["Games", "Sports"])
pipeline.add_stage(domain_classifier)

# Save the results
writer = JsonlWriter(path="games_and_sports/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

Multilingual Domain Classifier#

Functionally similar to the Domain Classifier, but supports 52 languages.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import MultilingualDomainClassifier

pipeline = Pipeline(name="multilingual_domain_classification")
pipeline.add_stage(JsonlReader(file_paths="multilingual_dataset/", fields=["text", "id"]))
pipeline.add_stage(MultilingualDomainClassifier(filter_by=["Games", "Sports"]))
pipeline.add_stage(JsonlWriter(path="classified_output/"))

results = pipeline.run()  # Uses XennaExecutor by default

Quality Classifier#

The Quality Classifier assesses document quality using the NVIDIA Quality Classifier DeBERTa model.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import QualityClassifier

pipeline = Pipeline(name="quality_classification")
pipeline.add_stage(JsonlReader(file_paths="web_documents/", fields=["text", "id"]))
pipeline.add_stage(QualityClassifier())
pipeline.add_stage(JsonlWriter(path="quality_classified/"))

results = pipeline.run()  # Uses XennaExecutor by default

Note

The exact label categories returned by the Quality Classifier depend on the model configuration. Check the prediction column in your results to see the available labels for filtering with the filter_by parameter.

AEGIS Safety Classifier#

The AEGIS classifier detects unsafe content across 13 critical risk categories. It requires a HuggingFace token for access to Llama Guard.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import AegisClassifier

# Create pipeline
pipeline = Pipeline(name="aegis_classification")

# Load dataset
reader = JsonlReader(
    file_paths="content/",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the AEGIS classifier
token = "hf_1234"  # Your HuggingFace user access token
safety_classifier = AegisClassifier(
    aegis_variant="nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0",
    hf_token=token,
    filter_by=["safe", "O13"]  # Keep only safe content and "needs caution" category
)
pipeline.add_stage(safety_classifier)

# Save the results
writer = JsonlWriter(path="safe_content/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

The classifier adds a column with labels: “safe,” “O1” through “O13” (each representing specific safety risks), or “unknown.” For raw LLM output, use:

safety_classifier = AegisClassifier(
    aegis_variant="nvidia/Aegis-AI-Content-Safety-LlamaGuard-Defensive-1.0",
    hf_token=token,
    keep_raw_output=True,
    raw_output_field="raw_predictions"
)

Instruction Data Guard#

Detects LLM poisoning attacks in instruction-response datasets. Requires HuggingFace token access.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import InstructionDataGuardClassifier

# Create pipeline
pipeline = Pipeline(name="instruction_data_guard")

# Load dataset
# For instruction-response data: "Instruction: {instruction}. Input: {input_}. Response: {response}."
reader = JsonlReader(
    file_paths="instruction_data/",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the classifier
token = "hf_1234"  # Your HuggingFace user access token
classifier = InstructionDataGuardClassifier(hf_token=token)
pipeline.add_stage(classifier)

# Save the results
writer = JsonlWriter(path="guard_classified/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

The output includes two columns: a float score instruction_data_guard_poisoning_score and a Boolean is_poisoned.

FineWeb Educational Content Classifier#

Scores documents on educational value from 0–5. This helps prioritize content for knowledge-intensive tasks.

Score Ranges and Meanings#

Score	Label	Description	Example Content
0-1	Very Low	No educational value	Spam, advertisements, broken content
2	Low	Minimal educational content	Simple lists, basic product descriptions
3	Moderate	Some educational value	News articles, basic how-to guides
4	High	Good educational content	Detailed tutorials, academic discussions
5	Very High	Excellent educational material	Comprehensive guides, scholarly articles

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import FineWebEduClassifier

# Create pipeline
pipeline = Pipeline(name="fineweb_edu_classification")

# Load dataset
reader = JsonlReader(
    file_paths="web_documents/*.jsonl",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the FineWeb Edu classifier
edu_classifier = FineWebEduClassifier(
    model_inference_batch_size=256,
    float_score_field="fineweb-edu-score-float",  # Raw float scores
    int_score_field="fineweb-edu-score-int",      # Rounded integer scores
    label_field="fineweb-edu-score-label"         # Quality labels
)
pipeline.add_stage(edu_classifier)

# Save the results
writer = JsonlWriter(path="edu_classified/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

FineWeb Mixtral and Nemotron Edu Classifiers#

Similar to the FineWeb Edu Classifier but trained with different annotation sources:

FineWebMixtralEduClassifier: Uses annotations from Mixtral 8x22B-Instruct
FineWebNemotronEduClassifier: Uses annotations from Nemotron-4-340B-Instruct

Both provide a quality label column marking scores above 2.5 as “high_quality”:

Quality Label Mapping#

Score Range	Quality Label	Description
0.0 - 2.5	`low_quality`	Below average educational value
2.5 - 5.0	`high_quality`	Above average educational value

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import FineWebMixtralEduClassifier  # or FineWebNemotronEduClassifier

# Create pipeline
pipeline = Pipeline(name="fineweb_mixtral_edu_classification")

# Load dataset
reader = JsonlReader(
    file_paths="web_documents/*.jsonl",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the FineWeb Mixtral Edu classifier
classifier = FineWebMixtralEduClassifier(
    float_score_field="fineweb-mixtral-edu-score-float",  # Raw float scores
    int_score_field="fineweb-mixtral-edu-score-int",      # Rounded integer scores
    label_field="fineweb-mixtral-edu-score-label"          # "high_quality" or "low_quality"
)
pipeline.add_stage(classifier)

# Save the results
writer = JsonlWriter(path="mixtral_edu_classified/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

Content Type Classifier#

Categorizes documents into 11 distinct speech types.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import ContentTypeClassifier

# Create pipeline
pipeline = Pipeline(name="content_type_classification")

# Load dataset
reader = JsonlReader(
    file_paths="content/",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the Content Type classifier
classifier = ContentTypeClassifier(filter_by=["Blogs", "News"])
pipeline.add_stage(classifier)

# Save the results
writer = JsonlWriter(path="content_type_classified/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

Prompt Task and Complexity Classifier#

Classifies prompts by task type and complexity dimensions.

from nemo_curator.pipeline import Pipeline
from nemo_curator.stages.text.io.reader import JsonlReader
from nemo_curator.stages.text.io.writer import JsonlWriter
from nemo_curator.stages.text.classifiers import PromptTaskComplexityClassifier

# Create pipeline
pipeline = Pipeline(name="prompt_task_complexity_classification")

# Load dataset
reader = JsonlReader(
    file_paths="prompts/",
    fields=["text", "id"]
)
pipeline.add_stage(reader)

# Apply the Prompt Task Complexity classifier
classifier = PromptTaskComplexityClassifier()
pipeline.add_stage(classifier)

# Save the results
writer = JsonlWriter(path="prompt_complexity_classified/")
pipeline.add_stage(writer)

# Execute pipeline
results = pipeline.run()  # Uses XennaExecutor by default

Custom Model Integration#

You can integrate your own classification models by extending DistributedDataClassifier. Refer to the Text Classifiers README for implementation details and examples.

Performance Optimization#

NVIDIA NeMo Curator’s distributed classifiers are optimized for high-throughput processing through several key features:

Intelligent Batching and Sequence Handling#

The classifiers optimize throughput through:

Length-based sorting: Input sequences are sorted by length when sort_by_length=True (default)
Efficient batching: Similar-length sequences are grouped together to minimize padding overhead
GPU memory optimization: Batches are sized to maximize GPU utilization based on available memory