Quality Filtering Guide

Complete guide to NeMo Curator's 30+ quality filters.

Text-based filters

Word count

from nemo_curator.filters import WordCountFilter

# Filter by word count
dataset = dataset.filter(WordCountFilter(min_words=50, max_words=100000))

Repeated content

from nemo_curator.filters import RepeatedLinesFilter

# Remove documents with >30% repeated lines
dataset = dataset.filter(RepeatedLinesFilter(max_repeated_line_fraction=0.3))

Symbol ratio

from nemo_curator.filters import SymbolToWordRatioFilter

# Remove documents with too many symbols
dataset = dataset.filter(SymbolToWordRatioFilter(max_symbol_to_word_ratio=0.3))

URL ratio

from nemo_curator.filters import UrlRatioFilter

# Remove documents with many URLs
dataset = dataset.filter(UrlRatioFilter(max_url_ratio=0.2))

Language filtering

from nemo_curator.filters import LanguageIdentificationFilter

# Keep only English documents
dataset = dataset.filter(LanguageIdentificationFilter(target_languages=["en"]))

# Multiple languages
dataset = dataset.filter(LanguageIdentificationFilter(target_languages=["en", "es", "fr"]))

Classifier-based filtering

Quality classifier

from nemo_curator.classifiers import QualityClassifier

quality_clf = QualityClassifier(
    model_path="nvidia/quality-classifier-deberta",
    batch_size=256,
    device="cuda"
)

# Filter low-quality (threshold > 0.5 = high quality)
dataset = dataset.filter(lambda doc: quality_clf(doc["text"]) > 0.5)

NSFW classifier

from nemo_curator.classifiers import NSFWClassifier

nsfw_clf = NSFWClassifier(threshold=0.9, device="cuda")

# Remove NSFW content
dataset = dataset.filter(lambda doc: nsfw_clf(doc["text"]) < 0.9)

Heuristic filters

Full list of 30+ filters:

• WordCountFilter
• RepeatedLinesFilter
• UrlRatioFilter
• SymbolToWordRatioFilter
• NonAlphaNumericFilter
• BulletsFilter
• WhiteSpaceFilter
• ParenthesesFilter
• LongWordFilter
• And 20+ more...

Best practices

1. Apply cheap filters first - Word count before GPU classifiers
2. Tune thresholds on sample - Test on 10k docs before full run
3. Use GPU classifiers sparingly - Expensive but effective
4. Chain filters efficiently - Order by cost (cheap → expensive)