is-it-slop-preprocessing 0.6.0a2

Fast TF-IDF vectorization. Preprocessing step for `is-it-slop` package, written in Rust.

is-it-slop-preprocessing

Fast TF-IDF text vectorization for training AI text detection models.

Implementation in Rust with Python bindings.

Note for inference users: If you only want to use the AI text detection model for predictions, install is-it-slop instead. This preprocessing library is primarily for the training step or accessing the preprocessing pipeline directly.

The Python bindings allow us to use the same Rust-based text preprocessing at training and inference time, ensuring consistency between model training and deployment.

Features

• Token n-grams: Uses tiktoken BPE token sequences (not characters/words)
• sklearn-compatible API: Drop-in replacement for training pipelines
• Parallel processing: Automatic multi-threading via Rust/rayon
• Multiple serialization formats: rkyv (default), bincode, and JSON support

Installation

pip install is-it-slop-preprocessing

Quick Start

from is_it_slop_preprocessing import TfidfVectorizer, VectorizerParams

# Configure vectorizer (n-gram range is fixed at 2-4 tokens)
params = VectorizerParams(
    min_df=10,           # Ignore terms in < 10 docs
    max_df=0.8,          # Ignore terms in > 80% of docs
    sublinear_tf=True    # Apply log scaling to term frequencies
)

# Fit and transform training data
vectorizer, X_train = TfidfVectorizer.fit_transform(train_texts, params)

# Transform test data
X_test = vectorizer.transform(test_texts)

# Save vectorizer for inference
vectorizer.save("tfidf_vectorizer.rkyv")

Platform Support

Pre-built wheels available for:

• Linux: x86_64, aarch64 (manylinux_2_28)
• macOS: Apple Silicon (ARM64)
• Windows: x86_64

License

MIT