is-it-slop 0.5.0

Detect AI-generated slop text using machine learning.

is-it-slop

Fast and accurate AI text detection using machine learning.

Python bindings for the is-it-slop AI text detection library, powered by Rust and ONNX Runtime.

This is the main inference library. For most users, this is all you need. If you want to train custom models or access the preprocessing pipeline directly, see is-it-slop-preprocessing.

Features

• Fast inference: Rust-backed ONNX runtime with optimized preprocessing
• Pre-trained model: Embedded at compile time, no downloads required
• Simple API: Single function call for predictions
• Batch processing: Efficient multi-text inference
• Text chunking: Handles variable-length documents (50-5000+ tokens)
• Cross-platform: Linux, macOS (Apple Silicon), Windows

Installation

uv add is-it-slop

or using pip:

pip install is-it-slop

Quick Start

from is_it_slop import is_this_slop

# Predict on single text
result = is_this_slop("Your text here")
print(result.classification)  # "Human" or "AI"
print(f"AI probability: {result.ai_probability:.2%}")

# Use custom threshold
result = is_this_slop("Your text here", threshold=0.7)
print(result.classification)

# Batch processing
from is_it_slop import is_this_slop_batch

texts = ["First text", "Second text", "Third text"]
results = is_this_slop_batch(texts)
for text, result in zip(texts, results):
    print(f"{text}: {result.classification} ({result.ai_probability:.1%})")

API Reference

Functions

is_this_slop(text, threshold=None)

Predict whether a single text is AI-generated or human-written.

Parameters:

• text (str): Input text string
• threshold (float, optional): Classification threshold (0.0-1.0). If not provided, uses the default optimized threshold.

Returns: PredictionResult object

Example:

result = is_this_slop("This text was written by a human.")
print(result.classification)  # "Human" or "AI"
print(result.ai_probability)  # 0.0 to 1.0
print(result.human_probability)  # 0.0 to 1.0

---

is_this_slop_batch(texts, threshold=None)

Predict whether multiple texts are AI-generated or human-written.

Parameters:

• texts (list[str]): List of text strings
• threshold (float, optional): Classification threshold (0.0-1.0)

Returns: List of PredictionResult objects

Example:

results = is_this_slop_batch([
    "First document to check",
    "Second document to check",
    "Third document to check"
])

for i, result in enumerate(results):
    print(f"Text {i+1}: {result.classification}")

PredictionResult Object

Result object with classification and probabilities:

Attributes:

• classification (str): Either "Human" or "AI"
• human_probability (float): Probability of human-written text (0.0-1.0)
• ai_probability (float): Probability of AI-generated text (0.0-1.0)

Note: human_probability + ai_probability == 1.0

String representation:

>>> result = is_this_slop("Some text")
>>> print(result)
Human (AI: 12.3%)

>>> repr(result)
'PredictionResult(human=0.877, ai=0.123, class=Human)'

Constants

CLASSIFICATION_THRESHOLD

The default threshold value used for classification. This threshold is optimized for overall F1 score based on validation data.

from is_it_slop import CLASSIFICATION_THRESHOLD

print(f"Default threshold: {CLASSIFICATION_THRESHOLD}")

MODEL_VERSION

The version of the embedded model.

from is_it_slop import MODEL_VERSION

print(f"Model version: {MODEL_VERSION}")

How It Works

1. Text Cleaning: Normalizes HTML entities, encoding artifacts, and whitespace
2. Tokenization: Uses tiktoken (o200k_base) BPE encoding
3. Chunking: Splits long texts into 150-token overlapping chunks
4. Vectorization: TF-IDF with 2-4 token n-grams
5. Inference: ONNX Runtime with LogisticRegression model
6. Aggregation: Combines chunk predictions using weighted mean

This pipeline ensures consistent preprocessing between training and inference.

Performance Characteristics

• Short texts (< 150 tokens): Single chunk, instant inference
• Medium texts (150-1000 tokens): ~2-7 chunks, efficient batch processing
• Long texts (1000+ tokens): Automatically chunked and aggregated

The Rust implementation provides significant speedup over pure Python:

• 5-10x faster preprocessing (tokenization, vectorization)
• Parallel batch processing
• Zero-copy operations where possible

Command-Line Interface

For CLI usage, install the Rust binary:

cargo install is-it-slop --features cli

# Basic usage
is-it-slop "Your text here"

# JSON output
is-it-slop "Your text here" --format json

# Classification only (0 or 1)
is-it-slop "Your text here" --format class

Platform Support

Pre-built wheels available for:

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

License

MIT

Links

• PyPI Package
• GitHub Repository
• Preprocessing Library