is-it-slop 0.6.0a0

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 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)
• Command-line interface: CLI included, no separate installation needed
• Cross-platform: Linux, macOS (Apple Silicon), Windows

Installation

uv add is-it-slop

or using pip:

pip install is-it-slop

or run directly without installing:

uvx is-it-slop "Your text here"

Quick Start

Python API

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%})")

Command-Line Interface

The package includes a CLI that can be used after installation:

# Default: human-readable with probabilities and confidence metrics
is-it-slop "Your text here"

# Classification label only
is-it-slop "Text" --label
# Output: Human

# Label with score
is-it-slop "Text" --label --score
# Output: Human (0.2340)

# Bare AI probability for scripting
is-it-slop "Text" --score
# Output: 0.2340

# Full JSON output
is-it-slop "Text" --json

# JSON lines (one object per line, useful for streaming)
is-it-slop "Text" --jsonl

# Batch processing (auto-detects .json vs line-delimited)
is-it-slop -b texts.txt

# Custom threshold
is-it-slop "Text" --threshold 0.7

# Read from stdin
echo "Your text" | is-it-slop

# Get help
is-it-slop --help

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)
• num_chunks (int): Number of text chunks processed (1 for short texts, more for long)
• chunk_agreement (float): Agreement across chunks (0.5-1.0). 1.0 = all chunks agree

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.

Alternative: Standalone Rust CLI

If you need faster startup time or want a standalone binary without Python, you can install the Rust CLI separately:

cargo install is-it-slop --features cli

The standalone Rust binary has slightly faster startup but requires the Rust toolchain to install. The Python package CLI provides the same functionality and is easier to install.

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