skeval

Not just what the model says — but what it means.

skeval is a lightweight, production-ready Python library for semantic sentence classification and LLM output evaluation. It fills the gap that standard benchmarks leave open: distinguishing what kind of language a model uses — facts, emotions, opinions, and instructions — rather than just measuring fluency or accuracy.

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Table of Contents

• Overview
• Technical Stack
• Getting Started
• Core Features
• CLI Reference
• API Reference
• Development Workflow
• Troubleshooting & FAQ
• Maintenance & Support
• License

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Overview

Most LLM evaluation focuses on accuracy, BLEU/ROUGE scores, or reasoning benchmarks. Real-world language understanding also requires distinguishing facts from opinions, detecting emotions, and identifying intent.

skeval provides a modular semantic classification and evaluation layer that works directly with LLM outputs, custom datasets, and benchmark pipelines.

Target users: ML engineers, NLP researchers, and LLM application developers who need semantic-level evaluation beyond token-level metrics.

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Technical Stack

Layer	Technology	Version
Language	Python	>=3.9
Neural network	PyTorch	>=2.6.0, <3.0.0
ML interface	scikit-learn	>=1.3.0, <2.0.0
Data handling	pandas	>=2.0.0, <3.0.0
Numerics	NumPy	>=1.24.0, <3.0.0
Progress bars	tqdm	>=4.66.0, <5.0.0
Docs	Sphinx + RTD theme	>=7.3.0
Linting	black, flake8, isort	>=24.0.0
Security	bandit, pip-audit	>=1.7.8

Architecture: EmbeddingBag (bag-of-words averaging) → Linear (class logits) — fast, CPU-friendly, sklearn-compatible.

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Getting Started

Prerequisites

• Python 3.9, 3.10, 3.11, or 3.12
• pip >=23.0

Installation

From PyPI (recommended):

pip install skeval

From source:

git clone https://github.com/skeval-ai/skeval.git
cd skeval
pip install -e .

With optional transformer backend (v0.3.0+):

pip install skeval[transformers]

Quick Start

from skeval.classifier import SentenceClassifier
from skeval.evaluator import Evaluator

# 1. Define training data
sentences = [
    "Water boils at 100 degrees Celsius",
    "I feel sad today",
    "I think this movie is amazing",
    "Please close the door",
]
labels = ["fact", "emotion", "opinion", "instruction"]

# 2. Train
classifier = SentenceClassifier(embed_dim=64, epochs=20)
classifier.fit(sentences, labels)

# 3. Predict
predictions = classifier.predict([
    "The sky is blue",
    "I am so happy",
    "I believe dogs are better than cats",
    "Turn off the lights",
])

# 4. Evaluate
evaluator = Evaluator()
results = evaluator.evaluate(predictions, ["fact", "emotion", "opinion", "instruction"])
print(results["accuracy"])
print(results["per_class"])

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Core Features

Semantic Classification

Classify sentences into four built-in categories — or any custom taxonomy you define:

Label	Example
fact	"Water boils at 100 degrees Celsius"
emotion	"I feel so happy today"
opinion	"I think this film is overrated"
instruction	"Please close the door"

classifier = SentenceClassifier(embed_dim=64, epochs=30, lr=0.01)
classifier.fit(sentences, labels)
predictions = classifier.predict(new_sentences)

Probability Outputs

Get per-class confidence scores — compatible with LIME, SHAP, and ONNX:

proba = classifier.predict_proba(["The sky is blue"])
# shape: (1, 4) — one probability per class
print(proba[0])  # e.g. [0.82, 0.05, 0.08, 0.05]

Validation Split & Early Stopping

classifier = SentenceClassifier(
    embed_dim=64,
    epochs=100,
    val_split=0.2,   # hold out 20% for validation
    patience=5,      # stop if no improvement for 5 epochs
    random_state=42,
)
classifier.fit(sentences, labels)

sklearn Integration

Works directly with GridSearchCV, Pipeline, and cross_val_score:

from sklearn.model_selection import GridSearchCV
from skeval.classifier import SentenceClassifier

param_grid = {"embed_dim": [32, 64, 128], "epochs": [10, 20]}
grid = GridSearchCV(SentenceClassifier(random_state=0), param_grid, cv=3)
grid.fit(sentences, labels)
print(grid.best_params_)

Model Persistence

# Save
classifier.save("saved_model/")
# Writes: saved_model/model.pt + saved_model/metadata.json

# Load in a new session
classifier = SentenceClassifier()
classifier.load("saved_model/")
predictions = classifier.predict(["Water is wet"])

Dataset Utilities

from skeval.dataset.loader import DatasetLoader

# CSV
sentences, labels = DatasetLoader.load_csv(
    "data/train.csv", text_col="text", label_col="label"
)

# JSON Lines
sentences, labels = DatasetLoader.load_json(
    "data/train.jsonl", text_key="text", label_key="label"
)

Evaluation Metrics

from skeval.evaluator import Evaluator

results = Evaluator().evaluate(predictions, ground_truth)

Returns:

Key	Description
accuracy	Overall fraction of correct predictions
per_class	{label: {precision, recall, f1-score, support}}
macro_avg	Unweighted average across classes
weighted_avg	Support-weighted average across classes
confusion_matrix	2-D list — rows = true, columns = predicted
labels	Sorted list of all class names

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CLI Reference

After installation, the skeval command is available:

skeval --help
skeval --version

Train

skeval train \
    --data data/train.csv \
    --text-col text \
    --label-col label \
    --save-dir saved_model/ \
    --embed-dim 64 \
    --epochs 20 \
    --batch-size 32 \
    --lr 0.005

Argument	Required	Default	Description
--data	Yes	—	Path to .csv or .jsonl training file
--text-col	Yes	—	Column name for sentence text
--label-col	Yes	—	Column name for labels
--save-dir	Yes	—	Directory to write model.pt and metadata.json
--embed-dim	No	64	Embedding dimension
--epochs	No	10	Number of training epochs
--batch-size	No	32	Mini-batch size
--lr	No	0.005	Learning rate

Evaluate

skeval evaluate \
    --model-dir saved_model/ \
    --data data/test.csv \
    --text-col text \
    --label-col label \
    --output report.json

Argument	Required	Default	Description
--model-dir	Yes	—	Directory containing saved model
--data	Yes	—	Path to .csv or .jsonl test file
--text-col	Yes	—	Column name for text
--label-col	Yes	—	Column name for labels
--output	No	—	Optional path to save JSON results

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API Reference

SentenceClassifier

from skeval.classifier import SentenceClassifier

Constructor

SentenceClassifier(
    embed_dim: int = 64,
    epochs: int = 5,
    batch_size: int = 32,
    lr: float = 0.005,
    random_state: int | None = None,
    num_workers: int = 0,
    pin_memory: bool = False,
    val_split: float = 0.0,
    patience: int = 0,
)

Methods

Method	Signature	Description
fit	fit(X, y) -> self	Build vocabulary and train on labelled sentences
predict	predict(X) -> list[str]	Predict class labels
predict_proba	predict_proba(X) -> np.ndarray	Softmax probabilities, shape (n, n_classes)
score	score(X, y) -> float	Mean accuracy
save	save(save_dir)	Persist model and vocabulary to disk
load	load(save_dir)	Restore model from disk

Evaluator

from skeval.evaluator import Evaluator

results = Evaluator().evaluate(predictions, ground_truth)

Returns a dict with keys: accuracy, per_class, macro_avg, weighted_avg, confusion_matrix, labels.

DatasetLoader

from skeval.dataset.loader import DatasetLoader

sentences, labels = DatasetLoader.load_csv(path, text_col, label_col)
sentences, labels = DatasetLoader.load_json(path, text_key, label_key)

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Development Workflow

Setup

git clone https://github.com/skeval-ai/skeval.git
cd skeval
python -m venv venv
source venv/bin/activate        # Windows: venv\Scripts\activate
pip install -e ".[dev,docs]"

Branch Naming

Type	Pattern	Example
Feature	feat/<description>	feat/transformer-backend
Bug fix	fix/<description>	fix/predict-empty-input
Docs	docs/<description>	docs/update-usage-rst
CI/CD	ci/<description>	ci/add-coverage-badge

Running Tests

# All tests
pytest tests/ -v

# With coverage report
pytest tests/ --cov=src/skeval --cov-report=term-missing

# Single file
pytest tests/test_sentence_classifier.py -v

Code Quality

black src/ tests/          # format
isort src/ tests/          # sort imports
flake8 src/ tests/         # lint
mypy src/                  # type check
bandit -r src/             # security scan

Pull Request Requirements

• All CI checks must pass: test, lint, build, install-and-import, security-audit
• At least one reviewer approval required
• Branch must be up to date with main before merge
• PRs from forks require maintainer approval before CI runs

CI/CD Pipeline

Check	Trigger	Description
Tests	PR + merge queue	pytest on Python 3.9, 3.10, 3.11, 3.12
Lint	PR + merge queue	black, flake8, isort, mypy
Build	PR + merge queue	package build dry-run
Security	PR + merge queue	bandit + pip-audit
Install	PR + merge queue	pip install + import smoke test
Release	Tag push	publish to PyPI

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Troubleshooting & FAQ

ModuleNotFoundError: No module named 'skeval' Run pip install -e . from the repo root, or pip install skeval from PyPI.

RuntimeError: Model is not fitted. Call fit() or load() first. You called predict() before fit() or load(). Always fit or load before predicting.

TypeError: fit() got an unexpected keyword argument 'epochs' epochs, lr, and batch_size are constructor parameters, not fit() arguments:

# Correct
classifier = SentenceClassifier(epochs=20, lr=0.01)
classifier.fit(sentences, labels)

ValueError: X and y must have the same length Your sentences and labels lists are different lengths. Check your dataset for missing rows.

make html fails in docs/ Run pip install sphinx sphinx-rtd-theme myst-parser then sphinx-build -b html . _build/html from the docs/ directory.

Predictions are all the same class Your training data is likely class-imbalanced or too small. Use at least 10–20 examples per class and increase epochs.

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Maintenance & Support

Release Cycle

Version	Status	Python
0.2.x	Active	3.9 – 3.12
0.1.x	End of life	3.9 – 3.11
0.3.0	In development	3.9 – 3.12

Reporting Issues

• Bug reports: Open an issue with steps to reproduce, Python version, and full error traceback.
• Feature requests: Open an issue with the enhancement label and describe the use case.
• Security vulnerabilities: Do not open a public issue. Email the maintainers directly or use GitHub's private security advisory.

Roadmap (v0.3.0)

• Transformer backend via sentence-transformers
• Multi-label classification support
• Sarcasm detection
• Benchmark dataset release
• Full CLI test coverage

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License

This project is licensed under the MIT License. See LICENSE for the full text.

Copyright (c) 2026 skeval Contributors.

Third-party dependencies are governed by their respective licenses (PyTorch — BSD-3, scikit-learn — BSD-3, NumPy — BSD-3, pandas — BSD-3).

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Contributing

Contributions are welcome. Please read CONTRIBUTING.md before submitting a PR.

Full documentation: skeval.readthedocs.io