ModelBrief

Train your model your way. ModelBrief analyses it and turns the results into a complete model report.

ModelBrief accepts an already-trained model plus any available train, validation, and test data. It never calls fit, never retrains the model, and produces console, standalone HTML, and PDF reports.

Overview

ModelBrief is an open-source Python library for analysing trained machine-learning models and automatically generating structured reports. Reports can include:

Model overview and parameters Dataset summary Task detection Feature importance Model performance and evaluation metrics Confusion matrix Error analysis Visualisations Optional AI-generated explanation and recommendations

Supported workflows

• Scikit-learn classifiers, regressors, clusters and compatible estimators
• XGBoost, LightGBM and CatBoost through optional extras
• PyTorch and TensorFlow/Keras classification models
• CNN/image classification data, plus extensible detection and segmentation metrics
• NLP pipelines, PyTorch/TensorFlow text models, token and text error utilities
• Regression, classification, clustering, time-series forecast evaluation and anomaly detection

Deep-learning inputs differ by project. Pass model-ready tensors or arrays. For custom data loaders, output schemas, object detection, generation, segmentation, or unusual prediction APIs, subclass BaseAdapter and pass adapter=YourAdapter(model).

ModelBrief(model, X_test=X_test, y_test=y_test, task="time_series")
ModelBrief(model, X_test=images, y_test=labels, task="computer_vision")
ModelBrief(model, X_test=texts, y_test=labels, task="nlp")
ModelBrief(model, X_test=X, task="clustering")

Design guarantees

• The supplied model is treated as trained and is never fitted.
• Validation is optional and independently reported when present.
• Optional frameworks are not heavily imported.
• A failed optional analysis is recorded as a warning rather than destroying the whole report.
• HTML is standalone. PDF creation uses ReportLab.

Current version: 0.2.0 Python: 3.10+

Quick start

pip install modelbrief

from modelbrief import ModelBrief

report = ModelBrief(
    model=model,
    X_train=X_train, y_train=y_train,
    X_val=X_val, y_val=y_val,       # optional
    X_test=X_test, y_test=y_test,
    ai=False,
)
report.show()
report.pdf("reports/model.pdf")
report.html("reports/model.html")

Reports include: Model overview, Dataset, Task, Parameters, Feature importance, Model performance, Confusion matrix, Error analysis, Charts, and Optional AI guidance.

Framework-specific dependencies remain optional.

pip install modelbrief
pip install "modelbrief[xgboost]"
pip install "modelbrief[lightgbm]"
pip install "modelbrief[catboost]"
pip install "modelbrief[pytorch]"       # includes torch and torchvision
pip install "modelbrief[tensorflow]"
pip install "modelbrief[vision]"
pip install "modelbrief[nlp]"           # transformers and torch
pip install "modelbrief[ai]"            # Groq SDK
pip install "modelbrief[all]"           # every integration

Setting Up the Development Environment including pytest

git clone <your-fork-url>
cd modelbrief
python -m venv .venv
# Windows: .venv\Scripts\activate
# macOS/Linux: source .venv/bin/activate
pip install -e ".[dev]"
pytest

Groq AI, optional

1. Create or retrieve an API key from GroqCloud.
2. Install the optional client: pip install "modelbrief[ai]".
3. Set the key in the environment. Never put it in source code or commit it.

PowerShell:

$env:GROQ_API_KEY="your-api-key"
$env:MODELBRIEF_GROQ_MODEL="openai/gpt-oss-20b" # optional

Windows CMD:

set GROQ_API_KEY=your-api-key
set MODELBRIEF_GROQ_MODEL=openai/gpt-oss-20b

macOS/Linux:

export GROQ_API_KEY="your-api-key"
export MODELBRIEF_GROQ_MODEL="openai/gpt-oss-20b" # optional

Install or Update

Using pip: pip install --upgrade modelbrief # Get the latest features!

Run tests:

pytest

Then use ModelBrief(..., ai=True). With ai=False, Groq is neither imported nor called. AI output is supplementary and should be reviewed by a qualified human.

contributing,workflow and example

Contributions are welcome! ModelBrief is intended to be an open-source project where developers and data scientists can add adapters, analyses, visualisations, tests and improvements.

See examples/ for classification, regression, clustering, time series, anomaly detection, PyTorch, TensorFlow, computer vision, CNN, object detection, segmentation, NLP, sentiment, Transformers, and text generation examples.

Fork, clone, create a branch, install .[dev], add code and tests, run pytest, and open a pull request. Keep framework-specific dependencies optional. Do not commit secrets, datasets, checkpoints, or large model weights.

Branching Strategy

• main → Production-ready code
• dev → Active development
• feature/* → New features
• fix/* → Bug fixes

Maintainers

ModelBrief is maintained by data scientist and software developer:

Name	GitHub	Email
Chidiebere Njoku	@ChidiebereNjoku	chidexnj@gmail.com
Abayomi Olagunju	@jerryola1	https://abayomiolagunju.net/
Micheal Adegoro	@micheal	adegboromicheal@gmail.com

License

Apache License 2.0