docs.giskard.ai Open in urlscan Pro
2606:4700::6811:2052  Public Scan

Form analysis
3 forms found in the DOM

GET search.html

<form class="header__search__container" method="get" action="search.html" role="search">
  <input class="header__search__input" placeholder="Search" name="q" aria-label=" Search">
  <input type="hidden" name="check_keywords" value="yes">
  <input type="hidden" name="area" value="default">
  <label class="close-icon" for="__search">
    <div class="visually-hidden">Hide search</div>
  </label>
</form>

GET search.html

<form class="sidebar-search-container" method="get" action="search.html" role="search">
  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
  <input type="hidden" name="check_keywords" value="yes">
  <input type="hidden" name="area" value="default">
</form>

GET //readthedocs.org/projects/giskard/search/

<form id="flyout-search-form" class="wy-form" target="_blank" action="//readthedocs.org/projects/giskard/search/" method="get">
  <input type="text" name="q" aria-label="Dokumente durchsuchen" placeholder="Dokumente durchsuchen">
</form>

Text Content

Contents Menu Expand Light mode Dark mode Auto light/dark mode
Hide navigation sidebar
Hide table of contents sidebar
Hide search
Toggle site navigation sidebar

Toggle search
Hide search

Toggle Light / Dark / Auto color theme
Star2,729




Getting Started

 * Why Giskard?
 * Quickstart
   Toggle navigation of Quickstart
   * 📚 LLM Quickstart
   * 📊 Tabular Quickstart
   * 🗣️ NLP Quickstart

Open-Source Library

 * 📥 Install the Giskard Python Library
 * 🔍 Scan a model
   Toggle navigation of 🔍 Scan a model
   * 📚 LLM scan
   * 📊 Tabular model scan
   * 🗣️ NLP model scan
   * Advanced scan usage
 * 🧰 RAG Testset Generation
 * 🧪 Customize your tests
   Toggle navigation of 🧪 Customize your tests
   * 👨‍🔬 Create tests
   * 🔪 Create data slices
   * 🔄 Create data transformations
 * 🔁 Integrate your tests
   Toggle navigation of 🔁 Integrate your tests
   * 🚀 Execute your test suite in your CI/CD pipeline
   * 🏃 MLflow
     Toggle navigation of 🏃 MLflow
     * MLflow Example - LLM
     * MLFlow Example - Tabular
   * 🐝 Weights & Biases
     Toggle navigation of 🐝 Weights & Biases
     * W&B Example - LLM
     * W&B Example - Tabular
   * 🧪 Pytest
     Toggle navigation of 🧪 Pytest
     * Example script

Giskard Hub

 * 🌐 Install the Giskard Hub
   Toggle navigation of 🌐 Install the Giskard Hub
   * 🤗 HuggingFace Spaces
   * 🏠 On-Premise
   * ☁️ Private Cloud
     Toggle navigation of ☁️ Private Cloud
     * AWS
     * Azure
     * GCP
 * ⬆️ Log datasets & models in the Hub
 * 👨‍🔬 Add domain-specific tests
 * 🧐 Debug your issues
 * ⚖️ Compare models
 * 🤝 Collaborate to build better models

Tutorials

 * LLM Tutorials
   Toggle navigation of LLM Tutorials
   * LLM Question Answering over the IPCC Climate Change Report
   * LLM Question Answering with Langchain, Qdrant and OpenAI
   * LLM Question Answering over the 2022 Winter Olympics Wikipedia articles
   * LLM product description from keywords
   * LLM Newspaper Comments Generation with LangChain and OpenAI
   * LLM Question Answering over the documentation with Langchain, FAISS and
     OpenAI
 * Tabular Tutorials
   Toggle navigation of Tabular Tutorials
   * 📊 Tabular Quickstart
   * Breast cancer detection [XGBoost]
   * Customer churn prediction [LGBM]
   * German credit scoring [scikit-learn]
   * Drug classification [scikit-learn]
   * IEEE Fraud detection adversarial validation [LGBM]
   * Insurance charges prediction [LGBM]
   * M5 Sales prediction [LGBM]
   * Wage classification [scikit-learn]
 * NLP Tutorials
   Toggle navigation of NLP Tutorials
   * Twitter sentiment analysis using RoBERTa model [HuggingFace]
   * Airline tweets sentiment analysis [HuggingFace]
   * Amazon reviews classification [scikit-learn]
   * ENRON email classification [scikit-learn]
   * Fake/real news classification [tensorflow (keras)]
   * Regression on the hotel reviews [scikit-learn]
   * Medical transcript classification [scikit-learn]
   * Movie Review Sentiment Classification with DISTILL-BERT [scikit-learn +
     torch preprocessing]
   * Newspaper classification [PyTorch]
   * Tripadvisor reviews sentiment classification [HuggingFace]

Knowledge

 * LLM Vulnerabilities
 * How does the LLM Scan work?
 * ML Model Vulnerabilities
   Toggle navigation of ML Model Vulnerabilities
   * Performance Bias
   * Unrobustness
   * Overconfidence
   * Underconfidence
   * Unethical behaviour
   * Data Leakage
   * Stochasticity
   * Spurious correlation
 * Catalogs
   Toggle navigation of Catalogs
   * Tests
     Toggle navigation of Tests
     * Classification tests
     * Regression tests
     * Text generation tests
   * Slicing functions
   * Transformation functions

Integrations

 * 🐙️ GitHub
   Toggle navigation of 🐙️ GitHub
   * 🚀 Execute your test suite in your CI/CD pipeline
 * 🏃 MLflow
   Toggle navigation of 🏃 MLflow
   * MLflow Example - LLM
   * MLFlow Example - Tabular
 * 🐝 Weights & Biases
   Toggle navigation of 🐝 Weights & Biases
   * W&B Example - LLM
   * W&B Example - Tabular
 * 🐶 DagsHub
 * 🤗 HuggingFace
 * 📒 AVID
   Toggle navigation of 📒 AVID
   * Reporting Giskard LLM Scans to AVID
 * 🧪 Pytest
   Toggle navigation of 🧪 Pytest
   * Example script

API Reference

 * Command-line interface
   Toggle navigation of Command-line interface
   * Setup a ngrok account
 * Models
   Toggle navigation of Models
   * Base model classes
   * Catboost models
   * Prediction function
   * HuggingFace models
   * Langchain models
   * Pytorch models
   * Sklearn models
   * Tensorflow models
 * Dataset
 * Model Scanner
   Toggle navigation of Model Scanner
   * Scan Report
   * Tabular & NLP Detectors
   * Detectors for LLM models
 * RAG Toolset
   Toggle navigation of RAG Toolset
   * Testset Generation
   * Vector Store
   * Correctness Evaluator
 * Tests
   Toggle navigation of Tests
   * Metamorphic tests
   * Statistical tests
   * Performance tests
   * Drift tests
   * LLM tests
   * Data quality tests
 * Slicing functions
 * Transformation functions
 * Automated model insights
 * Test suite

Community

 * Discord community
 * GitHub community
 * Contribute to Giskard
   Toggle navigation of Contribute to Giskard
   * How to configure local development environment
   * Giskard architecture
   * Configuration

Advertising for Developers Reach your niche with powerful contextual targeting
powered by ML
Ad by EthicalAds   ·   ℹ️
  v: stable
  v: stable
Versionen latest stable gsk-2892-rework-question-generation Auf Read the Docs
Projektstartseite Erstellungsprozesse Downloads Auf GitHub Ansehen Suche


--------------------------------------------------------------------------------

Bereitgestellt von Read the Docs · Datenschutz-Bestimmungen
Back to top
Edit this page
Toggle Light / Dark / Auto color theme
Toggle table of contents sidebar
Star2,729










THE TESTING FRAMEWORK DEDICATED TO ML MODELS, FROM TABULAR TO LLMS

Blog • Website • Discord



GETTING STARTED


OPEN-SOURCE LIBRARY


GISKARD HUB


TUTORIALS


KNOWLEDGE


INTEGRATIONS


API REFERENCE


COMMUNITY

Next
Why Giskard?
Copyright © 2024, Giskard AI
Made with Sphinx and @pradyunsg's Furo



word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word word word word word word word word word
word word word word word word word word

mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1
mmMwWLliI0fiflO&1