Distributional
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Distributional
    • Welcome to Distributional
    • Motivation
    • What is AI Testing?
    • Stages in the AI Software Development Lifecycle
    • Distributional Testing
    • Getting Access to Distributional
    • The Distributional Framework
    • Defining Tests in Distributional
    • Reviewing Test Sessions and Runs in Distributional
    • Data in Distributional
    • Getting Started
    • Access
    • Data
    • Testing
    • Python SDK
      • Quick Start
      • Functions
      • Objects
      • Experimental Functions
      • Eval Module
        • Quick Start
        • Application Metric Sets
        • How-To / FAQ
        • LLM-as-judge and Embedding Metrics
        • RAG / Question Answer Example
        • Eval Module Functions
          • Index of functions
          • eval
          • eval.metrics
    • Notifications
    • Release Notes
    • Instructions
    • Hello World (Sentiment Classifier)
    • Trading Strategy
    • LLM Text Summarization
    • Sandbox
    • Self-hosted
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For the complete documentation index, see llms.txt. This page is also available as Markdown.
  1. v0.21.x
  2. Using Distributional
  3. Python SDK
  4. Eval Module
  5. Eval Module Functions

Index of functions

  • eval

    • evaluate()

    • get_column_schemas_from_dataframe_and_metrics()

    • get_column_schemas_from_metrics()

  • eval.metrics

    • Metric

    • RougeScoreType

    • answer_quality_llm_accuracy()

    • answer_quality_llm_answer_correctness()

    • answer_quality_llm_answer_similarity()

    • answer_quality_llm_coherence()

    • answer_quality_llm_commital()

    • answer_quality_llm_completeness()

    • answer_quality_llm_contextual_relevance()

    • answer_quality_llm_faithfulness()

    • answer_quality_llm_grammar_accuracy()

    • answer_quality_llm_metrics()

    • answer_quality_llm_originality()

    • answer_quality_llm_relevance()

    • answer_viability_llm_metrics()

    • answer_viability_llm_reading_complexity()

    • answer_viability_llm_sentiment_assessment()

    • answer_viability_llm_text_fluency()

    • answer_viability_llm_text_toxicity()

    • automated_readability_index()

    • bleu()

    • character_count()

    • context_hit()

    • count_metrics()

    • flesch_kincaid_grade()

    • ground_truth_non_llm_answer_metrics()

    • ground_truth_non_llm_retrieval_metrics()

    • inner_product_retrieval()

    • inner_product_target_prediction()

    • levenshtein()

    • mrr()

    • non_llm_non_ground_truth_metrics()

    • quality_llm_text_similarity()

    • question_and_answer_metrics()

    • question_and_answer_metrics_extended()

    • rouge1()

    • rouge2()

    • rougeL()

    • rougeLsum()

    • rouge_metrics()

    • sentence_count()

    • summarization_metrics()

    • token_count()

    • text_metrics()

    • word_count()

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