#Evaluation Metrics

Tonic Validate provides a versatile framework for evaluating LLM outputs, including RAG pipelines. It offers metrics for measuring correctness and hallucination, and supports data preparation with Tonic Textual. Features include a UI for visualization, OpenAI integration, and adaptable metrics for comprehensive evaluation.

The repository acts as a detailed archive of methods for evaluating visual generation models and their outputs, including images and videos. It highlights crucial areas such as model performance, generated content analysis, and alignment with user inputs. It offers resources, metrics, and methodologies concerning latent representations, condition consistency, and overall quality assessments. Community contributions via issues or pull requests are welcomed to maintain its relevance. This serves as a guide for enhancing visual generation with insights and evaluation techniques.

This repository offers code to evaluate unsupervised metrics in Natural Language Generation. It processes hypothesis and reference inputs to compute various metrics like BLEU, ROUGE, and CIDEr. Java and Python dependencies are needed for installation, and it can be used via command line or Python API. It supports assessments of both individual and multiple examples, providing flexibility for research applications. Users can customize settings for metrics like CIDEr to enhance precision.

Simplify topic modeling with OCTIS by leveraging Bayesian Optimization for hyperparameter tuning. Users can preprocess datasets, utilize diverse models, and evaluate results effectively, supported by extensive tutorials and a user-friendly Python library. Ideal for both beginners and experts seeking efficient topic modeling.

CharacterEval provides an extensive dataset featuring 1,785 dialogues and 23,020 examples to evaluate 77 characters from Chinese literature. Utilizing a multidimensional assessment system with thirteen metrics, it integrates a character-based reward model demonstrating superior human correlation compared to other models, including GPT-4. The project includes detailed annotation documents and supports consensus scoring. Additionally, it facilitates the replication of evaluation scores using multiple open-source models.