2 dépôts
Awesome GitHub RepositoriesModel Quality Metrics
Calculations for accuracy, perplexity, and F1 scores to quantify the performance of language models.
Distinct from Accuracy Calculators: Distinct from Accuracy Calculators: encompasses a broader set of quality metrics including perplexity and F1 scores.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Model Quality Metrics. Refine with filters or upvote what's useful.
Awesome Model Quality Metrics GitHub Repositories
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- evolvinglmms-lab/lmms-eval
EvolvingLMMs-Lab/lmms-eval
3,701Voir sur GitHublmms-eval is a benchmarking system and performance analysis suite designed to measure the capabilities of large multimodal models. It provides a framework for evaluating models across text, image, audio, and video datasets, serving as a multimodal dataset orchestrator and benchmarking tool to quantify accuracy and efficiency. The project distinguishes itself through a unified multimodal message protocol that structures diverse media inputs for consistent model consumption. It features specialized benchmarking for audio, video, visual, document, and spatial reasoning, alongside tools for model
Calculates accuracy, perplexity, and F1 scores using configurable aggregation methods to quantify model quality.
Pythonagiaudio-evaluationbenchmark - huggingface/transfer-learning-conv-ai
huggingface/transfer-learning-conv-ai
1,757Voir sur GitHubThis framework is a research-oriented toolkit designed for training, fine-tuning, and evaluating conversational agents using transformer-based language architectures. It provides an integrated environment for adapting large pre-trained models to specific dialogue datasets, enabling the development of systems capable of generating coherent, human-like responses. The project distinguishes itself through its support for multi-GPU distributed training, which accelerates the optimization of large-scale models. It also features configurable probabilistic decoding strategies, such as nucleus and gre
Calculates standard metrics like perplexity and F1 scores to quantify the quality of conversational models.
Pythonchatbotsdeep-learningdialog
