{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"}],"enrichment":{"capability":"Evaluates machine translation quality using neural metrics, scoring translations against references or without references, and explaining error types according to MQM typology.","skillfed_tags":["machine-translation","neural-metrics","evaluation"],"use_cases":["Evaluate machine translation system outputs during development and benchmarking against reference translations","Score translations without references using quality-estimation models for deployment scenarios where gold references are unavailable","Identify and classify specific translation errors (minor, major, critical) using XCOMET models for error analysis and system improvement","Compare multiple MT systems with statistical significance testing to determine which performs best","Rank and select best translations from candidate pools using Minimum Bayes Risk decoding for quality-aware decoding","Evaluate document-level translation quality using context-aware DocCOMET models for improved accuracy on discourse phenomena"],"what_it_does":"unbabel-comet is a neural machine translation evaluation framework that scores translation quality using deep learning models trained on human assessments. It offers three evaluation modes: reference-based scoring (comparing translations to gold references), reference-free scoring (assessing translations without references), and explainable scoring (XCOMET models that identify error spans and classify them by severity according to MQM typology). The package includes CLI tools for batch scoring, system comparison with statistical significance testing, and Minimum Bayes Risk decoding for selecting best translations from candidate lists.\n\nThe framework depends on PyTorch, Hugging Face Transformers, and PyTorch Lightning, making it suitable for research and production environments where neural metric evaluation is needed. It supports document-level context through DocCOMET, enabling better handling of discourse phenomena and chat translation. Multiple pre-trained models are available via Hugging Face Hub, ranging from lightweight reference-free variants to large explainable models with billions of parameters.","worth_installing":"Yes. Active maintenance, permissive Apache-2.0 license, low install friction, no known vulnerabilities, and substantial community adoption (774 stars, top 15000 tier). The package is production-ready for MT evaluation workflows. Install if you need neural metric evaluation; skip if you require only lightweight statistical metrics like BLEU."},"id":"unbabel-comet","links":{"html":"https://skillfed.io/packages/unbabel-comet","md":"https://skillfed.io/packages/unbabel-comet.md","pypi":"https://pypi.org/project/unbabel-comet/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2025-09-01","license_spdx":null,"license_treatment":"permissive","name":"unbabel-comet","python_support":"supports_current","summary":"High-quality Machine Translation Evaluation"},"popularity":{"monthly_downloads":96453,"position":13210,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.7"}