unbabel-comet
High-quality Machine Translation Evaluation
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.8 or above.
- Some models (e.g., Unbabel/wmt22-cometkiwi-da) require Hugging Face Hub authentication and license acknowledgment.
- Low friction installation via wheel distribution.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most deployment scenarios.
last release 2025-09-01 (347 days) · last repo commit 2026-04-21 · 774 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 96,453 downloads/mo, #13,210 on PyPI
Alternatives
Verify before relying
pip install unbabel-comet
comet-score -s src.txt -t hyp.txt -r ref.txt
# Or in Python:
from comet import load_from_checkpoint
model = load_from_checkpoint("Unbabel/wmt22-comet-da")
scores = model.predict(data, batch_size=8)- Whether GPU acceleration is required or CPU-only scoring is practical for typical workloads
- Specific accuracy/correlation metrics compared to other MT evaluation frameworks
- Whether document-level context (DocCOMET) improves results for your language pair and domain
What it is and 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.
The 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.
Use it for
- 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
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
unbabel-comet on PyPI
Before you install
Low friction installation via wheel distribution. Active maintenance with recent commits; 774 repository stars. Depends on 13 runtime packages including torch, transformers, and pytorch-lightning, which are substantial but standard for ML workflows.
Requires Python 3.8 or above. Some models (e.g., Unbabel/wmt22-cometkiwi-da) require Hugging Face Hub authentication and license acknowledgment.
License in practice
Apache-2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most deployment scenarios.
Quickstart
pip install unbabel-comet
comet-score -s src.txt -t hyp.txt -r ref.txt
# Or in Python:
from comet import load_from_checkpoint
model = load_from_checkpoint("Unbabel/wmt22-comet-da")
scores = model.predict(data, batch_size=8)Verify before relying
- Whether GPU acceleration is required or CPU-only scoring is practical for typical workloads
- Specific accuracy/correlation metrics compared to other MT evaluation frameworks
- Whether document-level context (DocCOMET) improves results for your language pair and domain
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release <4.0.0,>=3.8.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 13 packagesentmaxhuggingface-hubjsonargparsenumpypandasprotobufpytorch-lightningsacrebleuscipysentencepiecetorchtorchmetricstransformers |
| Maintenance | Actively maintained 347 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 96,453 / month, #13,210 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaEnvironment :: ConsoleIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/Engineering :: Artificial Intelligence |
Evidence: unbabel_comet-2.2.7-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “machine translation evaluation”
- unbabel-cometEvaluates machine translation quality using neural metrics, scoring…
- sacrebleuSacreBLEU computes BLEU, chrF, and TER scores for machine translation…
- bert-scoreBERTScore computes automatic evaluation metrics for text generation…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also dtlpymetrics · rouge · sacrebleu · rouge-chinese · texterrors · bert-score · comet-ml · rouge-metric · cleanlab-tlm · rouge-score