{"categories":[{"label":"Testing","url":"https://skillfed.io/packages/category/software-development-testing/3"}],"enrichment":{"capability":"seqeval evaluates the performance of sequence labeling tasks like named-entity recognition and part-of-speech tagging by computing accuracy, precision, recall, and F1 scores across multiple tagging schemes.","skillfed_tags":["nlp-evaluation","sequence-labeling","ner-metrics"],"use_cases":["Evaluate NER model predictions against ground-truth labels using standard metrics like precision and recall.","Generate a classification report showing per-entity performance for a part-of-speech tagger or chunker.","Compare predictions across different tagging schemes (IOB1, IOB2, IOBES) to understand how schema choice affects scoring.","Validate sequence labeling output in strict mode to catch malformed tag sequences that default mode would overlook.","Benchmark sequence labeling systems using conlleval-compatible metrics for reproducibility with published results."],"what_it_does":"seqeval is a Python evaluation framework for sequence labeling tasks\u2014the kind of work you do when training models for named-entity recognition, part-of-speech tagging, semantic role labeling, or other chunking problems. It computes standard metrics (accuracy, precision, recall, F1) and generates classification reports, and it supports multiple tagging schemes (IOB1, IOB2, IOE1, IOE2, IOBES, BILOU). The package has two evaluation modes: a default mode compatible with the conlleval Perl script, and a strict mode that enforces schema compliance more rigidly.\n\nThe package has no runtime dependencies, making installation straightforward, but it is dormant\u2014the last release was in October 2020 and there have been no commits since August 2024. If you need sequence labeling evaluation and the existing metrics and schemes cover your use case, seqeval is a lightweight, stable choice; if you need active maintenance, bug fixes, or support for newer tagging schemes, you should verify whether the package still works with your Python version and consider alternatives.","worth_installing":"Yes, if your sequence labeling evaluation needs match the supported schemes and metrics. The package is lightweight, has no dependencies, carries a permissive MIT license, and is well-tested against conlleval. However, it is dormant\u2014last release October 2020\u2014so verify compatibility with your Python version before adopting. Do not use if you need active maintenance or support for newer tagging schemes."},"id":"seqeval","links":{"html":"https://skillfed.io/packages/seqeval","md":"https://skillfed.io/packages/seqeval.md","pypi":"https://pypi.org/project/seqeval/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2020-10-24","license_spdx":null,"license_treatment":"permissive","name":"seqeval","python_support":"unspecified","summary":"Testing framework for sequence labeling"},"popularity":{"monthly_downloads":372667,"position":7158,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.2"}