{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"BERTScore computes automatic evaluation metrics for text generation by comparing candidate and reference sentences using contextual embeddings from BERT, producing precision, recall, and F1 scores.","skillfed_tags":["nlp-evaluation","text-generation","embedding-based-metrics"],"use_cases":["Evaluate machine translation output by scoring candidate translations against reference translations.","Assess abstractive summarization quality by comparing generated summaries to reference summaries.","Score image captioning or visual question-answering outputs against reference captions or answers.","Benchmark text generation models during development without manual human evaluation.","Compare multiple candidate outputs for the same input and select the highest-scoring one."],"what_it_does":"BERTScore is an automatic evaluation metric for text generation tasks that uses contextual embeddings from pre-trained BERT models to compare candidate and reference sentences. Rather than relying on surface-level token overlap, it matches words by cosine similarity in embedding space, producing precision, recall, and F1 scores that correlate better with human judgment than traditional metrics.\n\nThe package wraps torch, transformers, and related dependencies to provide both a simple `score()` function and a `BERTScorer` object that caches models across multiple evaluations. It supports about 130 pre-trained models including BERT variants, RoBERTa, DeBERTa, T5, BART, and multilingual options. The default model is roberta-large, though the documentation recommends microsoft/deberta-xlarge-mnli for better correlation with human scores.","worth_installing":"Yes, with conditions. BERTScore is well-established with 1913 GitHub stars and solves a real problem in NLP evaluation. However, the package is dormant (last release 2023-02-20) and computationally heavy\u2014it requires PyTorch and GPU access for practical use, and first-run model downloads are slow. Install it if you need embedding-based text evaluation and can afford the compute cost; avoid it if you need active maintenance or lightweight CPU-only evaluation."},"id":"bert-score","links":{"html":"https://skillfed.io/packages/bert-score","md":"https://skillfed.io/packages/bert-score.md","pypi":"https://pypi.org/project/bert-score/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-02-20","license_spdx":null,"license_treatment":"permissive","name":"bert-score","python_support":"supports_current","summary":"PyTorch implementation of BERT score"},"popularity":{"monthly_downloads":673466,"position":5396,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.3.13"}