# Tiiiger/bert_score

BERT score for text generation

Repository: https://github.com/Tiiiger/bert_score
Canonical: https://ross.abutalabs.com/products/bert_score
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: natural-language-processing, machine-learning
Last push: 2024-07-30T09:41:28+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2692, "days_push": 764, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1916, forks 240 (observed 2026-08-28T04:05:53.795282+00:00)

## What it is
BERTScore is a PyTorch implementation of the BERTScore automatic evaluation metric for text generation, which scores generated text against references using contextual embeddings from pretrained models like BERT and RoBERTa. It supports ~130 pretrained models and correlates well with human evaluation.

## Use cases
- evaluate machine translation output quality
- score text summarization against references
- compare generated text with human judgments
- compute semantic similarity between sentences
- evaluate LLM text generation quality
- benchmark NLP generation models

## When to choose
- you need an automatic evaluation metric that correlates better with human judgment than BLEU or ROUGE
- you are evaluating text generation tasks like summarization or translation
- you want a PyTorch-based metric with broad pretrained model support

## When to avoid
- you need a lightweight metric without GPU or large model downloads
- you need exact n-gram overlap scores like BLEU for reporting
- you need a metric actively adding new features rather than maintenance fixes

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, machine-learning, benchmarking
- domain: machine-learning
- platform: python
- tags: bert, evaluation-metric, text-generation, pytorch, transformers, natural-language-processing

## Member repositories
- Tiiiger/bert_score (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:53.795282+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:10:10.277978+00:00, confidence not recorded.
  - readme: https://github.com/Tiiiger/bert_score (fetched 2026-08-28T04:05:53.795282+00:00, sha 5eacd997c2b1)
  - registry_pypi: https://pypi.org/pypi/bert_score/json (fetched 2026-08-29T10:49:30.973129+00:00, sha ca81a53ef441)
- Data as of 2026-08-30T08:39:29.467469+00:00.