What is BERTScore and how does it measure similarity?

BERTScore measures semantic similarity between LLM outputs and reference text by returning a similarity value from 0 to 1, where 0.9+ indicates nearly identical meaning, 0.7-0.9 indicates similar meaning, and below 0.7 indicates different meaning.

When should I use BERTScore instead of exact-match assertions?

Use BERTScore when exact-match or keyword-based assertions are too strict for evaluating free-form text output and you need semantic similarity measures, especially when there are multiple acceptable phrasings of a correct answer.

What is the setup requirement for using BERTScore?

Setup requires running `pip install -r requirements.txt`, and the first run downloads a roughly 1.4GB BERT model, so be aware of this initial download size.

Can BERTScore compare an output against multiple reference answers?

Yes, the advanced configuration compares an output against multiple valid reference answers, computing an F1 score against each and returning the maximum (best match) score.

Prompt Chain

Evaluate LLM Output Quality with BERTScore Similarity

Name: Eval Bert Score
Availability: OnlineOnly
Author: Promptfoo

Promptfoo example scoring LLM output by semantic similarity to a reference answer using BERTScore, including multi-reference matching.

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Updated 10 days ago

Source checked Sep 10, 2026

Version 0.123.0

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Why it matters

Automatically measure semantic similarity between LLM-generated outputs and reference text using BERTScore, enabling objective quality assessment and regression testing of language model responses with configurable thresholds.

Outcomes

What it gets done

Compare LLM outputs against reference text using BERT embeddings

Score semantic similarity on a 0-1 scale with configurable pass thresholds

Test against multiple valid reference answers and select best match

Integrate BERTScore assertions into automated evaluation pipelines

Install

Add it to your toolbox

Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.

Run in your project directory:

curl -fsSL https://spark.entire.vc/get/pfoo-eval-bert-score | bash

After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.

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Steps

Steps in the chain

Setup BERTScore evaluation

Configure basic BERTScore test

Run basic evaluation

Configure advanced multi-reference test

Run advanced evaluation

Overview

Eval Bert Score

Uses BERTScore to measure semantic similarity between LLM outputs and one or more reference texts as a promptfoo python assertion. Use when evaluating LLM output quality by semantic similarity rather than exact string matching.

What it does

This promptfoo example uses BERTScore to measure semantic similarity between LLM outputs and reference text, via a Python assertion:

tests:
  - vars:
      text: 'Hello world'
      reference: 'Hi there'
    assert:
      - type: python
        value: file://bertscore_check.py
        threshold: 0.7 # Pass if similarity > 70%

An advanced variant compares an output against multiple valid references using the bert_score library directly, computing an F1 score against each candidate reference and returning the best (maximum) match. BERTScore returns a similarity score from 0 to 1: 0.9 or higher means nearly identical meaning, 0.7-0.9 means similar meaning, and below 0.7 means different meaning.

When to use - and when NOT to

Use this example when evaluating LLM output quality by semantic similarity to a reference answer, including cases with multiple acceptable reference answers, rather than exact string matching.

Inputs and outputs

Requires pip install -r requirements.txt; the first run downloads the BERT model (~1.4GB). Run the basic example via promptfoo eval, or the advanced multi-reference example via promptfoo eval -c promptfooconfig-advanced.yaml.

Integrations

Built on the bert_score Python library, invoked as a promptfoo python-type assertion with a configurable pass threshold.

Who it's for

Engineers evaluating LLM outputs for semantic correctness against one or more reference answers who need a similarity score more tolerant of paraphrasing than exact-match or simple string-similarity checks.

Source README

eval-bert-score (BERTScore Evaluation)

Use BERTScore to measure semantic similarity between LLM outputs and reference text.

npx promptfoo@latest init --example eval-bert-score
cd eval-bert-score

Setup

pip install -r requirements.txt

Note: First run will download the BERT model (~1.4GB).

Usage

Basic Example

### promptfooconfig.yaml
tests:
  - vars:
      text: 'Hello world'
      reference: 'Hi there'
    assert:
      - type: python
        value: file://bertscore_check.py
        threshold: 0.7 # Pass if similarity > 70%

Run: promptfoo eval

Advanced Example

Compare against multiple valid references:

### promptfooconfig-advanced.yaml
assert:
  - type: python
    value: |
      from bert_score import score
      references = [
          "First valid answer",
          "Second valid answer",
          "Third valid answer"
      ]
      scores = []
      for ref in references:
          _, _, F1 = score([output], [ref], lang='en', verbose=False)
          scores.append(F1.item())
      return max(scores)  # Use best match

Run: promptfoo eval -c promptfooconfig-advanced.yaml

How It Works

BERTScore returns a similarity score from 0 to 1:

0.9+ = Nearly identical meaning
0.7-0.9 = Similar meaning
<0.7 = Different meaning

Learn more

FAQ

Common questions

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