What is Named Entity Recognition (NER)?

Named Entity Recognition identifies and classifies proper nouns in text such as people, organizations, locations, and dates.

What entities can this tool extract?

People, organizations, locations, dates, money amounts, and some miscellaneous patterns like emails.

Is this NER tool free?

Yes. It is free with no signup or limits.

Does it use an AI model?

It uses fast rule-based patterns in the browser, so results are best for English text and may need review.

Are my texts sent to a server?

No. Entity extraction runs locally in your browser.

How can I improve accuracy?

Use well-formed sentences with capitalization and include context around names and places.

✓ BERT model✓ Confidence scores✓ Runs on-device

Named Entity Recognizer

Identify people, organizations, locations, dates, money, and emails using on-device BERT.

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How to use Named Entity Recognizer

Three steps. Instant results.

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Step 1

Paste English text.

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Step 2

Load the BERT NER model and analyze.

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Step 3

Review highlighted entities, categories, and confidence scores.

How Named Entity Recognizer works

This tool runs the bert-base-NER token-classification model through Transformers.js. BERT uses surrounding context to classify person, organization, location, and miscellaneous entities. Date, money, and email patterns supplement model output. Inference happens in your browser.

Why use this Named Entity Recognizer?

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Context-aware BERT classification

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Confidence score for every model entity

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Deterministic dates, money, and email extraction

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On-device processing after model download

Frequently asked questions

What model does this NER tool use?

It runs Xenova/bert-base-NER in your browser through Transformers.js. The model identifies people, organizations, locations, and miscellaneous entities from context.

What else can it extract?

Deterministic patterns supplement the model for dates, money amounts, and email addresses.

How accurate is it?

BERT is substantially stronger than capitalization rules, but NER remains probabilistic. Review low-confidence entities and ambiguous names.

Is my text uploaded?

No. Model inference runs on your device after model files download.

Why is first use slower?

The browser downloads and caches the model on first use. Later runs reuse the cache.

Which language works best?

This model is trained for English. Other languages need a multilingual NER model.

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