GlinerNer

Import

import { GlinerNer } from '@stabrise/scaledp/ner'

Group

Understand

Reads

document

Writes

ner

Needs

@huggingface/transformers

import { Pipeline } from '@stabrise/scaledp'
import { PdfToImage } from '@stabrise/scaledp/pdf'
import { PaddleTextRecognizer } from '@stabrise/scaledp/ocr'
import { GlinerNer } from '@stabrise/scaledp/ner'

const rows = await new Pipeline([
    new PdfToImage({ resolution: 300 }),
    new PaddleTextRecognizer({ keepFormatting: true }),
    new GlinerNer({
        inputCol: 'text',
        labels: ['person', 'organization', 'email', 'phone', 'address'],
        threshold: 0.5,
    }),
]).transform(file)

for (const entity of rows[0].ner.entities) {
    console.log(entity.entity_group, entity.word, entity.score, entity.boxes)
}

Labels are the prompt

GLiNER scores a label by its text, so the wording is part of the query. 'phone' and 'phone_number' are different questions, and a model fine-tuned on one will score the other lower. Ask for 'medical_condition' and it looks for one, with no retraining.

import { DEFAULT_PII_LABELS, GLINER2_PII_LABELS } from '@stabrise/scaledp/ner'

whiteList filters the results by group after the fact, which is different from narrowing labels — narrowing the labels changes the inference.

Entities carry boxes

Entity.start/end are character offsets into Document.text, and boxes are the page boxes those characters fall in. The mapping is built by locating each box's text in the document text.

That is what makes redaction work: the boxes are where the entity actually is on the page.

Chunking

Long documents are windowed at chunkLength 500 characters with a chunkStride of 480, so a 20-character overlap catches entities that straddle a seam. Duplicates from that overlap are de-duplicated here; Python's sliding window reports them twice.

normaliseCasing

On by default, and it matters for scans. GLiNER1 models are cased, and a document set entirely in capitals looks unlike anything in training. The rewrite title-cases all-caps runs before inference and is length-preserving, so character offsets stay valid and reported words keep their original casing.

Model registry

import { NER_MODELS, getNerModel, modelSizeBytes } from '@stabrise/scaledp/ner'

An id not in the registry is rejected by the constructor. Two of the four models are private StabRise repos and need configure({ auth }) — see Private models and auth.

Parameters