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piyushptiwari/InsureNER
InsureNER is a token classification model from piyushptiwari. Use it when you need labels on individual words, such as names. It is set up for transformers. The card lists the license as apache-2.0.
Created by Bytical AI — AI agents that run insurance operations.
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From the Hugging Face model README
Created by Bytical AI — AI agents that run insurance operations.
InsureNER is a domain-specific Named Entity Recognition model for the UK insurance industry. Built on ModernBERT-base, it recognizes 13 insurance-specific entity types using BIO tagging (26 tags + O = 27 total labels).
INSUREOS is published as separate repos so each page stays focused:
InsureNER) contains model artifacts and usage.| Entity | Description | Example |
|---|---|---|
CLAIM_NUMBER | Insurance claim reference | CLM-2024-001234 |
DATE | Dates in insurance context | 15 March 2026 |
INSURER | Insurance company name | Aviva, AXA, Zurich |
LOB | Line of Business | Motor, Property, Liability |
MGA | Managing General Agent | Covéa, eSure |
MONEY | Monetary amounts | £45,000, $1.2M |
ORG | Organisation name | FCA, Lloyd's of London |
PERIL | Insurance peril/risk | Flood, Fire, Theft |
PERSON | Person name | John Smith |
POLICY_NUMBER | Policy reference | POL-UK-2024-56789 |
POSTCODE | UK postcode | SW1A 1AA, EC2M 7PP |
REGULATION | Regulatory reference | Consumer Duty, Solvency II |
SYNDICATE | Lloyd's syndicate | Syndicate 2623 |
VEHICLE | Vehicle description | 2023 BMW 320d |
| Parameter | Value |
|---|---|
| Base Model | answerdotai/ModernBERT-base |
| Training Samples | 8,000 synthetic NER-annotated insurance texts |
| Epochs | 8 |
| Label Schema | BIO (27 labels) |
| GPU | NVIDIA Tesla T4 16GB |
| Metric | Score |
|---|---|
| F1 | 1.0 |
| Precision | 1.0 |
| Recall | 1.0 |
| Eval Loss | 4.80e-05 |
| Eval Samples/sec | 68.72 |
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline
model = AutoModelForTokenClassification.from_pretrained("piyushptiwari/InsureNER")
tokenizer = AutoTokenizer.from_pretrained("piyushptiwari/InsureNER")
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer, aggregation_strategy="simple")
text = "Aviva policy POL-UK-2024-56789 covers John Smith at SW1A 1AA for motor insurance. Claim CLM-2024-001234 was filed on 15 March 2026 for £45,000."
entities = ner_pipeline(text)
for ent in entities:
print(f" {ent['entity_group']:20s} {ent['word']:30s} (score: {ent['score']:.3f})")
New to ML or insurance AI? We wrote a plain-English guide that explains the concepts, the data, and the actual training code behind every INSUREOS model:
training/ner_model.pyThis model is part of the INSUREOS — a complete AI/ML suite for insurance operations built by Bytical AI:
| Model | Task | Metric |
|---|---|---|
| InsureLLM-4B | Insurance domain LLM | ROUGE-1: 0.384 |
| InsureDocClassifier | 12-class document classification | F1: 1.0 |
| InsureNER (this model) | 13-entity Named Entity Recognition | F1: 1.0 |
| InsureFraudNet | Fraud detection (Motor/Property/Liability) | AUC-ROC: 1.0 |
| InsurePricing | Insurance pricing (GLM + EBM) | MAE: £11,132 |
@misc{bytical2026insurener,
title={InsureNER: Insurance Named Entity Recognition with ModernBERT},
author={Bytical AI},
year={2026},
url={https://huggingface.co/piyushptiwari/InsureNER}
}
Bytical builds AI agents that run insurance operations — claims automation, underwriting intelligence, digital sales, and core system modernization for insurers across the UK and Europe. Microsoft AI Partner | NVIDIA | Salesforce.