techkiyan/indian-financial-news-ner-gliner-v1

Indian Financial News NER GLiNER v1

indian-financial-news-ner-gliner-v1 is a GLiNER-based named entity recognition model fine-tuned for Indian financial news, market updates, company news, and stock-market-related text.

The model is designed as a lightweight candidate extraction layer for financial intelligence pipelines. It can be used to extract companies, indices, organizations, amounts, percentages, dates, sectors, financial terms, and event-related phrases from short financial-news text.

This is a v1 release. It should be treated as a domain-specialized candidate extractor, not as a final truth engine.

Model Details

• —Base model: EmergentMethods/gliner_medium_news-v2.1
• —Model family: GLiNER
• —Task: Named Entity Recognition
• —Domain: Indian financial news and market news
• —Training type: supervised fine-tuning
• —Hardware used: NVIDIA RTX 2060 12GB
• —Training examples: 73,864
• —Validation examples: 9,055
• —Training steps: 18,500
• —Training runtime: about 44 minutes

Labels

The model supports 10 labels:

• —company
• —index
• —organization
• —person
• —amount
• —percentage
• —date
• —financial_term
• —event_signal
• —sector

Label Definitions

company

Company mentions, including listed companies, IPO companies, and other business entities in financial-news context.

Examples:

• —Reliance Industries
• —HDFC Bank
• —Infosys
• —NSDL

index

Market indices and benchmarks.

Examples:

• —Nifty 50
• —Sensex
• —Bank Nifty
• —Nifty IT

organization

Regulators, exchanges, rating agencies, government bodies, courts, brokerages, and other financial organizations.

Examples:

• —SEBI
• —RBI
• —NSE
• —BSE
• —CRISIL
• —ICRA

person

Named individuals such as executives, promoters, ministers, regulators, analysts, and officials.

Examples:

• —Shaktikanta Das
• —Nirmala Sitharaman
• —Mukesh Ambani

amount

Monetary values.

Examples:

• —₹1,800 crore
• —Rs 500 crore
• —$35 million

percentage

Percentages, basis points, and rate movement expressions.

Examples:

• —5%
• —25 bps
• —4.77 per cent

date

Dates, quarters, financial years, and time-period references.

Examples:

• —Q1FY26
• —FY25
• —June 2026

financial_term

Financial metrics, instruments, corporate-action terms, and market-finance concepts.

Examples:

• —revenue
• —EBITDA
• —NPA
• —order book
• —dividend
• —bond
• —buyback
• —QIP

event_signal

Market or business event trigger phrases.

Examples:

• —wins order
• —receives approval
• —raises funds
• —reports profit growth
• —board approves buyback
• —resigns

sector

Industry or market sectors.

Examples:

• —banking
• —IT
• —pharma
• —NBFC
• —auto
• —energy

Usage

from gliner import GLiNER

model = GLiNER.from_pretrained("techkiyan/indian-financial-news-ner-gliner-v1")

labels = [
    "company",
    "index",
    "organization",
    "person",
    "amount",
    "percentage",
    "date",
    "financial_term",
    "event_signal",
    "sector",
]

text = "HDFC Bank shares rose 3% after the company reported strong Q1 profit growth."

entities = model.predict_entities(text, labels, threshold=0.5)

for entity in entities:
    print(entity)

Recommended Use

This model is best used as the first layer in a financial information extraction pipeline:

1. 1.Extract entity candidates.
2. 2.Resolve company names to NSE/BSE symbols.
3. 3.Normalize amounts, percentages, and dates using deterministic rules.
4. 4.Link organizations and sectors to internal dictionaries.
5. 5.Use a separate event classifier for event type and market-intelligence workflows.

Recommended Labels for Production Pipelines

For stricter production use, start with:

• —company
• —index
• —organization
• —amount
• —percentage
• —financial_term
• —sector

The following labels should be treated as experimental or review-only in v1:

• —event_signal
• —person
• —date

Evaluation

Internal corrected merged-label test benchmark:

Important: this is an internal benchmark on corrected merged-label test data. It should not be interpreted as a public SOTA claim.

Dataset

The model was trained on a corrected silver + seed-gold Indian financial-news corpus.

Summary:

• —Total exported rows: 92,485
• —Annotation-corrected rows: 43,584
• —Reviewed rows: 100
• —Seed-gold rows: 95
• —Training rows: 73,864
• —Validation rows: 9,055

The training data is not included in this model release. Some source text may come from third-party financial-news sources, so users should verify licensing before redistributing datasets.

Limitations

• —This is a v1 model.
• —It is intended for candidate extraction, not final financial reasoning.
• —Entity linking to NSE/BSE symbols is not included.
• —event_signal is experimental and may be better handled by a separate event classification model.
• —The model does not provide investment advice, trading signals, buy/sell calls, or price predictions.

Disclaimer

This model is for financial text understanding and market-intelligence research. It does not provide investment advice, buy/sell recommendations, target prices, or trading decisions.