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Saurabh18888/
gliner-news-geo

Name: gliner-news-geo
Availability: InStock

fastino/gliner2-base-v1

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GLiNER2 NER for Knowledge Graph Construction

Hugging Face Model

Overview

This project fine-tunes GLiNER2 to build the Named Entity Recognition (NER) layer of a Knowledge Graph construction pipeline.

The goal is not to create a generic NER model. The goal is to reliably identify the entities that will become nodes in a Knowledge Graph, which can later be connected through relation extraction and stored in a graph database.

The final NER model is designed for five core geopolitical entity types:

PERSON
ORG
GPE
EVENT
DATE

These entities are particularly useful for extracting structured information from news, reports, articles, and other unstructured text before constructing the Knowledge Graph.

You can access the fine-tuned model here: Saurabh18888/gliner-news-geo

Knowledge Graph Pipeline

The NER model represents the first major information-extraction stage of the Knowledge Graph pipeline.

text

Raw Documents
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Text Extraction / Preprocessing
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GLiNER2 NER
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     ├── PERSON
     ├── ORG
     ├── GPE
     ├── EVENT
     └── DATE
     │
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Entity Normalization / Deduplication
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Relation Extraction
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Knowledge Graph
     │
     ├── Entity Nodes
     └── Relationship Edges

The purpose of this fine-tuning stage is to produce high-quality entity candidates for downstream Knowledge Graph construction.

Entity Ontology

Entity	Description	Example
`PERSON`	Names of individual people including politicians, business leaders, and officials.	Donald Trump
`ORG`	Organizations, companies, governments, political parties, NGOs, and institutions.	NATO
`GPE`	Geopolitical entities such as countries, states, provinces, cities, and territories.	Iran, Washington
`EVENT`	Named or identifiable real-world events including geopolitical, military, and historical events.	2024 Paris Olympics
`DATE`	Temporal expressions including dates, months, years, date ranges, and relative dates.	August 14, 2026

Training Data Strategy

Because no single dataset provides high-quality coverage for all five target labels, this pipeline merges three distinct datasets:

1. TNER OntoNotes 5

The foundational dataset. It provides excellent, high-volume coverage for PERSON, ORG, GPE, and DATE. It contains very few EVENTs.

2. Few-NERD

Used strictly to bolster the EVENT class. Few-NERD provides a massive pool of events, which we filter and randomly downsample to balance against the OntoNotes labels.

3. Custom JSONL (`combined_output.jsonl`)

A highly-trusted, domain-specific local dataset focused entirely on geopolitical EVENTs. We drop corrupted labels from this dataset (like broken PERSON/ORG extractions) and keep only the gold-standard EVENTs to inject strong domain knowledge into the model.

Overcoming the Annotation Coverage Problem

A major problem when merging datasets is differing annotation coverage.

For example, our Custom JSONL dataset drops PERSON and ORG labels because they were corrupted. If we feed a sentence containing a person's name to the model but don't label it as PERSON, the model learns a False Negative (i.e., it learns that the name is not a person).

Instead of using complex multi-stage pseudo-labeling, we solve this gracefully using GLiNER's dynamic prompting format.

During dataset construction, we restrict the prompted valid_labels based on the source dataset:

For OntoNotes, we prompt the model to learn all 5 labels.
For Custom JSONL, we only prompt the model for EVENT.

By not asking the model to predict PERSON or ORG on the Custom dataset, we completely bypass the False Negative penalty.

Dataset Balancing & Unified Training

Because Few-NERD contains over 100,000 events, simply concatenating the datasets would cause the EVENT class to dominate the loss function, destroying the model's ability to recognize dates or organizations.

The pipeline implements a controlled budgeting strategy:

It calculates the fixed number of entities provided by OntoNotes and Custom datasets.
It sets a dynamic TARGET_BUDGET (e.g., 18,000 entities per class).
It samples just enough Few-NERD records to reach the budget.

Final GLiNER2 Model

The final model uses LoRA parameter-efficient fine-tuning on top of the base GLiNER architecture:

text

Base Model: fastino/gliner2-base-v1
Epochs: 8
Batch size: 18
Gradient accumulation: 2
Encoder learning rate: 1e-5
Task learning rate: 5e-4
LoRA Rank (r): 8 (Configurable to 16/32)
LoRA Alpha: 16.0 (Configurable to 32.0/64.0)

Early stopping (patience=3) and validation are evaluated at every epoch.

Why These Entities Matter for the Knowledge Graph

The extracted entities will eventually become Knowledge Graph nodes.

For example, from:

President Donald Trump met NATO officials in Washington on August 14, 2026 during the Iran conflict.

The NER system extracts:

text

PERSON    → Donald Trump
ORG       → NATO
GPE       → Washington
DATE      → August 14, 2026
EVENT     → Iran conflict

These entities can then be passed to the downstream relation-extraction system to form the basis of the Knowledge Graph:

text

[Donald Trump] ──(met)──▶ [NATO] ──(located in)──▶ [Washington]
                               │
                       (occurred during)
                               │
                               ▼
                       [Iran conflict]

Training Workflow

The complete notebook workflow is:

text

1. Load OntoNotes 5, Few-NERD, and Custom JSONL
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2. Normalize all formats to Character-Level Spans
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3. Validate and drop overlapping/whitespace spans
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4. Global text deduplication (preventing data leakage)
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5. Class Balancing (Downsample Few-NERD to Target Budget)
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6. GroupShuffleSplit (Train / Val / Test)
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7. Convert to GLiNER2 InputExample format
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8. Fine-tune unified GLiNER2 with LoRA
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9. Strict Span + Label Evaluation & Error Analysis
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10. Save Model & Adapters

Evaluation

The notebook includes an exact-match evaluation based on strict span boundaries:

(entity start, entity end, entity label)

It outputs Micro/Macro statistics including Precision, Recall, and F1.

A dedicated Error Analysis module is included to randomly sample and review:

Missed Entities (False Negatives)
Spurious Entities (False Positives)
Wrong Labels

Running the Notebook

The notebook is designed to run in Google Colab.

Install dependencies:

pip install -q "gliner2[local]" datasets scikit-learn matplotlib seaborn pandas tqdm huggingface_hub

Upload the domain-specific data:

combined_output.jsonl

Run the notebook sequentially to preprocess the multi-source data, train the LoRA adapter, and save the merged /best_model locally or push it directly to the Hugging Face hub.

Saurabh18888/gliner-news-geo

GLiNER2 NER for Knowledge Graph Construction

Overview

Knowledge Graph Pipeline

Entity Ontology

Training Data Strategy

1. TNER OntoNotes 5

2. Few-NERD

3. Custom JSONL (combined_output.jsonl)

Overcoming the Annotation Coverage Problem

Dataset Balancing & Unified Training

Final GLiNER2 Model

Why These Entities Matter for the Knowledge Graph

Training Workflow

Evaluation

Running the Notebook

Saurabh18888/
gliner-news-geo

3. Custom JSONL (`combined_output.jsonl`)