from amwal import load_ner

ner = load_ner()

text = "يطرح البنك المركزي المصري، بعد غد، سندات خزانة ثابتة ومتغيرة العائد بقيمة 45 مليار جنيه"
result = ner(text)

print(result["entities"])

Model Summary

AMWAL is a spaCy-based Named Entity Recognition (NER) system designed for extracting financial entities from Arabic text, with a primary focus on Arabic financial news and reports.

The model addresses challenges specific to Arabic financial NLP, including orthographic variation, domain-specific terminology, and the scarcity of annotated financial resources for Arabic.

Intended Use

AMWAL is intended for:

Arabic financial news analysis
Information extraction from financial reports
Financial text preprocessing
Academic research in Arabic NLP and finance
Data enrichment for financial knowledge graphs

It is not intended for:

General-purpose Arabic NER
Non-financial domains
Direct use with Hugging Face Transformers APIs

Data Collection and Annotation

A specialized Arabic financial corpus was constructed from three major Arabic financial newspapers, covering the period 2000–2023.

The annotation process followed a semi-automatic workflow:

Automatic candidate entity extraction
Manual annotation
Expert review and correction

The final dataset contains:

17.1K annotated entity tokens
21 financial entity categories
Consistent domain coverage across multiple time periods

Entity Schema and Standardization

Entity categories were standardized using concepts from the Financial Industry Business Ontology (FIBO, 2020) to ensure conceptual consistency and compatibility with structured financial representations.

Model Architecture and Training

Framework: spaCy
Pipeline: Custom Named Entity Recognition (NER)
Domain: Arabic financial text

The model was trained on the annotated corpus using spaCy’s NER pipeline. To mitigate sparsity caused by Arabic orthographic variation, normalization was applied consistently during training and inference.

Arabic Normalization

The following normalization steps are applied internally during inference, matching the training setup:

Removal of all diacritics
Character normalization:
- إ, أ, آ → ا
- ؤ, ئ → ء
- ة → ه
- ى → ي

The original input text is always preserved and returned as raw_text.

Entity Types

The model recognizes 21 financial entity types, including (but not limited to):

COUNTRY
CITY
CURRENCY
FINANCIAL_INSTRUMENT
BANK
ORGANIZATION
NATIONALITY
EVENT
TIME
QUANTITY_OR_UNIT

Evaluation Results

The model was evaluated on a held-out test set using standard NER metrics:

Metric	Score
Precision	96.08%
Recall	95.87%
F1-score	95.97%

These results are competitive with reported financial NER systems in other languages, despite the additional challenges posed by Arabic morphology and orthography.

Usage

AMWAL supports two officially supported usage modes.

Option 1 — Install via `pip` (recommended)

pip install git+https://huggingface.co/Muhsabrys/AMWAL_ArFinNER

python

from amwal import load_ner

ner = load_ner()
result = ner("يطرح البنك المركزي المصري، بعد غد، سندات خزانة ثابتة ومتغيرة العائد بقيمة 45 مليار جنيه")
print(result["entities"])
[{'text': 'البنك المركزي المصري', 'label': 'BANK', 'start': 5, 'end': 25}, {'text': 'سندات', 'label': 'FINANCIAL_INSTRUMENT', 'start': 35, 'end': 40}, {'text': '45 مليار', 'label': 'QUNATITY_OR_UNIT', 'start': 74, 'end': 82}, {'text': 'جنيه', 'label': 'CURRENCY', 'start': 83, 'end': 87}]

Option 2 — Use directly from Hugging Face (no installation)

python

from huggingface_hub import snapshot_download
import sys

repo_path = snapshot_download("Muhsabrys/AMWAL_ArFinNER")
sys.path.append(repo_path)

from amwal import load_ner

ner = load_ner(local_path=repo_path)
result = ner("الصادرات البترولية المصرية ترتفع إلى 3.6 مليار دولار خلال 9 أشهر")
print(result["entities"])

Output Format

json

{
  "entities_in_order": [
    {
      "text": "الصادرات",
      "label": "Events",
      "start": 1,
      "end": 9
    },
    {
      "text": "البتروليه",
      "label": "PRODUCT_OR_SERVICE",
      "start": 10,
      "end": 19
    },
    {
      "text": "المصريه",
      "label": "NATIONALITY",
      "start": 20,
      "end": 27
    },
    {
      "text": "ترتفع",
      "label": "Events",
      "start": 28,
      "end": 33
    },
    {
      "text": "مليار",
      "label": "QUNATITY_OR_UNIT",
      "start": 42,
      "end": 47
    },
    {
      "text": "دولار",
      "label": "CURRENCY",
      "start": 48,
      "end": 53
    }
  ]
}

Limitations

Domain-specific to financial text
Not suitable for general-purpose Arabic NER
Does not model relations between entities
Not compatible with Hugging Face Transformers APIs

Future Work

Planned future directions include:

Expanding the annotated corpus
Introducing hierarchical entity structures
Modeling relations between financial entities
Constructing an Arabic financial knowledge graph

Citation

bibtex

@inproceedings{abdo2025amwal,
  title={AMWAL: Named Entity Recognition for Arabic Financial News},
  author={Abdo, Muhammad S and Hatekar, Yash and {\'C}avar, Damir},
  booktitle={Proceedings of the Joint Workshop of the 9th Financial Technology and Natural Language Processing (FinNLP), the 6th Financial Narrative Processing (FNP), and the 1st Workshop on Large Language Models for Finance and Legal (LLMFinLegal)},
  pages={207--213},
  year={2025}
}

Muhsabrys/AMWAL_ArFinNER

📄 Associated Paper

AMWAL: Arabic Financial Named Entity Recognition (NER)

Quick Start

Install (recommended)