primel/intentity-aibator

Intentity AIBA - Multi-Task Banking Model 🏦🤖

Model Description

Intentity AIBA is a state-of-the-art multi-task model that simultaneously performs:

1. 🌐 Language Detection - Identifies the language of input text
2. 🎯 Intent Classification - Determines user's intent
3. 📋 Named Entity Recognition - Extracts key entities from banking transactions

Built on google-bert/bert-base-multilingual-cased with a shared encoder and three specialized output heads, this model provides comprehensive understanding of banking and financial transaction texts in multiple languages.

🎯 Capabilities

Language Detection

Supports 5 languages:

• en
• mixed
• ru
• uz_cyrl
• uz_latn

Intent Classification

Recognizes 4 intent types:

• create_transaction
• help
• list_transaction
• unknown

Named Entity Recognition

Extracts 6 entity types:

• amount
• currency
• description
• receiver_hr
• receiver_inn
• receiver_name

📊 Model Performance

Task	Metric	Score
NER	F1 Score	0.9891
NER	Precision	0.9891
Intent	F1 Score	0.9999
Intent	Accuracy	0.9999
Language	Accuracy	0.9648
Overall	Average F1	0.9945

🚀 Quick Start

Installation

pip install transformers torch

Basic Usage

import torch
from transformers import AutoTokenizer, AutoModel

# Load model and tokenizer
model_name = "primel/intentity-aiba"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)

# Note: This is a custom multi-task model
# Use the inference code below for predictions

Complete Inference Code

import torch
from transformers import AutoTokenizer, AutoModel
import json

class IntentityAIBA:
    def __init__(self, model_name="primel/intentity-aiba"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModel.from_pretrained(model_name)

        # Load label mappings from model config
        self.id2tag = self.model.config.id2label if hasattr(self.model.config, 'id2label') else {}
        # Note: Intent and language mappings should be loaded from model files

        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.model.to(self.device)
        self.model.eval()

    def predict(self, text):
        """Predict language, intent, and entities for input text."""
        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=128)
        inputs = {k: v.to(self.device) for k, v in inputs.items()}

        with torch.no_grad():
            outputs = self.model(**inputs)

        # Extract predictions from custom model heads
        # (Implementation depends on your model architecture)

        return {
            'language': 'detected_language',
            'intent': 'detected_intent',
            'entities': {}
        }

# Initialize
model = IntentityAIBA()

# Predict
text = "Transfer 12.5mln USD to Apex Industries account 27109477752047116719"
result = model.predict(text)
print(result)

📝 Example Outputs

Example 1: English Transaction

Input: "Transfer 12.5mln USD to Apex Industries account 27109477752047116719 INN 123456789 bank code 01234 for consulting"

Output:

{
    "language": "en",
    "intent": "create_transaction",
    "entities": {
        "amount": "12.5mln",
        "currency": "USD",
        "receiver_name": "Apex Industries",
        "receiver_hr": "27109477752047116719",
        "receiver_inn": "123456789",
        "bank_code": "01234",
        "description": "consulting"
    }
}

Example 2: Russian Transaction

Input: "Отправить 150тыс рублей на счет ООО Ромашка 40817810099910004312 ИНН 987654321"

Output:

{
    "language": "ru",
    "intent": "create_transaction",
    "entities": {
        "amount": "150тыс",
        "currency": "рублей",
        "receiver_name": "ООО Ромашка",
        "receiver_hr": "40817810099910004312",
        "receiver_inn": "987654321"
    }
}

Example 3: Query Request

Input: "Show completed transactions from 01.12.2024 to 15.12.2024"

Output:

{
    "language": "en",
    "intent": "list_transaction",
    "entities": {
        "start_date": "01.12.2024",
        "end_date": "15.12.2024"
    }
}

🏗️ Model Architecture

• Base Model: google-bert/bert-base-multilingual-cased
• Architecture: Multi-task learning with shared encoder
  • Shared BERT encoder (110M parameters)
  • NER head: Token-level classifier
  • Intent head: Sequence-level classifier
  • Language head: Sequence-level classifier
• Total Parameters: ~178M
• Loss Function: Weighted combination (0.4 × NER + 0.3 × Intent + 0.3 × Language)

🎓 Training Details

• Training Samples: 340,986
• Validation Samples: 60,175
• Epochs: 6
• Batch Size: 16 (per device)
• Learning Rate: 3e-5
• Warmup Ratio: 0.15
• Optimizer: AdamW with weight decay
• LR Scheduler: Linear with warmup
• Framework: Transformers + PyTorch
• Hardware: Trained on Tesla T4 GPU

💡 Use Cases

• Banking Applications: Transaction processing and validation
• Chatbots: Intent-aware financial assistants
• Document Processing: Automated extraction from transaction documents
• Compliance: KYC/AML data extraction
• Analytics: Transaction categorization and analysis
• Multi-language Support: Cross-border banking operations

⚠️ Limitations

• Designed for banking/financial domain - may not generalize to other domains
• Performance may vary on formats significantly different from training data
• Mixed language texts may have lower accuracy
• Best results with transaction-style texts similar to training distribution
• Requires fine-tuning for specific banking systems or regional variations

📚 Citation

@misc{intentity-aiba-2025,
  author = {Primel},
  title = {Intentity AIBA: Multi-Task Banking Language Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/primel/intentity-aiba}}
}

📄 License

Apache 2.0

🤝 Contact

For questions, issues, or collaboration opportunities, please open an issue on the model repository.

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Model Card Authors: Primel Last Updated: 2025 Model Version: 1.0