Upload Intentity AIBA - Multi-Task Banking Model (Language + Intent + NER)

README.md

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+---
+language:
+- en
+- ru
+- uz
+- multilingual
+license: apache-2.0
+tags:
+- multi-task-learning
+- token-classification
+- text-classification
+- ner
+- named-entity-recognition
+- intent-classification
+- language-detection
+- banking
+- transactions
+- financial
+- multilingual
+- bert
+- pytorch
+datasets:
+- custom
+metrics:
+- precision
+- recall
+- f1
+- accuracy
+- seqeval
+widget:
+- text: "Transfer 12.5mln USD to Apex Industries account 27109477752047116719 INN 123456789 bank code 01234 for consulting"
+  example_title: "English Transaction"
+- text: "Отправить 150тыс рублей на счет ООО Ромашка 40817810099910004312 ИНН 987654321 за услуги"
+  example_title: "Russian Transaction"
+- text: "44380583609046995897 ҳисобга 170190.66 UZS ўтказиш Голден Стар ИНН 485232484"
+  example_title: "Uzbek Cyrillic Transaction"
+- text: "Show completed transactions from 01.12.2024 to 15.12.2024"
+  example_title: "Query Request"
+library_name: transformers
+pipeline_tag: token-classification
+---
+
+# 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
+
+```bash
+pip install transformers torch
+```
+
+### Basic Usage
+
+```python
+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
+
+```python
+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**:
+```python
+{
+    "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**:
+```python
+{
+    "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**:
+```python
+{
+    "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
+
+```bibtex
+@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.
+
+---
+
+**Model Card Authors**: Primel
+**Last Updated**: 2025
+**Model Version**: 1.0

label_mappings.json

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+{
+  "tag2id": {
+    "B-amount": 0,
+    "B-currency": 1,
+    "B-description": 2,
+    "B-receiver_hr": 3,
+    "B-receiver_inn": 4,
+    "B-receiver_name": 5,
+    "I-amount": 6,
+    "I-currency": 7,
+    "I-description": 8,
+    "I-receiver_hr": 9,
+    "I-receiver_inn": 10,
+    "I-receiver_name": 11,
+    "O": 12
+  },
+  "id2tag": {
+    "0": "B-amount",
+    "1": "B-currency",
+    "2": "B-description",
+    "3": "B-receiver_hr",
+    "4": "B-receiver_inn",
+    "5": "B-receiver_name",
+    "6": "I-amount",
+    "7": "I-currency",
+    "8": "I-description",
+    "9": "I-receiver_hr",
+    "10": "I-receiver_inn",
+    "11": "I-receiver_name",
+    "12": "O"
+  },
+  "intent2id": {
+    "create_transaction": 0,
+    "help": 1,
+    "list_transaction": 2,
+    "unknown": 3
+  },
+  "id2intent": {
+    "0": "create_transaction",
+    "1": "help",
+    "2": "list_transaction",
+    "3": "unknown"
+  },
+  "lang2id": {
+    "en": 0,
+    "mixed": 1,
+    "ru": 2,
+    "uz_cyrl": 3,
+    "uz_latn": 4
+  },
+  "id2lang": {
+    "0": "en",
+    "1": "mixed",
+    "2": "ru",
+    "3": "uz_cyrl",
+    "4": "uz_latn"
+  }
+}

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special_tokens_map.json

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tokenizer_config.json

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+}

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training_config.json

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+  "num_val_samples": 60175,
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