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LICENSE

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+MIT License
+
+Copyright (c) 2025 Mykyta Kotenko
+
+Based on RoBERTa model from Hugging Face Transformers library.
+Original RoBERTa model: Copyright (c) Facebook, Inc. and its affiliates.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+language: en
+license: mit
+tags:
+- token-classification
+- named-entity-recognition
+- ner
+- contact-management
+- roberta
+base_model: roberta-base
+datasets:
+- custom
+model-index:
+- name: assistant-bot-ner-model
+  results:
+  - task:
+      type: token-classification
+      name: Named Entity Recognition
+    metrics:
+    - type: accuracy
+      value: 0.951
+      name: Accuracy
+    - type: f1
+      value: 0.946
+      name: F1 Score
+---
+
+# NER Model for Contact Management Assistant Bot
+
+This model is a fine-tuned RoBERTa-base model for Named Entity Recognition (NER) in contact management tasks.
+
+## Model Description
+
+- **Developed by:** Mykyta Kotenko
+- **Base Model:** [roberta-base](https://huggingface.co/roberta-base) by Facebook AI
+- **Task:** Token Classification (Named Entity Recognition)
+- **Language:** English
+- **License:** MIT
+- **Accuracy:** 95.1%
+- **Entity Accuracy:** 93.7%
+- **F1 Score:** 94.6%
+
+## Supported Entities
+
+This model extracts the following entity types:
+
+- **NAME**: Person's full name
+- **PHONE**: Phone numbers in various formats
+- **EMAIL**: Email addresses
+- **ADDRESS**: Full street addresses (including building numbers, street names, apartments, cities, states, ZIP codes)
+- **BIRTHDAY**: Dates of birth
+- **TAG**: Contact tags
+- **NOTE_TEXT**: Note content
+- **ID**: Contact/note identifiers
+- **DAYS**: Time periods
+
+## Usage
+
+### Basic Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("mykytakotenko/assistant-bot-ner-model")
+model = AutoModelForTokenClassification.from_pretrained("mykytakotenko/assistant-bot-ner-model")
+
+# Create NER pipeline
+ner_pipeline = pipeline(
+    "token-classification",
+    model=model,
+    tokenizer=tokenizer,
+    aggregation_strategy="simple"  # Merge B-/I- tokens
+)
+
+# Extract entities
+text = "Add contact John Smith 212-555-0123 john@example.com 123 Broadway, New York"
+results = ner_pipeline(text)
+
+for result in results:
+    print(f"{result['entity_group']}: {result['word']}")
+```
+
+**Output:**
+```
+NAME: John Smith
+PHONE: 212-555-0123
+EMAIL: john@example.com
+ADDRESS: 123 Broadway, New York
+```
+
+### Advanced Usage with Address Recognition
+
+```python
+# Example with full address including building number
+text = "Add contact Alon 212-555-0123 alon@example.com 45, 5 Ave, unit 34, New York"
+results = ner_pipeline(text)
+
+for result in results:
+    print(f"{result['entity_group']}: {result['word']}")
+```
+
+**Output:**
+```
+NAME: Alon
+PHONE: 212-555-0123
+EMAIL: alon@example.com
+ADDRESS: 45, 5 Ave, unit 34, New York
+```
+
+### Batch Processing
+
+```python
+texts = [
+    "Add contact Sarah 718-555-4567 sarah@email.com lives at 123 Broadway, Apt 5B, NY 10001",
+    "Create contact Michael at 789 Park Avenue, Suite 12, Manhattan, NY 10021 phone 917-555-8901",
+    "Register David Martinez 1234 Sunset Boulevard, Los Angeles, CA 90028"
+]
+
+for text in texts:
+    results = ner_pipeline(text)
+    print(f"\nText: {text}")
+    for result in results:
+        print(f"  - {result['entity_group']}: {result['word']}")
+```
+
+## Training Details
+
+### Dataset
+- **Size:** 2,185 training examples
+- **ADDRESS entities:** 543 occurrences (including full street addresses with building numbers)
+- **NAME entities:** 1,897 occurrences
+- **PHONE entities:** 564 occurrences
+- **EMAIL entities:** 415 occurrences
+- **BIRTHDAY entities:** 252 occurrences
+
+### Training Configuration
+- **Base Model:** roberta-base
+- **Learning Rate:** 3e-5
+- **Batch Size:** 16
+- **Max Length:** 128 tokens
+- **Epochs:** 5
+- **Optimizer:** AdamW
+- **Training Framework:** Hugging Face Transformers
+
+### Performance Metrics
+
+| Metric | Value |
+|--------|-------|
+| Accuracy | 95.1% |
+| Entity Accuracy | 93.7% |
+| Precision | 94.9% |
+| Recall | 95.1% |
+| F1 Score | 94.6% |
+
+## Key Features
+
+### ✅ Full Address Recognition
+Unlike many NER models that only recognize city names, this model recognizes **complete street addresses** including:
+- Building numbers (45, 123, 1234, etc.)
+- Street names (Broadway, 5 Ave, Sunset Boulevard, etc.)
+- Unit/Apartment numbers (unit 34, Apt 5B, Suite 12, Floor 3)
+- Cities and states (New York, NY, Los Angeles, CA, etc.)
+- ZIP codes (10001, 90028, 77002, etc.)
+
+### Example: Full Address Recognition
+
+**Before (typical NER models):**
+```
+Input: "add address for Alon 45, 5 ave, unit 34, New York"
+ADDRESS: "New York" ❌ (only city)
+```
+
+**After (this model):**
+```
+Input: "add address for Alon 45, 5 ave, unit 34, New York"
+ADDRESS: "45, 5 ave, unit 34, New York" ✅ (full address with building number!)
+```
+
+## Example Predictions
+
+### Example 1: Complete Contact
+```python
+text = "Add contact John Smith 212-555-0123 john@example.com 45, 5 Ave, unit 34, New York"
+```
+**Extracted Entities:**
+- NAME: John Smith
+- PHONE: 212-555-0123
+- EMAIL: john@example.com
+- ADDRESS: 45, 5 Ave, unit 34, New York
+
+### Example 2: Address with ZIP Code
+```python
+text = "Create contact Sarah at 123 Broadway, Apt 5B, New York, NY 10001"
+```
+**Extracted Entities:**
+- NAME: Sarah
+- ADDRESS: 123 Broadway, Apt 5B, New York, NY 10001
+
+### Example 3: Complex Address
+```python
+text = "Save contact for Michael at 789 Park Avenue, Suite 12, Manhattan, NY 10021 phone 917-555-8901"
+```
+**Extracted Entities:**
+- NAME: Michael
+- PHONE: 917-555-8901
+- ADDRESS: 789 Park Avenue, Suite 12, Manhattan, NY 10021
+
+### Example 4: Different City
+```python
+text = "Register David Martinez 1234 Sunset Boulevard, Los Angeles, CA 90028"
+```
+**Extracted Entities:**
+- NAME: David Martinez
+- ADDRESS: 1234 Sunset Boulevard, Los Angeles, CA 90028
+
+## Intended Use
+
+This model is designed for:
+- Contact management applications
+- Personal assistant bots
+- CRM systems with natural language interface
+- Address extraction from text
+- Contact information parsing
+
+## Limitations
+
+- **Optimized for US-style addresses** - International addresses not yet in training data
+- **Best performance on English text** - Other languages not supported
+- **Contact management domain** - May not generalize well to other domains without fine-tuning
+
+## Model Architecture
+
+Based on RoBERTa (Robustly Optimized BERT Pretraining Approach):
+- **Layers:** 12 transformer layers
+- **Hidden size:** 768
+- **Attention heads:** 12
+- **Parameters:** ~125M
+- **Task:** Token Classification with IOB2 tagging scheme
+
+## Entity Label Format
+
+The model uses IOB2 (Inside-Outside-Beginning) format:
+- `B-{ENTITY}`: Beginning of entity
+- `I-{ENTITY}`: Inside/continuation of entity
+- `O`: Outside any entity
+
+Example:
+```
+Tokens:  ["Add", "contact", "John", "Smith", "212", "-", "555", "-", "0123"]
+Labels:  ["O",   "O",       "B-NAME", "I-NAME", "B-PHONE", "I-PHONE", "I-PHONE", "I-PHONE", "I-PHONE"]
+```
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{kotenko2025nermodel,
+  author = {Kotenko, Mykyta},
+  title = {NER Model for Contact Management Assistant Bot},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/mykytakotenko/assistant-bot-ner-model}},
+  note = {Based on RoBERTa by Facebook AI. Achieves 95.1\% accuracy with full address recognition including building numbers.}
+}
+```
+
+## Acknowledgments
+
+- **Base Model:** RoBERTa by Facebook AI Research
+- **Framework:** Hugging Face Transformers
+- **Training:** Fine-tuned on custom contact management dataset with 2,185 examples
+- **Special Feature:** Enhanced address recognition with building numbers, apartments, and full street addresses
+
+## Technical Improvements
+
+This model includes several technical improvements over standard NER models:
+
+1. **Enhanced Tokenization:** Improved handling of addresses with fuzzy matching algorithm
+2. **Rich Training Data:** 115+ real-world address examples from major US cities
+3. **Address Variations:** Multiple formats including "address-first" patterns
+4. **High Accuracy:** 95.1% overall accuracy, 93.7% entity-level accuracy
+
+## Updates
+
+- **v1.0.0 (2025-01-18):** Initial release
+  - 95.1% accuracy
+  - Full address recognition with building numbers
+  - 2,185 training examples
+  - Support for 9 entity types
+
+## License
+
+MIT License - See LICENSE file for details.
+
+This model is a derivative work based on RoBERTa, which is licensed under MIT License by Facebook, Inc.
+
+## Contact
+
+- **Author:** Mykyta Kotenko
+- **Repository:** [assistant-bot](https://github.com/kms-engineer/assistant-bot)
+- **Issues:** Please report issues on GitHub
+- **Hugging Face:** [mykytakotenko](https://huggingface.co/mykytakotenko)
+
+## Related Models
+
+- **Intent Classifier:** [mykytakotenko/assistant-bot-intent-classifier](https://huggingface.co/mykytakotenko/assistant-bot-intent-classifier)
+- **Dataset:** [mykytakotenko/assistant-bot-ner-dataset](https://huggingface.co/datasets/mykytakotenko/assistant-bot-ner-dataset)

checkpoint-440/config.json

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