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# 🧠 NER-BERT-AI-Model-using-annotated-corpus-ner
A BERT-based Named Entity Recognition (NER) model fine-tuned on the Entity Annotated Corpus. It classifies tokens in text into predefined entity types such as Person (PER), Organization (ORG), and Location (LOC). This model is well-suited for information extraction, resume parsing, and chatbot applications.
---
## ✨ Model Highlights
- 📌 Based on `bert-base-cased` (by Google)
- 🔍 Fine-tuned on the Entity Annotated Corpus (`ner_dataset.csv`)
- ⚡ Supports prediction of 3 entity types: PER, ORG, LOC
- 💾 Compatible with Hugging Face `pipeline()` for easy inference
---
## 🧠 Intended Uses
- Resume and document parsing
- Chatbots and virtual assistants
- Named entity tagging in structured documents
- Search and information retrieval systems
- News or content analysis
---
## 🚫 Limitations
- Trained only on English formal texts
- May not generalize well to informal text or domain-specific jargon
- Subword tokenization may split entities (e.g., "Cupertino" → "Cup", "##ert", "##ino")
- Limited to the entities available in the original dataset (PER, ORG, LOC only)
---
## 🏋️♂️ Training Details
| Field | Value |
|---------------|------------------------------|
| Base Model | `bert-base-cased` |
| Dataset | Entity Annotated Corpus |
| Framework | PyTorch with Transformers |
| Epochs | 3 |
| Batch Size | 16 |
| Max Length | 128 tokens |
| Optimizer | AdamW |
| Loss | CrossEntropyLoss (token-level) |
| Device | Trained on CUDA-enabled GPU |
---
## 📊 Evaluation Metrics
| Metric | Score |
|-----------|-------|
| Precision | 83.15 |
| Recall | 83.85 |
| F1-Score | 83.50 |
---
## 🔎 Label Mapping
| Label ID | Entity Type |
|----------|--------------|
| 0 | O |
| 1 | B-PER |
| 2 | I-PER |
| 3 | B-ORG |
| 4 | I-ORG |
| 5 | B-LOC |
| 6 | I-LOC |
---
## 🚀 Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = "/AventIQ-AI/NER-BERT-AI-Model-using-annotated-corpus-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "My name is Wolfgang and I live in Berlin"
ner_results = nlp(example)
print(ner_results)
```
## 🧩 Quantization
Post-training quantization can be applied using PyTorch to reduce model size and improve inference performance, especially on edge devices.
## 🗂 Repository Structure
```
.
├── model/ # Trained model files
├── tokenizer_config/ # Tokenizer and vocab files
├── model.safensors/ # Model in safetensors format
├── README.md # Model card
```
## 🤝 Contributing
We welcome feedback, bug reports, and improvements!
Feel free to open an issue or submit a pull request. |