Create README.md

README.md

@@ -0,0 +1,107 @@
+# 🧠 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.