# RoBERTa-Base Quantized Model for Named Entity Recognition (NER)

This repository contains a quantized version of the RoBERTa model fine-tuned for Named Entity Recognition (NER) on the WikiANN (English) dataset. The model is particularly suitable for **tagging named entities in news articles**, such as persons, organizations, and locations. It has been optimized for efficient deployment using quantization techniques.


## Model Details
 
- **Model Architecture:** RoBERTa Base   
- **Task:** Named Entity Recognition 
- **Dataset:** WikiANN (English)
- **Use Case:** Tagging news articles with named entities  
- **Quantization:** Float16  
- **Fine-tuning Framework:** Hugging Face Transformers  

## Usage
 
### Installation
 
```sh
pip install transformers torch
```
 
 
### Loading the Model
 
```python
 
from transformers import RobertaTokenizerFast, RobertaForSequenceClassification, Trainer, TrainingArguments
import torch


 
# Load tokenizer

tokenizer = RobertaTokenizerFast.from_pretrained("roberta-base")
 
# Create NER pipeline
ner_pipeline = pipeline(
    "ner",
    model=model,
    tokenizer=tokenizer,
    aggregation_strategy="simple"
)

# Sample news headline
text = "Apple Inc. is planning to open a new campus in London by the end of 2025."

# Inference
entities = ner_pipeline(text)

# Display results
for ent in entities:
    print(f"{ent['word']}: {ent['entity_group']} ({ent['score']:.2f})")

```
 
## Performance Metrics
 
- **Accuracy:** 0.923422	
- **Precision:** 0.923052	
- **Recall:** 0.923422
- **F1:** 0.923150

 
## Fine-Tuning Details
 
### Dataset
 
The dataset is taken from Hugging Face WikiANN (English).
 
### Training
 
- Number of epochs: 5  

- Batch size: 16

- Evaluation strategy: epoch  

- Learning rate: 3e-5  
 
### Quantization
 
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
 
## Repository Structure
 
```

.
├── config.json
├── tokenizer_config.json    
├── sepcial_tokens_map.json 
├── tokenizer.json        
├── model.safetensors    # Fine Tuned Model
├── README.md            # Model documentation

```
 
## Limitations
 
- The model may not generalize well to domains outside the fine-tuning dataset.  

- Quantization may result in minor accuracy degradation compared to full-precision models.  
 
## Contributing
 
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.