# Model Name: Your Model's Name

## Model Description
This model is a **Named Entity Recognition (NER)** model fine-tuned on the **CoNLL-03** dataset. It is designed to recognize **person**, **organization**, and **location** entities in English text. The model is based on the **BERT architecture** and is useful for information extraction tasks, such as named entity recognition in documents, web scraping, or chatbots.

### Model Architecture
- **Architecture**: BERT-based model for token classification
- **Pre-trained Model**: BERT
- **Fine-tuning Dataset**: CoNLL-03
- **Languages**: English

## Intended Use
This model is designed for Named Entity Recognition tasks. It can identify and classify entities such as:
- **Person**: People’s names (e.g., "Elon Musk")
- **Organization**: Company or organization names (e.g., "Tesla", "Bank of America")
- **Location**: Geographical locations (e.g., "New York", "Paris")

### Use Cases
- **Document classification**: Classifying text into named entity categories.
- **Information extraction**: Extracting entities from a large corpus of text.
- **Chatbots**: Enhance chatbots by identifying named entities within user queries.
- **Named entity linking**: Link entities to a knowledge base.

## How to Use
To use the model, you need to load the tokenizer and model with the `transformers` library. Here's an example of how to do that:

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline

# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("your-username/your-model-name")
model = AutoModelForTokenClassification.from_pretrained("your-username/your-model-name")

# Initialize the NER pipeline
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)

# Use the model to predict named entities in a text
result = ner_pipeline("Elon Musk is the CEO of Tesla and lives in California.")
print(result)

# Model Training Data
This model was trained on the CoNLL-03 dataset, which contains English text annotated with named entity labels. The dataset consists of:

Training set: 14,041 sentences
Validation set: 3,466 sentences
Test set: 3,684 sentences
The entities are labeled into three categories: Person, Organization, and Location.

# Preprocessing Steps
Tokenization using the BERT tokenizer.
Alignment of labels with tokenized inputs (considering word-piece tokens).
Padding and truncating sentences to a fixed length for uniformity.