transformers tensorboard safetensors bert token-classification named-entity-recognition en dataset:conll2003 license:mit autotrain_compatible endpoints_compatible region:us
---
datasets:
- conll2003
language: en
license: mit
tags:
- token-classification
- named-entity-recognition
- bert
- transformers
---

# Token Classification Model

## Description
This project involves developing a machine learning model for token classification, specifically for Named Entity Recognition (NER). Using a fine-tuned BERT model from the Hugging Face library, this system classifies tokens in text into predefined categories like names, locations, and dates.

The model is trained on a dataset annotated with entity labels to accurately classify each token. This token classification system is useful for information extraction, document processing, and conversational AI applications.

## Technologies Used

### Dataset
- **Source:** Kaggle: conll2003
- **Purpose:** Contains text data with annotated entities for token classification.

### Model
- **Base Model:** BERT (bert-base-uncased)
- **Library:** Hugging Face transformers
- **Task:** Token Classification (Named Entity Recognition)

### Approach

#### Preprocessing:
- Load and preprocess the dataset.
- Tokenize the text data and align labels with tokens.

#### Fine-Tuning:
- Fine-tune the BERT model on the token classification dataset.

#### Training:
- Train the model to classify each token into predefined entity labels.

#### Inference:
- Use the trained model to predict entity labels for new text inputs.

### Key Technologies
- **Deep Learning (BERT):** For advanced token classification and contextual understanding.
- **Natural Language Processing (NLP):** For text preprocessing, tokenization, and entity recognition.
- **Machine Learning Algorithms:** For model training and prediction tasks.

## Streamlit App
You can view and interact with the Streamlit app for token classification [here](https://huggingface.co/spaces/AdilHayat173/token_classifcation).

## Examples
Here are some examples of outputs from the model:

![example1](https://github.com/user-attachments/assets/9e9dd85c-1447-4229-b691-febec17439cf)
![example2](https://github.com/user-attachments/assets/97dfc391-bda9-4614-93f7-a5f45d64dd03)

## Google Colab Notebook
You can view and run the Google Colab notebook for this project [here](https://colab.research.google.com/drive/1GYVlIToQ_lnT8XEjGrR2WFkUQWpWXgQi#scrollTo=ZlyX1Lgn8gjj).

## Acknowledgements
- Hugging Face for transformer models and libraries.
- Streamlit for creating the interactive web interface.
- [Your Dataset Provider] for the token classification dataset.

## Author
- AdilHayat
- [Hugging Face Profile](https://huggingface.co/AdilHayat173)
- [GitHub Profile](https://github.com/AdilHayat21173)

## Feedback
If you have any feedback, please reach out to us at hayatadil300@gmail.com.
