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.

Examples

Here are some examples of outputs from the model:

Google Colab Notebook

You can view and run the Google Colab notebook for this project here.

Acknowledgements

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

Author

Feedback

If you have any feedback, please reach out to us at [email protected].

AdilHayat173
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token_classification