Welcome to the Named Entity Recognition with BERT-base-NER project! This repository focuses on utilizing the dslim/bert-base-NER
model to perform Named Entity Recognition (NER) tasks.
Named Entity Recognition (NER) is a natural language processing task where the goal is to identify and classify named entities mentioned in unstructured text into predefined categories such as person names, organizations, locations, dates, etc. BERT-base-NER
, based on the powerful BERT architecture, offers state-of-the-art performance in NER tasks.
To get started, create a virtual environment and activate it:
virtualenv venv
source venv/bin/activate
Next, install the required dependencies using pip:
pip install -r requirements.txt
Now, you can run the application:
gradio app.py
This will start the application, allowing you to input text and the DistilBART-CNN model can now classify named entities.
Simply input the text you want into the application's interface. The model will then process the input and provide a named entity of the text.
- Check out the model on
- Check out the Colab notebook for this project on
- Explore more about the BERT-base-NER model here
Feel free to reach out if you have any questions or feedback!