license: apache-2.0
datasets:
- HuggingFaceTB/issues-kaggle-notebooks
language:
- en
metrics:
- accuracy
base_model:
- distilbert/distilroberta-base
new_version: distilbert/distilroberta-base
pipeline_tag: fill-mask
library_name: transformers
tags:
- blanksfiller
The Blank Space Filling Model is a Natural Language Processing (NLP) based web application that predicts the most suitable missing word in a sentence. This project
allows the user to enter a sentence with a blank space (such as ____ or [MASK]) and the system intelligently fills in the missing word using a pre-trained Hugging Face Transformer model.
The application is developed using Python and Streamlit, which provides a simple and interactive web interface. The model used in this project is based on Transformer architecture, specifically a masked language model, which is trained to understand sentence context and predict hidden or missing words.
To make the application portable and easy to deploy on any system, the project is also containerized using Docker. This ensures that all dependencies and configurations remain the same across different machines.
This project demonstrates the practical use of Artificial Intelligence, Machine Learning, and Natural Language Processing in building smart language-based applications.
link= https://huggingface.co/spaces/abhijitdas2821/maskfilling