Instructions to use abhijitdas2821/Maskfilling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use abhijitdas2821/Maskfilling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="abhijitdas2821/Maskfilling")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("abhijitdas2821/Maskfilling", dtype="auto") - Notebooks
- Google Colab
- Kaggle
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The Blank Space Filling Model is a Natural Language Processing (NLP) based web application that predicts the most suitable missing word in a sentence.
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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.
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This project demonstrates the practical use of Artificial Intelligence, Machine Learning, and Natural Language Processing in building smart language-based applications.
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link= https://huggingface.co/spaces/abhijitdas2821/maskfilling
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The Blank Space Filling Model is a Natural Language Processing (NLP) based web application that predicts the most suitable missing word in a sentence.
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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.
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This project demonstrates the practical use of Artificial Intelligence, Machine Learning, and Natural Language Processing in building smart language-based applications.
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link= https://huggingface.co/spaces/abhijitdas2821/maskfilling
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