ROBINADC/BiGRU-CRF-with-Attention-for-NER
Named Entity Recognition (NER) with different combinations of BiGRU, Self-Attention and CRF
This project helps natural language processing researchers and students compare different machine learning architectures for Named Entity Recognition (NER). It takes raw text data, processes it, and then trains various models to identify and classify entities like names, organizations, or locations within the text. The output is a trained NER model and performance metrics, allowing users to understand which model configurations work best for this specific task.
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Use this if you are a researcher or student in natural language processing and want to experiment with different NER model architectures and input embeddings to optimize performance on a dataset.
Not ideal if you need a ready-to-use NER solution for a production system or if you are not familiar with machine learning model training and evaluation.
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Language
Python
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MIT
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Last pushed
Jan 08, 2021
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