Emrys-Hong/fastai_sequence_tagging
sequence tagging for NER for ULMFiT
This project helps natural language processing (NLP) practitioners improve how computers identify and categorize specific entities in text, like names, organizations, or locations. It takes raw text data and outputs labeled text, highlighting and classifying these entities. Data scientists and NLP engineers working on information extraction or content analysis tasks would find this useful.
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Use this if you are a data scientist or NLP engineer looking to experiment with ULMFiT-based sequence tagging for named entity recognition (NER) and want a starting point with specific architectural modifications.
Not ideal if you are looking for a plug-and-play solution without needing to engage with Python code and neural network architectures, or if you require an out-of-the-box, state-of-the-art NER model.
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20
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5
Language
Jupyter Notebook
License
MIT
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Last pushed
Nov 04, 2020
Commits (30d)
0
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