yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification
This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.
This project helps you classify text and extract key information from sentences. You provide raw text data, and it identifies important entities within the text or categorizes the entire sentence based on its meaning. It's designed for data scientists or NLP engineers who need to quickly set up and experiment with powerful language models for various text understanding tasks.
471 stars. No commits in the last 6 months.
Use this if you need a pre-configured template to apply BERT for tasks like identifying named entities (people, places, organizations) in text or categorizing user queries into specific intents, saving significant setup time.
Not ideal if you are looking for a fully-fledged, production-ready natural language processing pipeline without any coding or model interaction.
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Python
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Apache-2.0
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Last pushed
Dec 08, 2022
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