hertz-pj/BERT-BiLSTM-CRF-NER-pytorch
A BERT-based deep learning model combining BiLSTM and CRF layers for Chinese Named Entity Recognition.
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This repository implements a BERT-BiLSTM-CRF architecture for sequence labeling in Named Entity Recognition tasks. It uses BERT as a feature extractor, BiLSTM to capture sequential dependencies, and a CRF layer to enforce valid tag sequences. The model processes text in BIO format and identifies entity spans across categories like names, addresses, companies, and organizations. Evaluation results on the CLUENER2020 Chinese dataset are provided in the README.
Frequently asked
- What is hertz-pj/BERT-BiLSTM-CRF-NER-pytorch?
- A BERT-based deep learning model combining BiLSTM and CRF layers for Chinese Named Entity Recognition.
- Is BERT-BiLSTM-CRF-NER-pytorch open source?
- Yes — hertz-pj/BERT-BiLSTM-CRF-NER-pytorch is an open-source project tracked on heatdrop.
- What language is BERT-BiLSTM-CRF-NER-pytorch written in?
- hertz-pj/BERT-BiLSTM-CRF-NER-pytorch is primarily written in Python.
- How popular is BERT-BiLSTM-CRF-NER-pytorch?
- hertz-pj/BERT-BiLSTM-CRF-NER-pytorch has 427 stars on GitHub.
- Where can I find BERT-BiLSTM-CRF-NER-pytorch?
- hertz-pj/BERT-BiLSTM-CRF-NER-pytorch is on GitHub at https://github.com/hertz-pj/BERT-BiLSTM-CRF-NER-pytorch.