yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification
A TensorFlow-based template for applying BERT to sequence labeling and text classification tasks including NER and slot filling.
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This repository provides template code for using BERT (Bidirectional Encoder Representations from Transformers) to perform sequence labeling and text classification tasks. The code supports named entity recognition on the CoNLL-2003 dataset, and joint slot filling and intent prediction on the ATIS and SNIPS datasets. It uses TensorFlow for model implementation and training.
Frequently asked
- What is yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification?
- A TensorFlow-based template for applying BERT to sequence labeling and text classification tasks including NER and slot filling.
- Is BERT-for-Sequence-Labeling-and-Text-Classification open source?
- Yes — yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification is open source, released under the Apache-2.0 license.
- What language is BERT-for-Sequence-Labeling-and-Text-Classification written in?
- yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification is primarily written in Python.
- How popular is BERT-for-Sequence-Labeling-and-Text-Classification?
- yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification has 469 stars on GitHub.
- Where can I find BERT-for-Sequence-Labeling-and-Text-Classification?
- yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification is on GitHub at https://github.com/yuanxiaosc/BERT-for-Sequence-Labeling-and-Text-Classification.