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--- |
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language: |
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- code |
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tags: |
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- question-answering |
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- knowledge-graph |
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--- |
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# SPBERT MLM+WSO (Initialized) |
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## Introduction |
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Paper: [SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs](https://arxiv.org/abs/2106.09997) |
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Authors: _Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen_ |
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## How to use |
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For more details, do check out [our Github repo](https://github.com/heraclex12/NLP2SPARQL). |
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Here is an example in Pytorch: |
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```python |
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from transformers import AutoTokenizer, AutoModel |
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tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-wso-base') |
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model = AutoModel.from_pretrained("razent/spbert-mlm-wso-base") |
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text = "select * where brack_open var_a var_b var_c sep_dot brack_close" |
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encoded_input = tokenizer(text, return_tensors='pt') |
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output = model(**encoded_input) |
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``` |
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or Tensorflow |
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```python |
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from transformers import AutoTokenizer, TFAutoModel |
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tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-wso-base') |
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model = TFAutoModel.from_pretrained("razent/spbert-mlm-wso-base") |
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text = "select * where brack_open var_a var_b var_c sep_dot brack_close" |
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encoded_input = tokenizer(text, return_tensors='tf') |
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output = model(encoded_input) |
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``` |
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## Citation |
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``` |
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@misc{tran2021spbert, |
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title={SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs}, |
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author={Hieu Tran and Long Phan and James Anibal and Binh T. Nguyen and Truong-Son Nguyen}, |
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year={2021}, |
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eprint={2106.09997}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
