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README.md
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# SPBERT MLM (Scratch)
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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-zero')
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model = AutoModel.from_pretrained("razent/spbert-mlm-zero")
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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-zero')
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model = TFAutoModel.from_pretrained("razent/spbert-mlm-zero")
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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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```
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