SPBERT MLM (Scratch)
Introduction
Authors: Hieu Tran, Long Phan, James Anibal, Binh T. Nguyen, Truong-Son Nguyen
How to use
For more details, do check out our Github repo.
Here is an example in Pytorch:
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-zero')
model = AutoModel.from_pretrained("razent/spbert-mlm-zero")
text = "select * where brack_open var_a var_b var_c sep_dot brack_close"
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
or Tensorflow
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained('razent/spbert-mlm-zero')
model = TFAutoModel.from_pretrained("razent/spbert-mlm-zero")
text = "select * where brack_open var_a var_b var_c sep_dot brack_close"
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
Citation
@misc{tran2021spbert,
title={SPBERT: An Efficient Pre-training BERT on SPARQL Queries for Question Answering over Knowledge Graphs},
author={Hieu Tran and Long Phan and James Anibal and Binh T. Nguyen and Truong-Son Nguyen},
year={2021},
eprint={2106.09997},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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