SOBertBase

Model Description

SOBertBase is a 109M parameter BERT model trained on 27 billion tokens of SO data StackOverflow answer and comment text using the Megatron Toolkit.

SOBert is pre-trained with 19 GB data presented as 15 million samples where each sample contains an entire post and all its corresponding comments. We also include all code in each answer so that our model is bimodal in nature. We use a SentencePiece tokenizer trained with BytePair Encoding, which has the benefit over WordPiece of never labeling tokens as “unknown". Additionally, SOBert is trained with a a maximum sequence length of 2048 based on the empirical length distribution of StackOverflow posts and a relatively large batch size of 0.5M tokens. A larger 762 million parameter model can also be found here. More details can be found in the paper Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models.

How to use

from transformers import MegatronBertModel,PreTrainedTokenizerFast
model = MegatronBertModel.from_pretrained("mmukh/SOBertBase")
tokenizer = PreTrainedTokenizerFast.from_pretrained("mmukh/SOBertBase")

BibTeX entry and citation info

@article{mukherjee2023stack,
  title={Stack Over-Flowing with Results: The Case for Domain-Specific Pre-Training Over One-Size-Fits-All Models},
  author={Mukherjee, Manisha and Hellendoorn, Vincent J},
  journal={arXiv preprint arXiv:2306.03268},
  year={2023}
}