readme: # ReviewBERT
  
  BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.  
  
  `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. 
  
  
  ## Model Description
  
  The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.  
  Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).  
  
  `BERT-DK_laptop` is trained from 100MB laptop corpus under `Electronics/Computers & Accessories/Laptops`. 
  
  ## Instructions
  Loading the post-trained weights are as simple as, e.g., 
  
  ```python
  import torch
  from transformers import AutoModel, AutoTokenizer
  
  tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-DK_laptop")
  model = AutoModel.from_pretrained("activebus/BERT-DK_laptop")
  
  ```
  
  
  ## Evaluation Results
  
  Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf) 
  
  
  ## Citation
  If you find this work useful, please cite as following.
  ```
  @inproceedings{xu_bert2019,
      title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
      author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
      booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
      month = "jun",
      year = "2019",
  }
  ```
  