| # ReviewBERT | |
| BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects. | |
| `BERT-DK_rest` is trained from 1G (19 types) restaurants from Yelp. | |
| `BERT-PT_*` addtionally uses SQuAD 1.1. | |
| ## 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/). | |
| ## 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-PT_rest") | |
| model = AutoModel.from_pretrained("activebus/BERT-PT_rest") | |
| ``` | |
| ## 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", | |
| } | |
| ``` | |