model_cards.md

ReviewBERT

BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.

Model Description

The original model is from BERT-base-uncased trained from Wikipedia+BookCorpus.
Models are post-trained from Amazon Dataset and Yelp Dataset.

BERT-DK_laptop is trained from 100MB laptop corpus under Electronics/Computers & Accessories/Laptops. BERT-DK_rest is trained from 1G (19 types) restaurants from Yelp. BERT-PT_* addtionally uses SQuAD 1.1.

BERT_Review and BERT-XD_Review are cross-domain (beyond just laptop and restaurant) language models, post-trained (fine-tuned) on a combination of 5-core Amazon reviews and all Yelp data, expected to be 22 G in total. It is trained for 4 epochs on bert-base-uncased. The preprocessing code here.

Instructions

Loading the post-trained weights are as simple as, e.g.,

import torch
from transformers import BertModel

tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review")
model = AutoModel.from_pretrained("activebus/BERT_Review")

You can find the names of available models below.

Dataset	Laptop	Restaurant
BERT-DK	activebus/BERT-DK_laptop	activebus/BERT-DK_rest
BERT-PT	activebus/BERT-PT_laptop	activebus/BERT-PT_rest
BERT_Review activebus/BERT_Review
BERT-XD_Review activebus/BERT-XD_Review

Evaluation Results

Check our NAACL paper (WIP) BERT_Review is expected to have similar performance on domain-specific tasks (such as aspect extraction) as BERT-DK, but much better on general tasks such as aspect sentiment classification (different domains mostly share similar sentiment words).

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",
}