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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. 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 and Yelp Dataset.


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

import torch
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("activebus/BERT-PT_laptop")
model = AutoModel.from_pretrained("activebus/BERT-PT_laptop")

Evaluation Results

Check our NAACL paper


If you find this work useful, please cite as following.

    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",
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