Hu Xu
9907dc523a
* add model_cards for BERT trained on reviews. * add link to repository. * refine README.md for each review model
45 lines
1.8 KiB
Markdown
45 lines
1.8 KiB
Markdown
# ReviewBERT
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BERT (post-)trained from review corpus to understand sentiment, options and various e-commence aspects.
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`BERT_Review` is cross-domain (beyond just `laptop` and `restaurant`) language model with one example from randomly mixed domains, 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`.
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The preprocessing code [here](https://github.com/howardhsu/BERT-for-RRC-ABSA/transformers).
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## Model Description
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The original model is from `BERT-base-uncased` trained from Wikipedia+BookCorpus.
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Models are post-trained from [Amazon Dataset](http://jmcauley.ucsd.edu/data/amazon/) and [Yelp Dataset](https://www.yelp.com/dataset/challenge/).
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## Instructions
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Loading the post-trained weights are as simple as, e.g.,
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```python
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import torch
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from transformers import AutoModel, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("activebus/BERT_Review")
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model = AutoModel.from_pretrained("activebus/BERT_Review")
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```
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## Evaluation Results
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Check our [NAACL paper](https://www.aclweb.org/anthology/N19-1242.pdf)
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`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).
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## Citation
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If you find this work useful, please cite as following.
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```
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@inproceedings{xu_bert2019,
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title = "BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis",
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author = "Xu, Hu and Liu, Bing and Shu, Lei and Yu, Philip S.",
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booktitle = "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics",
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month = "jun",
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year = "2019",
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}
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```
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