# 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",
}
```

{}
transformers pytorch jax safetensors bert fill-mask autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 

# 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 and Yelp Dataset.  

'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., 




## Evaluation Results

Check our NAACL paper 


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


# 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 and Yelp Dataset.  

'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., ## Evaluation Results

Check our NAACL paper 


If you find this work useful, please cite as following.
TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
 # 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 and Yelp Dataset.  

'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., ## Evaluation Results

Check our NAACL paper 


If you find this work useful, please cite as following.
passage: TAGS
#transformers #pytorch #jax #safetensors #bert #fill-mask #autotrain_compatible #endpoints_compatible #region-us 
# 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 and Yelp Dataset.  

'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.,## Evaluation Results

Check our NAACL paper 


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