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fani-lab / LADy

LADy 💃: A Benchmark Toolkit for Latent Aspect Detection Enriched with Backtranslation Augmentation
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Check the existing readme and codeline #46

Open farinamhz opened 1 year ago

farinamhz commented 1 year ago

Hey @Lillliant, @DelaramRajaei and @impedaka,

Our codeline and readme have been recently updated. I kindly request you review the instructions in the readme for the installation process and obtain the latest version from the main branch. Please let me know if you encounter any issues with the code or readme or have any suggestions for improving the readme.

Feel free to raise any problems or questions you may have here.

@hosseinfani

farinamhz commented 10 months ago

Hey @Lillliant, I hope you had a wonderful start to the new year!

I was wondering if you could help us update our README for the Twitter dataset by adding new information to the tables or wherever it's necessary. This might involve including:

If there are any specific links you require from our LADy results on OneDrive that aren't already available on GitHub, please don't hesitate to inform me, and I'll provide you with a public link to access them.

In the meantime, you can take an overall look and update other sections that need updating as well. (I think files are also not updated for example for the baselines in aml)

Lillliant commented 10 months ago

Hi @farinamhz,

For this week, I've added information regarding the twitter dataset and adding information regarding the updated fasttext and bert baselines. I've also added a new image for the abstract aspect model image, which includes the two baselines. This part is already in the pull request.

In the meanwhile, I'm currently taking a look at the other sections and see if they need update. Some of the parts I've added are:

I also have some questions: in the readme, does google review dataset refer to the twitter dataset, or something else? As well, are sentiment analysis and web app ready for the readme? I've added brief mention of sentiment analysis in the aml structure, but I'm wondering if I can start adding information about AbstractSentimentModel, how to access web app, etc.