tags:
- reviews
- multi-class
- classifier
- text classification
- roberta-base
widget:
- text: >-
The room is looks better than what we have expected. Spacious and clean.
The room is warm all the time and the beds are so soft and comfortable to
sleep on. We dont get a lot of sleep in Tokyo, and sleeping on Minn's bed
was the greatest thing ever.The hotel is near the JR and Nijojo Mae
station & lots of supermarket.Thank you for having us. This place is
highly recommended if you decide to stay in Kyoto.
- text: >-
We went for a weekend to be out in nature with our kids and a friend. The
house is very cute inside and decorated nicely BUT the property photos
leave out a house right next-door, so not private, a messy yard area w
broken down sheds and construction, a gun range close by so all we could
hear was gunshots all day, the kitchen cabinets esp the pantry were dirty
and filled w junk and the hot tub was foggy, dirty and they must have just
dumped a lot of bleach in rather than balancing the chemicals and cleaning
it properly because everyone got rashes/eye irritation/headaches and had
to get out and shower. The house really only sleeps five and you are stuck
scrounging for pillows blankets and sheets and blowing up an aero bed for
anyone else. The first one had a leak so we had to find a second and do it
all again. We could not find a trundle bed. I really wanted to like it as
cute as the pictures are but the real thing leaves a lot to be desired.
- text: Was quiet and nice
Jupyter Notebooks
GitHub link : lihuicham/airbnb-helpfulness-classifier
Fine-tuning Python code in finetuning.ipynb
Team Members (S001 - Synthetic Expert Team E) :
Li Hui Cham, Isaac Sparrow, Christopher Arraya, Nicholas Wong, Lei Zhang, Leonard Yang
Description
This model is an AirBnB reviews helpfulness classifier. It can predict the helpfulness, from most helpful (A) to least helpful (C) of the reviews on AirBnB website.
Pre-trained LLM
Our project fine-tuned FacebookAI/roberta-base for multi-class text (sequence) classification.
Dataset
5000 samples are scraped from AirBnB website based on listing_id from this Kaggle AirBnB Listings & Reviews dataset.Samples were translated from French to English language.
Training Set : 4560 samples synthetically labelled by GPT-4 Turbo. Cost was approximately $60.
Test/Evaluation Set : 500 samples labelled manually by two groups (each group labelled 250 samples), majority votes applies. A scoring rubrics (shown below) is used for labelling.
Training Details
hyperparameters = {'learning_rate': 3e-05,
'per_device_train_batch_size': 16,
'weight_decay': 1e-04,
'num_train_epochs': 4,
'warmup_steps': 500}
We trained our model on Colab Pro which costed us approximately 56 computing units.
Slides
