Downloads · 30 days
9
0% of all-time downloads
lihuicham/airbnb-reviews-helpfulness-classifier-roberta-base
airbnb-reviews-helpfulness-classifier-roberta-base is a text classification model from lihuicham. Use it when you need a label for a piece of text. It is set up for transformers.
GitHub link : lihuicham/airbnb-helpfulness-classifier
Downloads · 30 days
9
0% of all-time downloads
All-time downloads
2K
Public
Parameters
125M
499 MB on disk
Likes
2
Public
Click a slice to open those files.
.safetensors499 MB · 99%
From the Hugging Face model README
GitHub link : lihuicham/airbnb-helpfulness-classifier
Fine-tuning Python code in finetuning.ipynb
Li Hui Cham, Isaac Sparrow, Christopher Arraya, Nicholas Wong, Lei Zhang, Leonard Yang
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.
Our project fine-tuned FacebookAI/roberta-base for multi-class text (sequence) classification.
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.
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.




