Edit model card

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

image/png

image/png

image/png

image/png

image/png

Downloads last month
12
Safetensors
Model size
125M params
Tensor type
F32
·