finetuning-sentiment-model-bank_reviews-otherbank

This model is a fine-tuned version of distilbert/distilbert-base-uncased-finetuned-sst-2-english on app store reviews from OCBC bank and POSB bank (Singapore). It achieves the following results on the evaluation set:

• Loss: 0.4811
• Accuracy: 0.8630
• F1: 0.6970

Model description

Data was labelled according to review stars. If stars >3, review was ranked positive. Otherwise, it is labelled as negative. We have tried 4 stars instead of 3 as app developers would deem any negativity in reviews as negative as a whole, but accuracy dropped. Further investigations will need to be run. Above 4 stars positive: https://huggingface.co./ajiayi/finetuning-sentiment-model-bank_reviews-otherbank-4insteadof3 All data (OCBC,POSB,GXS): https://huggingface.co./ajiayi/finetuning-sentiment-model-bank_reviews

Intended uses & limitations

Model was used in the following project: https://github.com/weixuanontherun/DSA3101_Group-19 It was finetuned using OCBC and POSB and tested on GXS bank reviews.

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 2

Framework versions

• Transformers 4.38.2
• Pytorch 2.2.1+cu121
• Tokenizers 0.15.2