---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased-finetuned-sst-2-english
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-bank_reviews-otherbank
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# finetuning-sentiment-model-bank_reviews-otherbank

This model is a fine-tuned version of [distilbert/distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/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. (GXS bank reviews NOT part of finetuning process)

## 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

### Training results
Model managed to achieve a 97-98% accuracy rate when run on GXS bank reviews. 


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Tokenizers 0.15.2