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---
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
should probably proofread and complete it, then remove this comment. -->
# 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.
## 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
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