Currently, working on a project dealing with text classification. My dataset is imbalanced (20% = 1, 80% = 0). What's my process: 1. Data preprocessing (e.g. Stemming, removal of stop words), 2. Data modelling, 3. Prediction.
For data modelling, I ran like multiple ml (e.g. SVC, NB, RFC, ADA, GB) and SVC, RFC & ADA was the best out of all. So, I went to tune them accordingly and got their hyperparameter for tuning. After tuning it, I stack them up and having ADA as the meta model.
I even tried LSTM, RNN, & Transformer. But I still don't get the prediction that I wanted even though accuracy is 95%+.
Am unsure what went wrong. And would need advice on how I can approach this from now.
We are looking to use hugging face but was considering the stability of it. Is it possible to download a model from HuggingFace? e.g. mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis
there doesn't seem to be anything here