Hi Experts,

We are dealing with Text Classification Problem. We have around 80K records with around 50 classes. The data is highly imbalanced. It has 2 columns one for description and other contains class.
Till now we have tried following models and techniques:

1. Data Preprocessing: a. Lowercase conversion, removed numeric texts, removed punctuations b. Removed unimportant words and stop words c. Lemmatization
2. TFIDF transformation
3. Using SKLEARN Models: a. Linear SVC b. Linear Regression c. Logistic Regression d. Decision Trees e. Random Forest
4. Using Huggingface Transformers: a. Google Bert b. Distil Bert
5. SMOTE sampling

It is observed that the maximum accuracy we got is 70% (Random Forest and Google Bert).
Is there any scope to improve accuracy?
If yes, what other techniques or models we can use to improve accuracy?