setfit training with a pandas dataframe

I would like to train a zero shot classifier on an annotated sample dataset.

I am following some tutorials but as all use their own data and the same pretarined model, I am trying to confirm: Is this the best approach?

Data example: 

import pandas as pd
from datasets import Dataset
    
# Sample feedback data, it will have 8 samples per label
feedback_dict = [
    {'text': 'The product is great and works well.', 'label': 'Product Performance'},
    {'text': 'I love the design of the product.', 'label': 'Product Design'},
    {'text': 'The product is difficult to use.', 'label': 'Usability'},
    {'text': 'The customer service was very helpful.', 'label': 'Customer Service'},
    {'text': 'The product was delivered on time.', 'label': 'Delivery Time'}
]

# Create a DataFrame with the feedback data
df = pd.DataFrame(feedback_dict)

# convert to Dataset format
df = Dataset.from_pandas(df)

By having the previous data format, this is the approach for model finetunning:

from setfit import SetFitModel, SetFitTrainer

# Select a model
model = SetFitModel.from_pretrained("sentence-transformers/paraphrase-mpnet-base-v2")

# training with Setfit
trainer = SetFitTrainer(
    model=model,
    train_dataset=df, # to keep the code simple I do not create the df_train
    eval_dataset=df, # to keep the code simple I do not create the df_eval
    column_mapping={"text": "text", "label": "label"} 
)

trainer.train()

The issue here is that the process never ends after more than 500 hours in a laptop, and the dataset it is only about 88 records with 11 labels.

from setfit training with a pandas dataframe