Thank you. It really helps me.
I am using my customer data and it has a total of 9 labels the distributions are,
class-1: 223
class-2: 119
class-3: 42
class-4:  37
class-5:  24
class-6:  23
class-7: 16
class-8: 12
class-9:  4
if  I give all 9 classes to the model it treats the last 2-To 3 classes as outliers because it has very few samples.
so now my task is to take only the last four classes and try a few-shot classification on those samples.
and my assumption to perform few-shot classification is to use any pre-trained model so that it already has some knowledge(correct me if I am wrong and give any suggestions on how to do few-shot text classification)
so for that, i am using (model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=4))
and here I am not doing any test and train split because:

my samples are very less
even I do test and train split and see the scores on test data we can’t conclude the model is performing well even it is giving a 90 f1 score.
so I am doing manual eyeball evaluation by giving unlabelled samples to the model.
please suggest any other approaches to tackle this other than sampling.
I have another approach in my mind:
form classes, 1&2  take 5 samples calculate embeddings for 5 samples, and average it so that we get one embedding and map that embedding with class 1
if we do the above for all samples then we get ~40 embeddings for class1 220%5=~40
then we get balanced samples.
can you suggest how to do this experiment with HF and if you feel this really works?