We can create a model from AutoModel(TFAutoModel) function:
from transformers import AutoModel
model = AutoModel.from_pretrained('distilbert-base-uncase')
In other hand, a model is created by AutoModelForSequenceClassification(TFAutoModelForSequenceClassification):
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification('distilbert-base-uncase')
As I know, both models use distilbert-base-uncase library to create models. From name of methods, the second class( AutoModelForSequenceClassification ) is created for Sequence Classification.
But what are really differences in 2 classes? And how to use them correctly?
(I searched in huggingface but it is not clear)
The difference between AutoModel and AutoModelForSequenceClassification model is that AutoModelForSequenceClassification has a classification head on top of the model outputs which can be easily trained with the base model