How to train a model designed by myself with the Transformer Framework
Hello everyone.Since I met Hugging Face the first time, I have gone around exploring it with its own NLP course and other documentation for a while. But I haven’t seen any instructions about training architectures that I designed on my own. The most relevant thing is to load an existing architecture without parameters and train it from scratch.So, after I design my own architecture (with Tensorflow since I don’t think Transformers supports this), how can I make use of its Dataset, Tokenizer, or even Trainer and Accelerator libraries?My model class will look like:class Transformer(tf.keras.Model):definit(self, *, arguments):super().init()self.layer1 =  somethingself.layer2 = songthing

   self.final_layer = somehting

def call(self, inputs):and after preprocess data, i want to useprepare_tf_datasetand then start straining. It would be great if Accelerate, Trainer and other thing can also apply.Thank you guys so much.