Pytorch model outputs are special dataclasses so that you can get autocompletion for their attributes in an IDE.
They also behave like a tuple or a dictionary (e.g., you can index with an integer, a slice or a string) in which
case the attributes not set (that have None values) are ignored.
Once your model is fine-tuned, you can save it with its tokenizer in the following way:
```py
pt_save_directory = "./pt_save_pretrained"
tokenizer.save_pretrained(pt_save_directory)
pt_model.save_pretrained(pt_save_directory)
===PT-TF-SPLIT===
tf_save_directory = "./tf_save_pretrained"
tokenizer.save_pretrained(tf_save_directory)
tf_model.save_pretrained(tf_save_directory)
```
You can then load this model back using the [AutoModel.from_pretrained] method by passing the
directory name instead of the model name. One cool feature of 🤗 Transformers is that you can easily switch between
