 Usage  ```python from transformers import AutoTokenizer from modeling_ct5 import CT5ForConditionalGeneration  tokenizer = AutoTokenizer.from_pretrained("mtreviso/ct5-small-en-wiki") model = CT5ForConditionalGeneration.from_pretrained("mtreviso/ct5-small-en-wiki") ```  For training:  ```python input_ids = tokenizer("The <extra_id_0> walks in <extra_id_1> park", return_tensors="pt").input_ids labels = tokenizer("<extra_id_0> man </c> <extra_id_1> the </c> <extra_id_2>", return_tensors="pt").input_ids outputs = model(input_ids=input_ids, labels=labels) loss = outputs.loss logits = outputs.logits ```  For generation:  ```python texts = [     "The <extra_id_0> walks in <extra_id_1> park",     "UN Chief says there is no way to <extra_id_0> in Syria", ] input_ids = tokenizer(texts, return_tensors="pt", padding=True).input_ids generated_ids = model.generate(     input_ids,      use_cache=False,  