| from transformers import T5Tokenizer, T5ForConditionalGeneration | |
| tokenizer = T5Tokenizer.from_pretrained("t5-small") | |
| model = T5ForConditionalGeneration.from_pretrained("t5-small") | |
| max_source_length = 128 | |
| max_target_length = 128 | |
| input_ids = tokenizer("translate English to German: The house is wonderful.", return_tensors="pt").input_ids | |
| labels = tokenizer("Das Haus ist wunderbar.", return_tensors="pt").input_ids | |
| # the forward function automatically creates the correct decoder_input_ids | |
| loss = model(input_ids=input_ids, labels=labels).loss | |
| loss.item() | |
| print(loss.item()) |