Please help understand example code from Hugging Face for GPT2

`tokenizer = GPT2Tokenizer.from_pretrained("gpt2")`

`model = GPT2LMHeadModel.from_pretrained("gpt2")`

`inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")`

`outputs = model(**inputs, labels=inputs["input_ids"])`

`loss = outputs.loss`

`logits = outputs.logits`

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The code above is from hugging face documentation. Assuming I have an optimizer and do something like:

`loss.backward()`

`optimizer.step()`

Am I successfully "finetuning" the model with one input example? What exactly is happening in the line:

`outputs = model(**inputs, labels=inputs["input_ids"])`

Why is labels the input ids of the inputs?

Is it training as follows? Given …

code example face languagetechnology