I’m training GPT-2, constructed in the following manner:
configuration = GPT2Config( output_hidden_states=True, ) architecture = AutoModelForCausalLM.from_config( configuration)
When I pass an input sequence into the model, like so, I can access its output logits:
# Output type: BaseModelOutputWithPastAndCrossAttentions architecture_output = self.architecture( input_ids=intractn.task_input_ids, attention_mask=intractn.task_attention_masks)
How does one convert the logits into string sentences? I discovered the
.generate() method, but this seems to generate new outputs.
I suppose I could convert the logits to a distribution, sample, convert to token ids and then use
tokenizer.decode() but that seems too manual. What’s the right way to do this in HuggingFace?