hezar.models.text_generation.gpt2.gpt2_text_generation module

class hezar.models.text_generation.gpt2.gpt2_text_generation.GPT2TextGeneration(config: GPT2TextGenerationConfig, **kwargs)

Bases: Model

compute_loss(logits: Tensor, labels: Tensor) → Tensor

Compute loss on the model outputs against the given labels

Parameters:

• logits – Logits tensor to compute loss on

• labels – Labels tensor

Note: Subclasses can also override this method and add other arguments besides logits and labels

Returns:

Loss tensor

forward(token_ids, past_key_values=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, encoder_hidden_states=None, encoder_attention_mask=None, use_cache=None, output_attentions=None, output_hidden_states=None, return_dict=None, **kwargs)

Forward inputs through the model and return logits, etc.

Parameters:

model_inputs – The required inputs for the model forward

Returns:

A dict of outputs like logits, loss, etc.

generate(token_ids, **kwargs)

Generation method for all generative models. Generative models have the is_generative attribute set to True. The behavior of this method is usually controlled by generation part of the model’s config.

Parameters:

• model_inputs – Model inputs for generation, usually the same as forward’s model_inputs

• **kwargs – Generation kwargs

Returns:

Generated output tensor

is_generative: bool = True

loss_func_name: str | LossType = 'cross_entropy'

post_process(generated_ids: Tensor)

Process model outputs and return human-readable results. Called in self.predict()

Parameters:

• model_outputs – model outputs to process

• **kwargs – extra arguments specific to the derived class

Returns:

Processed model output values and converted to human-readable results

preprocess(texts: str | List[str], **kwargs)

Given raw inputs, preprocess the inputs and prepare them for model’s forward().

Parameters:

• raw_inputs – Raw model inputs

• **kwargs – Extra kwargs specific to the model. See the model’s specific class for more info

Returns:

A dict of inputs for model forward

required_backends: List[Backends | str] = [Backends.TRANSFORMERS, Backends.TOKENIZERS]