How to create a config.json after saving a model

BramVanroy · June 16, 2022, 9:03am

What is your use-case that you are using Transformers but not Transformers models? If you want to use the HF Trainer alongside with your own PyTorch model, I recommended to subclass the relevant classes, similar to PretrainedModel

github.com

huggingface/transformers/blob/3981ee8650042e89d9c430ec34def2d58a2a12f7/src/transformers/modeling_utils.py#L955

      
        
                        exclude_embeddings (`bool`, *optional*, defaults to `True`):
                            Whether or not to count embedding and softmax operations.
            
            
        Returns:
                        `int`: The number of floating-point operations.
                    """
            
            
        return 6 * self.estimate_tokens(input_dict) * self.num_parameters(exclude_embeddings=exclude_embeddings)
            
            

            
class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMixin):
                r"""
                Base class for all models.
            
            
    [`PreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
                downloading and saving models as well as a few methods common to all models to:
            
            
        - resize the input embeddings,
                    - prune heads in the self-attention heads.
            
            
    Class attributes (overridden by derived classes):

And to use your own PretrainedConfig alongside of it.

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How to create a config.json after saving a model

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