def init_training_args(self) -> None:
        """
        This method is used to generate the `spacy` configuration file, which is used to train
        """
        from spacy.cli.init_config import init_config

        # We generate the config with GPU just when we are using `spacy-transformers`,
        # otherwise the default configuration will be messed up for `spacy`.
        self.trainer_kwargs = init_config(
            lang=self.language,
            pipeline=self._spacy_pipeline_components,
            optimize=self.optimize,
            gpu=True,
        )

        self.trainer_kwargs["paths"]["train"] = self._train_dataset_path
        self.trainer_kwargs["paths"]["dev"] = self._eval_dataset_path
        self.trainer_kwargs["system"]["seed"] = self._seed or 42

        # Now we can already set the GPU properties if we want to train/fine-tune a
        # `spacy` model using the GPU, or a `spacy-transformers` model using the CPU.
        self.trainer_kwargs["system"]["gpu_allocator"] = (
            ("pytorch" if self.has_torch else "tensorflow" if self.has_tensorflow else None) if self.use_gpu else None
        )
        self.trainer_kwargs["nlp"]["batch_size"] = 128 if self.use_gpu else 16

        # If we use `spacy-transformers` then we need to set the `transformer` component
        # in the pipeline, and we need to set the `name` of the model to load.
        self.trainer_kwargs["components"]["transformer"]["name"] = self._model
        self.trainer_kwargs["nlp"]["pipeline"] = ["transformer"] + self._spacy_pipeline_components

        if "transformer" in self.trainer_kwargs["nlp"]["pipeline"]:
            # The `transformer` component cannot be frozen, but we can set the `grad_factor`
            # to 0.0 to avoid updating the weights of the `transformer` component. Even though
            # the computation of those weights will be performed, the gradients will be
            # multiplied by 0.0, so the weights will not be updated.
            # self.config["training"]["frozen_components"] = ["transformer"]
            # self.config["training"]["annotating_components"] = ["transformer"]
            if not self.update_transformer:
                self.trainer_kwargs["components"]["transformer"]["grad_factor"] = 0.0

        This method is used to generate the `spacy` configuration file, which is used to train
        
argilla-v1/src/argilla_v1/training/spacy.py