def to_dict()

in src/setfit/model_card.py [0:0]

• 63 lines of code
• 21 McCabe index (conditional complexity)

    def to_dict(self) -> Dict[str, Any]:
        super_dict = {field.name: getattr(self, field.name) for field in fields(self)}

        # Compute required formats from the raw data
        if self.eval_results_dict:
            dataset_split = list(self.eval_results_dict.keys())[0].split("_")[0]
            dataset_id = self.dataset_id or "unknown"
            dataset_name = self.dataset_name or self.dataset_id or "Unknown"
            eval_results = [
                EvalResult(
                    task_type="text-classification",
                    dataset_type=dataset_id,
                    dataset_name=dataset_name,
                    dataset_split=dataset_split,
                    dataset_revision=self.dataset_revision,
                    metric_type=metric_key.split("_", maxsplit=1)[1],
                    metric_value=metric_value,
                    task_name="Text Classification",
                    metric_name=metric_key.split("_", maxsplit=1)[1].title(),
                )
                for metric_key, metric_value in self.eval_results_dict.items()
            ]
            super_dict["metrics"] = [metric_key.split("_", maxsplit=1)[1] for metric_key in self.eval_results_dict]
            super_dict["model-index"] = eval_results_to_model_index(self.model_name, eval_results)
        eval_lines_list = [
            {
                key: f"**{self._maybe_round(value)}**" if line["Step"] == self.best_model_step else value
                for key, value in line.items()
            }
            for line in self.eval_lines_list
        ]
        super_dict["eval_lines"] = make_markdown_table(eval_lines_list)
        super_dict["explain_bold_in_eval"] = "**" in super_dict["eval_lines"]
        # Replace |:---:| with |:---| for left alignment
        super_dict["label_examples"] = make_markdown_table(self.label_example_list).replace("-:|", "--|")
        super_dict["train_set_metrics"] = make_markdown_table(self.train_set_metrics_list).replace("-:|", "--|")
        super_dict["train_set_sentences_per_label_list"] = make_markdown_table(
            self.train_set_sentences_per_label_list
        ).replace("-:|", "--|")
        super_dict["metrics_table"] = make_markdown_table(self.metric_lines).replace("-:|", "--|")
        if self.code_carbon_callback and self.code_carbon_callback.tracker:
            emissions_data = self.code_carbon_callback.tracker._prepare_emissions_data()
            super_dict["co2_eq_emissions"] = {
                # * 1000 to convert kg to g
                "emissions": float(emissions_data.emissions) * 1000,
                "source": "codecarbon",
                "training_type": "fine-tuning",
                "on_cloud": emissions_data.on_cloud == "Y",
                "cpu_model": emissions_data.cpu_model,
                "ram_total_size": emissions_data.ram_total_size,
                "hours_used": round(emissions_data.duration / 3600, 3),
            }
            if emissions_data.gpu_model:
                super_dict["co2_eq_emissions"]["hardware_used"] = emissions_data.gpu_model
        if self.dataset_id:
            super_dict["datasets"] = [self.dataset_id]
        if self.st_id:
            super_dict["base_model"] = self.st_id
        super_dict["model_max_length"] = self.model.model_body.get_max_seq_length()
        if super_dict["num_classes"] is None:
            if self.model.labels:
                super_dict["num_classes"] = len(self.model.labels)
        if super_dict["absa"]:
            super_dict.update(super_dict.pop("absa"))

        for key in IGNORED_FIELDS:
            super_dict.pop(key, None)
        return super_dict