def evaluate(self, eval_file: str, **judge_kwargs: Any) -> pd.DataFrame:
        """
        Evaluate model predictions on the CharXiv dataset.

        Args:
            eval_file: Path to the file containing model predictions
            **judge_kwargs: Additional arguments for the judge model

        Returns:
            DataFrame with evaluation scores by category
        """
        # Set up judge model
        if "LOCAL_LLM" in os.environ:
            judge_model = os.path.basename(os.environ.get("LOCAL_LLM"))
        else:
            judge_model = judge_kwargs.get("model", "gpt-4o-mini")

        if judge_model != "gpt-4o-mini":
            warnings.warn(
                f"The judge_model '{judge_model}' is not gpt-4o-mini. Evaluation results may not be accurate."
            )

        judge_model = build_judge(**judge_kwargs)
        judge_model_name = judge_model.model

        # Define file paths
        suffix = eval_file.split(".")[-1]
        result_file = eval_file.replace(f".{suffix}", f"_{judge_model_name}.xlsx")
        temp_result_file = eval_file.replace(f".{suffix}", f"_{judge_model_name}.pkl")
        score_file = result_file.replace(".xlsx", "_score.csv")

        # Return existing results if available
        if os.path.exists(result_file):
            score = self.get_scores(result_file)
            file.dump(score, score_file)
            return score

        data = file.load(eval_file)
        if "score" not in data.columns:
            data["score"] = 0
        if "extract_answer" not in data.columns:
            data["extract_answer"] = ""

        # Load intermediate results if available
        processed_results = {}
        if os.path.exists(temp_result_file):
            processed_results = file.load(temp_result_file)

        # Identify unprocessed indices
        indices = [i for i in range(len(data)) if i not in processed_results]
        tups = [(judge_model, data.iloc[i]) for i in range(len(data))]

        # Process remaining examples
        nproc = judge_kwargs.pop("nproc", 4)
        if len(indices):
            utils.track_progress_rich(
                auxeval,
                tups,
                nproc=nproc,
                chunksize=nproc,
                keys=indices,
                save=temp_result_file,
                **judge_kwargs,
            )
            processed_results = file.load(temp_result_file)

        # Update data with evaluation results
        data["score"] = data.apply(lambda x: processed_results[x.name]["score"], axis=1)
        data["extract_answer"] = data.apply(
            lambda x: processed_results[x.name]["extract_answer"], axis=1
        )

        # Save results and return scores
        file.dump(data, result_file)
        score = self.get_scores(result_file)
        file.dump(score, score_file)
        return score

        Evaluate model predictions on the CharXiv dataset.

        Args:
            eval_file: Path to the file containing model predictions
            **judge_kwargs: Additional arguments for the judge model

        Returns:
            DataFrame with evaluation scores by category
        
vlmeval/dataset/charxiv.py