How to use the simpletransformers.ner.ner_utils.InputExample function in simpletransformers

to_predict: A python list of text (str) to be sent to the model for prediction.

        Returns:
            preds: A Python list of lists with dicts containg each word mapped to its NER tag.
            model_outputs: A python list of the raw model outputs for each text.
        """

        tokenizer = self.tokenizer
        device = self.device
        model = self.model
        args = self.args
        pad_token_label_id = self.pad_token_label_id

        self._move_model_to_device()

        predict_examples = [InputExample(i, sentence.split(), ["O" for word in sentence.split()]) for i, sentence in enumerate(to_predict)]

        eval_dataset = self.load_and_cache_examples(None, to_predict=predict_examples)

        eval_sampler = SequentialSampler(eval_dataset)
        eval_dataloader = DataLoader(eval_dataset, sampler=eval_sampler, batch_size=args["eval_batch_size"])

        eval_loss = 0.0
        nb_eval_steps = 0
        preds = None
        out_label_ids = None
        model.eval()

        for batch in tqdm(eval_dataloader, disable=args['silent']):
            batch = tuple(t.to(device) for t in batch)

            with torch.no_grad():

def read_examples_from_file(data_file, mode):
    file_path = data_file
    guid_index = 1
    examples = []
    with open(file_path, encoding="utf-8") as f:
        words = []
        labels = []
        for line in f:
            if line.startswith("-DOCSTART-") or line == "" or line == "\n":
                if words:
                    examples.append(InputExample(guid="{}-{}".format(mode, guid_index),
                                                 words=words,
                                                 labels=labels))
                    guid_index += 1
                    words = []
                    labels = []
            else:
                splits = line.split(" ")
                words.append(splits[0])
                if len(splits) > 1:
                    labels.append(splits[-1].replace("\n", ""))
                else:
                    # Examples could have no label for mode = "test"
                    labels.append("O")
        if words:
            examples.append(InputExample(guid="%s-%d".format(mode, guid_index),
                                         words=words,

def get_examples_from_df(data):
    return [InputExample(guid=sentence_id, words=sentence_df['words'].tolist(), labels=sentence_df['labels'].tolist()) for sentence_id, sentence_df in data.groupby(['sentence_id'])]