examples/tensorflow/token-classification/run_ner.py/get_labels
def main():
    # region Argument Parsing
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_ner", model_args, data_args, framework="tensorflow")
    # endregion

    # region Setup logging
    # we only want one process per machine to log things on the screen.
    # accelerator.is_local_main_process is only True for one process per machine.
    logger.setLevel(logging.INFO)
    datasets.utils.logging.set_verbosity_warning()
    transformers.utils.logging.set_verbosity_info()

    # If passed along, set the training seed now.
    if training_args.seed is not None:
        set_seed(training_args.seed)
    # endregion

    # region Loading datasets
    # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
    # or just provide the name of one of the public datasets for token classification task available on the hub at https://huggingface.co/datasets/
    # (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use the column called 'tokens' or the first column if no column called
    # 'tokens' is found. You can easily tweak this behavior (see below).
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        data_files = {}
        if data_args.train_file is not None:
            data_files["train"] = data_args.train_file
        if data_args.validation_file is not None:
            data_files["validation"] = data_args.validation_file
        extension = data_args.train_file.split(".")[-1]
        raw_datasets = load_dataset(
            extension,
            data_files=data_files,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    if raw_datasets["train"] is not None:
        column_names = raw_datasets["train"].column_names
        features = raw_datasets["train"].features
    else:
        column_names = raw_datasets["validation"].column_names
        features = raw_datasets["validation"].features

    if data_args.text_column_name is not None:
        text_column_name = data_args.text_column_name
    elif "tokens" in column_names:
        text_column_name = "tokens"
    else:
        text_column_name = column_names[0]

    if data_args.label_column_name is not None:
        label_column_name = data_args.label_column_name
    elif f"{data_args.task_name}_tags" in column_names:
        label_column_name = f"{data_args.task_name}_tags"
    else:
        label_column_name = column_names[1]

    # In the event the labels are not a `Sequence[ClassLabel]`, we will need to go through the dataset to get the
    # unique labels.
    def get_label_list(labels):
        unique_labels = set()
        for label in labels:
            unique_labels = unique_labels | set(label)
        label_list = list(unique_labels)
        label_list.sort()
        return label_list

    if isinstance(features[label_column_name].feature, ClassLabel):
        label_list = features[label_column_name].feature.names
        # No need to convert the labels since they are already ints.
        label_to_id = {i: i for i in range(len(label_list))}
    else:
        label_list = get_label_list(raw_datasets["train"][label_column_name])
        label_to_id = {l: i for i, l in enumerate(label_list)}
    num_labels = len(label_list)
    # endregion

    # region Load config and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, num_labels=num_labels)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, num_labels=num_labels)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

    tokenizer_name_or_path = model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path
    if not tokenizer_name_or_path:
        raise ValueError(
            "You are instantiating a new tokenizer from scratch. This is not supported by this script."
            "You can do it from another script, save it, and load it from here, using --tokenizer_name."
        )

    if config.model_type in {"gpt2", "roberta"}:
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True, add_prefix_space=True)
    else:
        tokenizer = AutoTokenizer.from_pretrained(tokenizer_name_or_path, use_fast=True)
    # endregion

    # region Preprocessing the raw datasets
    # First we tokenize all the texts.
    padding = "max_length" if data_args.pad_to_max_length else False

    # Tokenize all texts and align the labels with them.

    def tokenize_and_align_labels(examples):
        tokenized_inputs = tokenizer(
            examples[text_column_name],
            max_length=data_args.max_length,
            padding=padding,
            truncation=True,
            # We use this argument because the texts in our dataset are lists of words (with a label for each word).
            is_split_into_words=True,
        )

        labels = []
        for i, label in enumerate(examples[label_column_name]):
            word_ids = tokenized_inputs.word_ids(batch_index=i)
            previous_word_idx = None
            label_ids = []
            for word_idx in word_ids:
                # Special tokens have a word id that is None. We set the label to -100 so they are automatically
                # ignored in the loss function.
                if word_idx is None:
                    label_ids.append(-100)
                # We set the label for the first token of each word.
                elif word_idx != previous_word_idx:
                    label_ids.append(label_to_id[label[word_idx]])
                # For the other tokens in a word, we set the label to either the current label or -100, depending on
                # the label_all_tokens flag.
                else:
                    label_ids.append(label_to_id[label[word_idx]] if data_args.label_all_tokens else -100)
                previous_word_idx = word_idx

            labels.append(label_ids)
        tokenized_inputs["labels"] = labels
        return tokenized_inputs

    processed_raw_datasets = raw_datasets.map(
        tokenize_and_align_labels,
        batched=True,
        remove_columns=raw_datasets["train"].column_names,
        desc="Running tokenizer on dataset",
    )

    train_dataset = processed_raw_datasets["train"]
    eval_dataset = processed_raw_datasets["validation"]

    if data_args.max_train_samples is not None:
        max_train_samples = min(len(train_dataset), data_args.max_train_samples)
        train_dataset = train_dataset.select(range(max_train_samples))

    if data_args.max_eval_samples is not None:
        max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
        eval_dataset = eval_dataset.select(range(max_eval_samples))

    # Log a few random samples from the training set:
    for index in random.sample(range(len(train_dataset)), 3):
        logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
    # endregion

    with training_args.strategy.scope():
        # region Initialize model
        if model_args.model_name_or_path:
            model = TFAutoModelForTokenClassification.from_pretrained(
                model_args.model_name_or_path,
                config=config,
            )
        else:
            logger.info("Training new model from scratch")
            model = TFAutoModelForTokenClassification.from_config(config)

        # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
        # on a small vocab and want a smaller embedding size, remove this test.
        embeddings = model.get_input_embeddings()

        # Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings.
        #       As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and
        #       the weights will always be in embeddings.embeddings.
        if hasattr(embeddings, "embeddings"):
            embedding_size = embeddings.embeddings.shape[0]
        else:
            embedding_size = embeddings.weight.shape[0]
        if len(tokenizer) > embedding_size:
            model.resize_token_embeddings(len(tokenizer))
        # endregion

        # region Create TF datasets

        # We need the DataCollatorForTokenClassification here, as we need to correctly pad labels as
        # well as inputs.
        collate_fn = DataCollatorForTokenClassification(tokenizer=tokenizer, return_tensors="np")
        num_replicas = training_args.strategy.num_replicas_in_sync
        total_train_batch_size = training_args.per_device_train_batch_size * num_replicas

        dataset_options = tf.data.Options()
        dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF

        # model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
        # training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
        # use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
        # yourself if you use this method, whereas they are automatically inferred from the model input names when
        # using model.prepare_tf_dataset()
        # For more info see the docs:
        # https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
        # https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset

        tf_train_dataset = model.prepare_tf_dataset(
            train_dataset,
            collate_fn=collate_fn,
            batch_size=total_train_batch_size,
            shuffle=True,
        ).with_options(dataset_options)
        total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
        tf_eval_dataset = model.prepare_tf_dataset(
            eval_dataset,
            collate_fn=collate_fn,
            batch_size=total_eval_batch_size,
            shuffle=False,
        ).with_options(dataset_options)

        # endregion

        # region Optimizer, loss and compilation
        num_train_steps = int(len(tf_train_dataset) * training_args.num_train_epochs)
        if training_args.warmup_steps > 0:
            num_warmup_steps = training_args.warmup_steps
        elif training_args.warmup_ratio > 0:
            num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
        else:
            num_warmup_steps = 0

        optimizer, lr_schedule = create_optimizer(
            init_lr=training_args.learning_rate,
            num_train_steps=num_train_steps,
            num_warmup_steps=num_warmup_steps,
            adam_beta1=training_args.adam_beta1,
            adam_beta2=training_args.adam_beta2,
            adam_epsilon=training_args.adam_epsilon,
            weight_decay_rate=training_args.weight_decay,
            adam_global_clipnorm=training_args.max_grad_norm,
        )

        model.compile(optimizer=optimizer, jit_compile=training_args.xla)
        # endregion

        # Metrics
        metric = evaluate.load("seqeval")

        def get_labels(y_pred, y_true):
            # Transform predictions and references tensos to numpy arrays

            # Remove ignored index (special tokens)
            true_predictions = [
                [label_list[p] for (p, l) in zip(pred, gold_label) if l != -100]
                for pred, gold_label in zip(y_pred, y_true)
            ]
            true_labels = [
                [label_list[l] for (p, l) in zip(pred, gold_label) if l != -100]
                for pred, gold_label in zip(y_pred, y_true)
            ]
            return true_predictions, true_labels

        def compute_metrics():
            results = metric.compute()
            if data_args.return_entity_level_metrics:
                # Unpack nested dictionaries
                final_results = {}
                for key, value in results.items():
                    if isinstance(value, dict):
                        for n, v in value.items():
                            final_results[f"{key}_{n}"] = v
                    else:
                        final_results[key] = value
                return final_results
            else:
                return {
                    "precision": results["overall_precision"],
                    "recall": results["overall_recall"],
                    "f1": results["overall_f1"],
                    "accuracy": results["overall_accuracy"],
                }

        # endregion

        # region Preparing push_to_hub and model card
        push_to_hub_model_id = training_args.push_to_hub_model_id
        model_name = model_args.model_name_or_path.split("/")[-1]
        if not push_to_hub_model_id:
            if data_args.dataset_name is not None:
                push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
            else:
                push_to_hub_model_id = f"{model_name}-finetuned-token-classification"

        model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "token-classification"}
        if data_args.dataset_name is not None:
            model_card_kwargs["dataset_tags"] = data_args.dataset_name
            if data_args.dataset_config_name is not None:
                model_card_kwargs["dataset_args"] = data_args.dataset_config_name
                model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
            else:
                model_card_kwargs["dataset"] = data_args.dataset_name

        if training_args.push_to_hub:
            callbacks = [
                PushToHubCallback(
                    output_dir=training_args.output_dir,
                    hub_model_id=push_to_hub_model_id,
                    hub_token=training_args.push_to_hub_token,
                    tokenizer=tokenizer,
                    **model_card_kwargs,
                )
            ]
        else:
            callbacks = []
        # endregion

        # region Training
        logger.info("***** Running training *****")
        logger.info(f"  Num examples = {len(train_dataset)}")
        logger.info(f"  Num Epochs = {training_args.num_train_epochs}")
        logger.info(f"  Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
        logger.info(f"  Total train batch size = {total_train_batch_size}")
        # Only show the progress bar once on each machine.

        model.fit(
            tf_train_dataset,
            validation_data=tf_eval_dataset,
            epochs=int(training_args.num_train_epochs),
            callbacks=callbacks,
        )
        # endregion

        # region Predictions
        # If you have variable batch sizes (i.e. not using pad_to_max_length), then
        # this bit might fail on TF < 2.8 because TF can't concatenate outputs of varying seq
        # length from predict().

        try:
            predictions = model.predict(tf_eval_dataset, batch_size=training_args.per_device_eval_batch_size)["logits"]
        except tf.python.framework.errors_impl.InvalidArgumentError:
            raise ValueError(
                "Concatenating predictions failed! If your version of TensorFlow is 2.8.0 or older "
                "then you will need to use --pad_to_max_length to generate predictions, as older "
                "versions of TensorFlow cannot concatenate variable-length predictions as RaggedTensor."
            )
        if isinstance(predictions, tf.RaggedTensor):
            predictions = predictions.to_tensor(default_value=-100)
        predictions = tf.math.argmax(predictions, axis=-1).numpy()
        if "label" in eval_dataset:
            labels = eval_dataset.with_format("tf")["label"]
        else:
            labels = eval_dataset.with_format("tf")["labels"]
        if isinstance(labels, tf.RaggedTensor):
            labels = labels.to_tensor(default_value=-100)
        labels = labels.numpy()
        attention_mask = eval_dataset.with_format("tf")["attention_mask"]
        if isinstance(attention_mask, tf.RaggedTensor):
            attention_mask = attention_mask.to_tensor(default_value=-100)
        attention_mask = attention_mask.numpy()
        labels[attention_mask == 0] = -100
        preds, refs = get_labels(predictions, labels)
        metric.add_batch(
            predictions=preds,
            references=refs,
        )
        eval_metric = compute_metrics()
        logger.info("Evaluation metrics:")
        for key, val in eval_metric.items():
            logger.info(f"{key}: {val:.4f}")

        if training_args.output_dir is not None:
            output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
            with open(output_eval_file, "w") as writer:
                writer.write(json.dumps(eval_metric))
        # endregion

    if training_args.output_dir is not None and not training_args.push_to_hub:
        # If we're not pushing to hub, at least save a local copy when we're done
        model.save_pretrained(training_args.output_dir)