examples/tensorflow/text-classification/run_text_classification.py/main
def main():
    # region Argument parsing
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        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_text_classification", model_args, data_args, framework="tensorflow")

    output_dir = Path(training_args.output_dir)
    output_dir.mkdir(parents=True, exist_ok=True)
    # endregion

    # region Checkpoints
    # Detecting last checkpoint.
    checkpoint = None
    if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
        if (output_dir / CONFIG_NAME).is_file() and (output_dir / TF2_WEIGHTS_NAME).is_file():
            checkpoint = output_dir
            logger.info(
                f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
                " behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )
        else:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to continue regardless."
            )

    # endregion

    # region Logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO)

    logger.info(f"Training/evaluation parameters {training_args}")
    # endregion

    # region Loading data
    # For CSV/JSON files, this script will use the 'label' field as the label and the 'sentence1' and optionally
    # 'sentence2' fields as inputs if they exist. If not, the first two fields not named label are used if at least two
    # columns are provided. Note that the term 'sentence' can be slightly misleading, as they often contain more than
    # a single grammatical sentence, when the task requires it.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. 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.
    data_files = {"train": data_args.train_file, "validation": data_args.validation_file, "test": data_args.test_file}
    data_files = {key: file for key, file in data_files.items() if file is not None}

    for key in data_files.keys():
        logger.info(f"Loading a local file for {key}: {data_files[key]}")

    if data_args.input_file_extension == "csv":
        # Loading a dataset from local csv files
        datasets = load_dataset(
            "csv",
            data_files=data_files,
            cache_dir=model_args.cache_dir,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        # Loading a dataset from local json files
        datasets = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir)
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.
    # endregion

    # region Label preprocessing
    # If you've passed us a training set, we try to infer your labels from it
    if "train" in datasets:
        # By default we assume that if your label column looks like a float then you're doing regression,
        # and if not then you're doing classification. This is something you may want to change!
        is_regression = datasets["train"].features["label"].dtype in ["float32", "float64"]
        if is_regression:
            num_labels = 1
        else:
            # A useful fast method:
            # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
            label_list = datasets["train"].unique("label")
            label_list.sort()  # Let's sort it for determinism
            num_labels = len(label_list)
    # If you haven't passed a training set, we read label info from the saved model (this happens later)
    else:
        num_labels = None
        label_list = None
        is_regression = None
    # endregion

    # region Load model config and tokenizer
    if checkpoint is not None:
        config_path = training_args.output_dir
    elif model_args.config_name:
        config_path = model_args.config_name
    else:
        config_path = model_args.model_name_or_path
    if num_labels is not None:
        config = AutoConfig.from_pretrained(
            config_path,
            num_labels=num_labels,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        config = AutoConfig.from_pretrained(
            config_path,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    # endregion

    # region Dataset preprocessing
    # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
    column_names = {col for cols in datasets.column_names.values() for col in cols}
    non_label_column_names = [name for name in column_names if name != "label"]
    if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
        sentence1_key, sentence2_key = "sentence1", "sentence2"
    elif "sentence1" in non_label_column_names:
        sentence1_key, sentence2_key = "sentence1", None
    else:
        if len(non_label_column_names) >= 2:
            sentence1_key, sentence2_key = non_label_column_names[:2]
        else:
            sentence1_key, sentence2_key = non_label_column_names[0], None

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    # Ensure that our labels match the model's, if it has some pre-specified
    if "train" in datasets:
        if not is_regression and config.label2id != PretrainedConfig(num_labels=num_labels).label2id:
            label_name_to_id = config.label2id
            if sorted(label_name_to_id.keys()) == sorted(label_list):
                label_to_id = label_name_to_id  # Use the model's labels
            else:
                logger.warning(
                    "Your model seems to have been trained with labels, but they don't match the dataset: ",
                    f"model labels: {sorted(label_name_to_id.keys())}, dataset labels:"
                    f" {sorted(label_list)}.\nIgnoring the model labels as a result.",
                )
                label_to_id = {v: i for i, v in enumerate(label_list)}
        elif not is_regression:
            label_to_id = {v: i for i, v in enumerate(label_list)}
        else:
            label_to_id = None
        # Now we've established our label2id, let's overwrite the model config with it.
        config.label2id = label_to_id
        if config.label2id is not None:
            config.id2label = {id: label for label, id in label_to_id.items()}
        else:
            config.id2label = None
    else:
        label_to_id = config.label2id  # Just load the data from the model

    if "validation" in datasets and config.label2id is not None:
        validation_label_list = datasets["validation"].unique("label")
        for val_label in validation_label_list:
            assert val_label in label_to_id, f"Label {val_label} is in the validation set but not the training set!"

    def preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(*args, max_length=max_seq_length, truncation=True)

        # Map labels to IDs
        if config.label2id is not None and "label" in examples:
            result["label"] = [(config.label2id[l] if l != -1 else -1) for l in examples["label"]]
        return result

    datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache)

    # endregion

    with training_args.strategy.scope():
        # region Load pretrained model
        # Set seed before initializing model
        set_seed(training_args.seed)
        #
        # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
        # download model & vocab.
        if checkpoint is None:
            model_path = model_args.model_name_or_path
        else:
            model_path = checkpoint
        model = TFAutoModelForSequenceClassification.from_pretrained(
            model_path,
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
        # endregion

        # region Convert data to a tf.data.Dataset
        dataset_options = tf.data.Options()
        dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
        num_replicas = training_args.strategy.num_replicas_in_sync

        tf_data = {}
        max_samples = {
            "train": data_args.max_train_samples,
            "validation": data_args.max_val_samples,
            "test": data_args.max_test_samples,
        }
        for key in ("train", "validation", "test"):
            if key not in datasets:
                tf_data[key] = None
                continue
            if (
                (key == "train" and not training_args.do_train)
                or (key == "validation" and not training_args.do_eval)
                or (key == "test" and not training_args.do_predict)
            ):
                tf_data[key] = None
                continue
            if key in ("train", "validation"):
                assert "label" in datasets[key].features, f"Missing labels from {key} data!"
            if key == "train":
                shuffle = True
                batch_size = training_args.per_device_train_batch_size * num_replicas
            else:
                shuffle = False
                batch_size = training_args.per_device_eval_batch_size * num_replicas
            samples_limit = max_samples[key]
            dataset = datasets[key]
            if samples_limit is not None:
                dataset = dataset.select(range(samples_limit))

            # 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

            data = model.prepare_tf_dataset(
                dataset,
                shuffle=shuffle,
                batch_size=batch_size,
                tokenizer=tokenizer,
            )
            data = data.with_options(dataset_options)
            tf_data[key] = data
        # endregion

        # region Optimizer, loss and compilation

        if training_args.do_train:
            num_train_steps = len(tf_data["train"]) * 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, 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,
            )
        else:
            optimizer = None
        if is_regression:
            metrics = []
        else:
            metrics = ["accuracy"]
        model.compile(optimizer=optimizer, metrics=metrics)
        # 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:
            push_to_hub_model_id = f"{model_name}-finetuned-text-classification"

        model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}

        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 and validation
        if tf_data["train"] is not None:
            model.fit(
                tf_data["train"],
                validation_data=tf_data["validation"],
                epochs=int(training_args.num_train_epochs),
                callbacks=callbacks,
            )
        if tf_data["validation"] is not None:
            logger.info("Computing metrics on validation data...")
            if is_regression:
                loss = model.evaluate(tf_data["validation"])
                logger.info(f"Eval loss: {loss:.5f}")
            else:
                loss, accuracy = model.evaluate(tf_data["validation"])
                logger.info(f"Eval loss: {loss:.5f}, Eval accuracy: {accuracy * 100:.4f}%")
            if training_args.output_dir is not None:
                output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
                eval_dict = {"eval_loss": loss}
                if not is_regression:
                    eval_dict["eval_accuracy"] = accuracy
                with open(output_eval_file, "w") as writer:
                    writer.write(json.dumps(eval_dict))
        # endregion

        # region Prediction
        if tf_data["test"] is not None:
            logger.info("Doing predictions on test dataset...")
            predictions = model.predict(tf_data["test"])["logits"]
            predicted_class = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
            output_test_file = os.path.join(training_args.output_dir, "test_results.txt")
            with open(output_test_file, "w") as writer:
                writer.write("index\tprediction\n")
                for index, item in enumerate(predicted_class):
                    if is_regression:
                        writer.write(f"{index}\t{item:3.3f}\n")
                    else:
                        item = config.id2label[item]
                        writer.write(f"{index}\t{item}\n")
            logger.info(f"Wrote predictions to {output_test_file}!")
        # 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)