Add TF multiple choice example (#12865)

* Add new multiple-choice example, remove old one

examples/tensorflow/multiple-choice/README.md

 <!---
-Copyright 2020 The HuggingFace Team. All rights reserved.
+Copyright 2021 The HuggingFace Team. All rights reserved.
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -13,26 +13,31 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
 -->
+# Multiple-choice training (e.g. SWAG)
 
-# Multiple Choice
+This folder contains the `run_swag.py` script, showing an examples of *multiple-choice answering* with the 
+🤗 Transformers library. For straightforward use-cases you may be able to use these scripts without modification, 
+although we have also included comments in the code to indicate areas that you may need to adapt to your own projects.
 
-## Fine-tuning on SWAG
+### Multi-GPU and TPU usage
 
+By default, the script uses a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs
+can also be used by passing the name of the TPU resource with the `--tpu` argument.
+
+### Memory usage and data loading
+
+One thing to note is that all data is loaded into memory in this script. Most multiple-choice datasets are small
+enough that this is not an issue, but if you have a very large dataset you will need to modify the script to handle
+data streaming. This is particularly challenging for TPUs, given the stricter requirements and the sheer volume of data
+required to keep them fed. A full explanation of all the possible pitfalls is a bit beyond this example script and 
+README, but for more information you can see the 'Input Datasets' section of 
+[this document](https://www.tensorflow.org/guide/tpu).
+
+### Example command
 ```bash
-export SWAG_DIR=/path/to/swag_data_dir
-python ./examples/multiple-choice/run_tf_multiple_choice.py \
---task_name swag \
---model_name_or_path bert-base-cased \
---do_train \
---do_eval \
---data_dir $SWAG_DIR \
---learning_rate 5e-5 \
---num_train_epochs 3 \
---max_seq_length 80 \
---output_dir models_bert/swag_base \
---per_gpu_eval_batch_size=16 \
---per_device_train_batch_size=16 \
---logging-dir logs \
---gradient_accumulation_steps 2 \
---overwrite_output
+python run_swag.py \
+ --model_name_or_path distilbert-base-cased \
+ --output_dir output \
+ --do_eval \
+ --do_train
 ```

examples/tensorflow/multiple-choice/run_swag.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Fine-tuning the library models for multiple choice.
+"""
+# You can also adapt this script on your own multiple choice task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Optional
+
+import datasets
+import numpy as np
+import tensorflow as tf
+from datasets import load_dataset
+
+import transformers
+from transformers import (
+    CONFIG_NAME,
+    TF2_WEIGHTS_NAME,
+    AutoConfig,
+    AutoTokenizer,
+    HfArgumentParser,
+    TFAutoModelForMultipleChoice,
+    TFTrainingArguments,
+    create_optimizer,
+    set_seed,
+)
+from transformers.utils import check_min_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.10.0.dev0")
+
+logger = logging.getLogger(__name__)
+
+
+# region Helper classes and functions
+class SavePretrainedCallback(tf.keras.callbacks.Callback):
+    # Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
+    # metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
+    # that saves the model with this method after each epoch.
+    def __init__(self, output_dir, **kwargs):
+        super().__init__()
+        self.output_dir = output_dir
+
+    def on_epoch_end(self, epoch, logs=None):
+        self.model.save_pretrained(self.output_dir)
+
+
+def convert_dataset_for_tensorflow(
+    dataset, non_label_column_names, batch_size, dataset_mode="variable_batch", shuffle=True, drop_remainder=True
+):
+    """Converts a Hugging Face dataset to a Tensorflow Dataset. The dataset_mode controls whether we pad all batches
+    to the maximum sequence length, or whether we only pad to the maximum length within that batch. The former
+    is most useful when training on TPU, as a new graph compilation is required for each sequence length.
+    """
+
+    def densify_ragged_batch(features, label=None):
+        features = {
+            feature: ragged_tensor.to_tensor(shape=batch_shape[feature]) for feature, ragged_tensor in features.items()
+        }
+        if label is None:
+            return features
+        else:
+            return features, label
+
+    feature_keys = list(set(dataset.features.keys()) - set(non_label_column_names + ["label"]))
+    if dataset_mode == "variable_batch":
+        batch_shape = {key: None for key in feature_keys}
+        data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
+    elif dataset_mode == "constant_batch":
+        data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
+        batch_shape = {
+            key: tf.concat(([batch_size], ragged_tensor.bounding_shape()[1:]), axis=0)
+            for key, ragged_tensor in data.items()
+        }
+    else:
+        raise ValueError("Unknown dataset mode!")
+
+    if "label" in dataset.features:
+        labels = tf.convert_to_tensor(np.array(dataset["label"]))
+        tf_dataset = tf.data.Dataset.from_tensor_slices((data, labels))
+    else:
+        tf_dataset = tf.data.Dataset.from_tensor_slices(data)
+    if shuffle:
+        tf_dataset = tf_dataset.shuffle(buffer_size=len(dataset))
+    options = tf.data.Options()
+    options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
+    tf_dataset = (
+        tf_dataset.with_options(options)
+        .batch(batch_size=batch_size, drop_remainder=drop_remainder)
+        .map(densify_ragged_batch)
+    )
+    return tf_dataset
+
+
+# endregion
+
+# region Arguments
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_seq_length: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The maximum total input sequence length after tokenization. If passed, sequences longer "
+            "than this will be truncated, sequences shorter will be padded."
+        },
+    )
+    pad_to_max_length: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to pad all samples to the maximum sentence length. "
+            "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
+            "efficient on GPU but very bad for TPU."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+
+    def __post_init__(self):
+        if self.train_file is not None:
+            extension = self.train_file.split(".")[-1]
+            assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+        if self.validation_file is not None:
+            extension = self.validation_file.split(".")[-1]
+            assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
+
+
+# endregion
+
+
+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()
+
+    output_dir = Path(training_args.output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    # 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)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+    # endregion
+
+    # region Checkpoints
+    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
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # region Load 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 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 'text' or the first column if no column called
+    # 'text' 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.train_file is not None or data_args.validation_file is not None:
+        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, cache_dir=model_args.cache_dir)
+    else:
+        # Downloading and loading the swag dataset from the hub.
+        raw_datasets = load_dataset("swag", "regular", cache_dir=model_args.cache_dir)
+    # 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.
+
+    # When using your own dataset or a different dataset from swag, you will probably need to change this.
+    ending_names = [f"ending{i}" for i in range(4)]
+    context_name = "sent1"
+    question_header_name = "sent2"
+    # 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
+
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    # download model & vocab.
+    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,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    # endregion
+
+    # region Dataset preprocessing
+    if data_args.max_seq_length is None:
+        max_seq_length = tokenizer.model_max_length
+        if max_seq_length > 1024:
+            logger.warning(
+                f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
+                "Picking 1024 instead. You can change that default value by passing --max_seq_length xxx."
+            )
+            max_seq_length = 1024
+    else:
+        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)
+
+    def preprocess_function(examples):
+        first_sentences = [[context] * 4 for context in examples[context_name]]
+        question_headers = examples[question_header_name]
+        second_sentences = [
+            [f"{header} {examples[end][i]}" for end in ending_names] for i, header in enumerate(question_headers)
+        ]
+
+        # Flatten out
+        first_sentences = sum(first_sentences, [])
+        second_sentences = sum(second_sentences, [])
+
+        # Tokenize
+        tokenized_examples = tokenizer(first_sentences, second_sentences, truncation=True, max_length=max_seq_length)
+        # Un-flatten
+        data = {k: [v[i : i + 4] for i in range(0, len(v), 4)] for k, v in tokenized_examples.items()}
+        return data
+
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset")
+        train_dataset = raw_datasets["train"]
+        non_label_columns = [feature for feature in train_dataset.features if feature not in ("label", "labels")]
+        if data_args.max_train_samples is not None:
+            train_dataset = train_dataset.select(range(data_args.max_train_samples))
+        with training_args.main_process_first(desc="train dataset map pre-processing"):
+            train_dataset = train_dataset.map(
+                preprocess_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                load_from_cache_file=not data_args.overwrite_cache,
+            )
+
+    if training_args.do_eval:
+        if "validation" not in raw_datasets:
+            raise ValueError("--do_eval requires a validation dataset")
+        eval_dataset = raw_datasets["validation"]
+        if not training_args.do_train:
+            non_label_columns = [feature for feature in eval_dataset.features if feature not in ("label", "labels")]
+        if data_args.max_eval_samples is not None:
+            eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
+        with training_args.main_process_first(desc="validation dataset map pre-processing"):
+            eval_dataset = eval_dataset.map(
+                preprocess_function,
+                batched=True,
+                num_proc=data_args.preprocessing_num_workers,
+                load_from_cache_file=not data_args.overwrite_cache,
+            )
+    # endregion
+
+    with training_args.strategy.scope():
+        # region Build model
+        if checkpoint is None:
+            model_path = model_args.model_name_or_path
+        else:
+            model_path = checkpoint
+        model = TFAutoModelForMultipleChoice.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,
+        )
+
+        num_replicas = training_args.strategy.num_replicas_in_sync
+        total_train_batch_size = training_args.per_device_train_batch_size * num_replicas
+        total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas
+        if training_args.do_train:
+            total_train_steps = (len(train_dataset) // total_train_batch_size) * int(training_args.num_train_epochs)
+            optimizer, lr_schedule = create_optimizer(
+                init_lr=training_args.learning_rate, num_train_steps=int(total_train_steps), num_warmup_steps=0
+            )
+        else:
+            optimizer = "adam"  # Just put anything in here, since we're not using it anyway
+        model.compile(
+            optimizer=optimizer,
+            loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+            metrics=[tf.keras.metrics.SparseCategoricalAccuracy(name="accuracy")],
+        )
+        # endregion
+
+        # region Training
+        if training_args.do_train:
+            tf_train_dataset = convert_dataset_for_tensorflow(
+                train_dataset, non_label_column_names=non_label_columns, batch_size=total_train_batch_size
+            )
+            if training_args.do_eval:
+                validation_data = convert_dataset_for_tensorflow(
+                    eval_dataset, non_label_column_names=non_label_columns, batch_size=total_eval_batch_size
+                )
+            else:
+                validation_data = None
+            model.fit(
+                tf_train_dataset,
+                validation_data=validation_data,
+                epochs=int(training_args.num_train_epochs),
+                callbacks=[SavePretrainedCallback(output_dir=training_args.output_dir)],
+            )
+        # endregion
+
+        # region Evaluation
+        if training_args.do_eval and not training_args.do_train:
+            # Do a standalone evaluation pass
+            tf_eval_dataset = convert_dataset_for_tensorflow(
+                eval_dataset, non_label_column_names=non_label_columns, batch_size=total_eval_batch_size
+            )
+            model.evaluate(tf_eval_dataset)
+        # endregion
+
+        # region Push to hub
+        if training_args.push_to_hub:
+            model.push_to_hub(
+                finetuned_from=model_args.model_name_or_path,
+                tasks="multiple-choice",
+                dataset_tags="swag",
+                dataset_args="regular",
+                dataset="SWAG",
+                language="en",
+            )
+        # endregion
+
+
+if __name__ == "__main__":
+    main()

examples/tensorflow/multiple-choice/run_tf_multiple_choice.py

-#!/usr/bin/env python
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
-# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-""" Finetuning the library models for multiple choice (Bert, Roberta, XLNet)."""
-
-
-import logging
-import os
-from dataclasses import dataclass, field
-from typing import Dict, Optional
-
-import numpy as np
-
-from transformers import (
-    AutoConfig,
-    AutoTokenizer,
-    EvalPrediction,
-    HfArgumentParser,
-    TFAutoModelForMultipleChoice,
-    TFTrainer,
-    TFTrainingArguments,
-    set_seed,
-)
-from transformers.utils import logging as hf_logging
-from utils_multiple_choice import Split, TFMultipleChoiceDataset, processors
-
-
-hf_logging.set_verbosity_info()
-hf_logging.enable_default_handler()
-hf_logging.enable_explicit_format()
-
-
-logger = logging.getLogger(__name__)
-
-
-def simple_accuracy(preds, labels):
-    return (preds == labels).mean()
-
-
-@dataclass
-class ModelArguments:
-    """
-    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
-    """
-
-    model_name_or_path: str = field(
-        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
-    )
-    config_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
-    )
-    tokenizer_name: Optional[str] = field(
-        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
-    )
-    cache_dir: Optional[str] = field(
-        default=None,
-        metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
-    )
-
-
-@dataclass
-class DataTrainingArguments:
-    """
-    Arguments pertaining to what data we are going to input our model for training and eval.
-    """
-
-    task_name: str = field(metadata={"help": "The name of the task to train on: " + ", ".join(processors.keys())})
-    data_dir: str = field(metadata={"help": "Should contain the data files for the task."})
-    max_seq_length: int = field(
-        default=128,
-        metadata={
-            "help": "The maximum total input sequence length after tokenization. Sequences longer "
-            "than this will be truncated, sequences shorter will be padded."
-        },
-    )
-    overwrite_cache: bool = field(
-        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
-    )
-
-
-def main():
-    # 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))
-    model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
-    if (
-        os.path.exists(training_args.output_dir)
-        and os.listdir(training_args.output_dir)
-        and training_args.do_train
-        and not training_args.overwrite_output_dir
-    ):
-        raise ValueError(
-            f"Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome."
-        )
-
-    # Setup logging
-    logging.basicConfig(
-        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
-        datefmt="%m/%d/%Y %H:%M:%S",
-        level=logging.INFO,
-    )
-    logger.warning(
-        f"device: {training_args.device}, n_replicas: {training_args.n_replicas}, "
-        f"16-bits training: {training_args.fp16}"
-    )
-    logger.info(f"Training/evaluation parameters {training_args}")
-
-    # Set seed
-    set_seed(training_args.seed)
-
-    try:
-        processor = processors[data_args.task_name]()
-        label_list = processor.get_labels()
-        num_labels = len(label_list)
-    except KeyError:
-        raise ValueError(f"Task not found: {data_args.task_name}")
-
-    # Load pretrained model and tokenizer
-    #
-    # Distributed training:
-    # The .from_pretrained methods guarantee that only one local process can concurrently
-    # download model & vocab.
-    config = AutoConfig.from_pretrained(
-        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
-        num_labels=num_labels,
-        finetuning_task=data_args.task_name,
-        cache_dir=model_args.cache_dir,
-    )
-    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,
-    )
-    with training_args.strategy.scope():
-        model = TFAutoModelForMultipleChoice.from_pretrained(
-            model_args.model_name_or_path,
-            from_pt=bool(".bin" in model_args.model_name_or_path),
-            config=config,
-            cache_dir=model_args.cache_dir,
-        )
-    # Get datasets
-    train_dataset = (
-        TFMultipleChoiceDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-            mode=Split.train,
-        )
-        if training_args.do_train
-        else None
-    )
-    eval_dataset = (
-        TFMultipleChoiceDataset(
-            data_dir=data_args.data_dir,
-            tokenizer=tokenizer,
-            task=data_args.task_name,
-            max_seq_length=data_args.max_seq_length,
-            overwrite_cache=data_args.overwrite_cache,
-            mode=Split.dev,
-        )
-        if training_args.do_eval
-        else None
-    )
-
-    def compute_metrics(p: EvalPrediction) -> Dict:
-        preds = np.argmax(p.predictions, axis=1)
-        return {"acc": simple_accuracy(preds, p.label_ids)}
-
-    # Initialize our Trainer
-    trainer = TFTrainer(
-        model=model,
-        args=training_args,
-        train_dataset=train_dataset.get_dataset() if train_dataset else None,
-        eval_dataset=eval_dataset.get_dataset() if eval_dataset else None,
-        compute_metrics=compute_metrics,
-    )
-
-    # Training
-    if training_args.do_train:
-        trainer.train()
-        trainer.save_model()
-        tokenizer.save_pretrained(training_args.output_dir)
-    # Evaluation
-    results = {}
-    if training_args.do_eval:
-        logger.info("*** Evaluate ***")
-
-        result = trainer.evaluate()
-
-        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
-        with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results *****")
-            for key, value in result.items():
-                logger.info(f"  {key} = {value}")
-                writer.write(f"{key} = {value}\n")
-
-            results.update(result)
-
-    return results
-
-
-if __name__ == "__main__":
-    main()