Upload run_classification.py

run_classification.py

@@ -0,0 +1,763 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2020 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.
+""" Finetuning the library models for text classification."""
+# You can also adapt this script on your own text classification task. Pointers for this are left as comments.
+
+import logging
+import os
+import random
+import sys
+import warnings
+from dataclasses import dataclass, field
+from typing import List, Optional
+
+import datasets
+import evaluate
+import numpy as np
+from datasets import Value, load_dataset
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+    DataCollatorWithPadding,
+    EvalPrediction,
+    HfArgumentParser,
+    Trainer,
+    TrainingArguments,
+    default_data_collator,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint
+from transformers.utils import check_min_version, send_example_telemetry
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+# check_min_version("4.38.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
+
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+
+    Using `HfArgumentParser` we can turn this class
+    into argparse arguments to be able to specify them on
+    the command line.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    do_regression: bool = field(
+        default=None,
+        metadata={
+            "help": "Whether to do regression instead of classification. If None, will be inferred from the dataset."
+        },
+    )
+    text_column_names: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The name of the text column in the input dataset or a CSV/JSON file. "
+                'If not specified, will use the "sentence" column for single/multi-label classification task.'
+            )
+        },
+    )
+    text_column_delimiter: Optional[str] = field(
+        default=" ", metadata={"help": "THe delimiter to use to join text columns into a single sentence."}
+    )
+    train_split_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": 'The name of the train split in the input dataset. If not specified, will use the "train" split when do_train is enabled'
+        },
+    )
+    validation_split_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": 'The name of the validation split in the input dataset. If not specified, will use the "validation" split when do_eval is enabled'
+        },
+    )
+    test_split_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": 'The name of the test split in the input dataset. If not specified, will use the "test" split when do_predict is enabled'
+        },
+    )
+    remove_splits: Optional[str] = field(
+        default=None,
+        metadata={"help": "The splits to remove from the dataset. Multiple splits should be separated by commas."},
+    )
+    remove_columns: Optional[str] = field(
+        default=None,
+        metadata={"help": "The columns to remove from the dataset. Multiple columns should be separated by commas."},
+    )
+    label_column_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": (
+                "The name of the label column in the input dataset or a CSV/JSON file. "
+                'If not specified, will use the "label" column for single/multi-label classification 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 preprocessed datasets or not."}
+    )
+    pad_to_max_length: bool = field(
+        default=True,
+        metadata={
+            "help": (
+                "Whether to pad all samples to `max_seq_length`. "
+                "If False, will pad the samples dynamically when batching to the maximum length in the batch."
+            )
+        },
+    )
+    shuffle_train_dataset: bool = field(
+        default=False, metadata={"help": "Whether to shuffle the train dataset or not."}
+    )
+    shuffle_seed: int = field(
+        default=42, metadata={"help": "Random seed that will be used to shuffle the train dataset."}
+    )
+    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."
+            )
+        },
+    )
+    max_predict_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": (
+                "For debugging purposes or quicker training, truncate the number of prediction examples to this "
+                "value if set."
+            )
+        },
+    )
+    metric_name: Optional[str] = field(default=None, metadata={"help": "The metric to use for evaluation."})
+    train_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the training data."}
+    )
+    validation_file: Optional[str] = field(
+        default=None, metadata={"help": "A csv or a json file containing the validation data."}
+    )
+    test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
+
+    def __post_init__(self):
+        if self.dataset_name is None:
+            if self.train_file is None or self.validation_file is None:
+                raise ValueError(" training/validation file or a dataset name.")
+
+            train_extension = self.train_file.split(".")[-1]
+            assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
+            validation_extension = self.validation_file.split(".")[-1]
+            assert (
+                validation_extension == train_extension
+            ), "`validation_file` should have the same extension (csv or json) as `train_file`."
+
+
+@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)."},
+    )
+    token: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
+                "generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
+            )
+        },
+    )
+    use_auth_token: bool = field(
+        default=None,
+        metadata={
+            "help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
+        },
+    )
+    trust_remote_code: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
+                "should only be set to `True` for repositories you trust and in which you have read the code, as it will "
+                "execute code present on the Hub on your local machine."
+            )
+        },
+    )
+    ignore_mismatched_sizes: bool = field(
+        default=False,
+        metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
+    )
+
+
+def get_label_list(raw_dataset, split="train") -> List[str]:
+    """Get the list of labels from a multi-label dataset"""
+
+    if isinstance(raw_dataset[split]["label"][0], list):
+        label_list = [label for sample in raw_dataset[split]["label"] for label in sample]
+        label_list = list(set(label_list))
+    else:
+        label_list = raw_dataset[split].unique("label")
+    # we will treat the label list as a list of string instead of int, consistent with model.config.label2id
+    label_list = [str(label) for label in label_list]
+    return label_list
+
+
+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, TrainingArguments))
+    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()
+
+    if model_args.use_auth_token is not None:
+        warnings.warn(
+            "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
+            FutureWarning,
+        )
+        if model_args.token is not None:
+            raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
+        model_args.token = model_args.use_auth_token
+
+    # 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_classification", model_args, data_args)
+
+    # Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+
+    if training_args.should_log:
+        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
+        transformers.utils.logging.set_verbosity_info()
+
+    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()
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
+        + f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Detecting last checkpoint.
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files, or specify a dataset name
+    # to load from huggingface/datasets. In ether case, you can specify a the key of the column(s) containing the text and
+    # the key of the column containing the label. If multiple columns are specified for the text, they will be joined together
+    # for the actual text value.
+    # 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,
+            cache_dir=model_args.cache_dir,
+            token=model_args.token,
+        )
+        # Try print some info about the dataset
+        logger.info(f"Dataset loaded: {raw_datasets}")
+        logger.info(raw_datasets)
+    else:
+        # Loading a dataset from your local files.
+        # CSV/JSON training and evaluation files are needed.
+        data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
+
+        # Get the test dataset: you can provide your own CSV/JSON test file
+        if training_args.do_predict:
+            if data_args.test_file is not None:
+                train_extension = data_args.train_file.split(".")[-1]
+                test_extension = data_args.test_file.split(".")[-1]
+                assert (
+                    test_extension == train_extension
+                ), "`test_file` should have the same extension (csv or json) as `train_file`."
+                data_files["test"] = data_args.test_file
+            else:
+                raise ValueError("Need either a dataset name or a test file for `do_predict`.")
+
+        for key in data_files.keys():
+            logger.info(f"load a local file for {key}: {data_files[key]}")
+
+        if data_args.train_file.endswith(".csv"):
+            # Loading a dataset from local csv files
+            raw_datasets = load_dataset(
+                "csv",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+        else:
+            # Loading a dataset from local json files
+            raw_datasets = load_dataset(
+                "json",
+                data_files=data_files,
+                cache_dir=model_args.cache_dir,
+                token=model_args.token,
+            )
+
+    # See more about loading any type of standard or custom dataset at
+    # https://huggingface.co/docs/datasets/loading_datasets.
+
+    if data_args.remove_splits is not None:
+        for split in data_args.remove_splits.split(","):
+            logger.info(f"removing split {split}")
+            raw_datasets.pop(split)
+
+    if data_args.train_split_name is not None:
+        logger.info(f"using {data_args.train_split_name} as train set")
+        raw_datasets["train"] = raw_datasets[data_args.train_split_name]
+        raw_datasets.pop(data_args.train_split_name)
+
+    if data_args.validation_split_name is not None:
+        logger.info(f"using {data_args.validation_split_name} as validation set")
+        raw_datasets["validation"] = raw_datasets[data_args.validation_split_name]
+        raw_datasets.pop(data_args.validation_split_name)
+
+    if data_args.test_split_name is not None:
+        logger.info(f"using {data_args.test_split_name} as test set")
+        raw_datasets["test"] = raw_datasets[data_args.test_split_name]
+        raw_datasets.pop(data_args.test_split_name)
+
+    if data_args.remove_columns is not None:
+        for split in raw_datasets.keys():
+            for column in data_args.remove_columns.split(","):
+                logger.info(f"removing column {column} from split {split}")
+                raw_datasets[split].remove_columns(column)
+
+    if data_args.label_column_name is not None and data_args.label_column_name != "label":
+        for key in raw_datasets.keys():
+            raw_datasets[key] = raw_datasets[key].rename_column(data_args.label_column_name, "label")
+
+    # Trying to have good defaults here, don't hesitate to tweak to your needs.
+
+    is_regression = (
+        raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
+        if data_args.do_regression is None
+        else data_args.do_regression
+    )
+
+    is_multi_label = False
+    if is_regression:
+        label_list = None
+        num_labels = 1
+        # regession requires float as label type, let's cast it if needed
+        for split in raw_datasets.keys():
+            if raw_datasets[split].features["label"].dtype not in ["float32", "float64"]:
+                logger.warning(
+                    f"Label type for {split} set to float32, was {raw_datasets[split].features['label'].dtype}"
+                )
+                features = raw_datasets[split].features
+                features.update({"label": Value("float32")})
+                try:
+                    raw_datasets[split] = raw_datasets[split].cast(features)
+                except TypeError as error:
+                    logger.error(
+                        f"Unable to cast {split} set to float32, please check the labels are correct, or maybe try with --do_regression=False"
+                    )
+                    raise error
+
+    else:  # classification
+        if raw_datasets["train"].features["label"].dtype == "list":  # multi-label classification
+            is_multi_label = True
+            logger.info("Label type is list, doing multi-label classification")
+        # Trying to find the number of labels in a multi-label classification task
+        # We have to deal with common cases that labels appear in the training set but not in the validation/test set.
+        # So we build the label list from the union of labels in train/val/test.
+        label_list = get_label_list(raw_datasets, split="train")
+        for split in ["validation", "test"]:
+            if split in raw_datasets:
+                val_or_test_labels = get_label_list(raw_datasets, split=split)
+                diff = set(val_or_test_labels).difference(set(label_list))
+                if len(diff) > 0:
+                    # add the labels that appear in val/test but not in train, throw a warning
+                    logger.warning(
+                        f"Labels {diff} in {split} set but not in training set, adding them to the label list"
+                    )
+                    label_list += list(diff)
+        # if label is -1, we throw a warning and remove it from the label list
+        for label in label_list:
+            if label == -1:
+                logger.warning("Label -1 found in label list, removing it.")
+                label_list.remove(label)
+
+        label_list.sort()
+        num_labels = len(label_list)
+        if num_labels <= 1:
+            raise ValueError("You need more than one label to do classification.")
+
+    # Load pretrained model and tokenizer
+    # In 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="text-classification",
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+
+    if is_regression:
+        config.problem_type = "regression"
+        logger.info("setting problem type to regression")
+    elif is_multi_label:
+        config.problem_type = "multi_label_classification"
+        logger.info("setting problem type to multi label classification")
+    else:
+        config.problem_type = "single_label_classification"
+        logger.info("setting problem type to single label classification")
+
+    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,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+    )
+    model = AutoModelForSequenceClassification.from_pretrained(
+        model_args.model_name_or_path,
+        from_tf=bool(".ckpt" in model_args.model_name_or_path),
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        token=model_args.token,
+        trust_remote_code=model_args.trust_remote_code,
+        ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
+    )
+
+    # Padding strategy
+    if data_args.pad_to_max_length:
+        padding = "max_length"
+    else:
+        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
+        padding = False
+
+    # for training ,we will update the config with label infos,
+    # if do_train is not set, we will use the label infos in the config
+    if training_args.do_train and not is_regression:  # classification, training
+        label_to_id = {v: i for i, v in enumerate(label_list)}
+        # update config with label infos
+        if model.config.label2id != label_to_id:
+            logger.warning(
+                "The label2id key in the model config.json is not equal to the label2id key of this "
+                "run. You can ignore this if you are doing finetuning."
+            )
+        model.config.label2id = label_to_id
+        model.config.id2label = {id: label for label, id in label_to_id.items()}
+    elif not is_regression:  # classification, but not training
+        logger.info("using label infos in the model config")
+        logger.info("label2id: {}".format(model.config.label2id))
+        label_to_id = model.config.label2id
+    else:  # regression
+        label_to_id = 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)
+
+    def multi_labels_to_ids(labels: List[str]) -> List[float]:
+        ids = [0.0] * len(label_to_id)  # BCELoss requires float as target type
+        for label in labels:
+            ids[label_to_id[label]] = 1.0
+        return ids
+
+    def preprocess_function(examples):
+        if data_args.text_column_names is not None:
+            text_column_names = data_args.text_column_names.split(",")
+            # join together text columns into "sentence" column
+            examples["sentence"] = examples[text_column_names[0]]
+            for column in text_column_names[1:]:
+                for i in range(len(examples[column])):
+                    examples["sentence"][i] += data_args.text_column_delimiter + examples[column][i]
+        # Tokenize the texts
+        result = tokenizer(examples["sentence"], padding=padding, max_length=max_seq_length, truncation=True)
+        if label_to_id is not None and "label" in examples:
+            if is_multi_label:
+                result["label"] = [multi_labels_to_ids(l) for l in examples["label"]]
+            else:
+                result["label"] = [(label_to_id[str(l)] if l != -1 else -1) for l in examples["label"]]
+        return result
+
+    # Running the preprocessing pipeline on all the datasets
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        raw_datasets = raw_datasets.map(
+            preprocess_function,
+            batched=True,
+            load_from_cache_file=not data_args.overwrite_cache,
+            desc="Running tokenizer on dataset",
+        )
+
+    if training_args.do_train:
+        if "train" not in raw_datasets:
+            raise ValueError("--do_train requires a train dataset.")
+        train_dataset = raw_datasets["train"]
+        if data_args.shuffle_train_dataset:
+            logger.info("Shuffling the training dataset")
+            train_dataset = train_dataset.shuffle(seed=data_args.shuffle_seed)
+        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 training_args.do_eval:
+        if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
+            if "test" not in raw_datasets and "test_matched" not in raw_datasets:
+                raise ValueError("--do_eval requires a validation or test dataset if validation is not defined.")
+            else:
+                logger.warning("Validation dataset not found. Falling back to test dataset for validation.")
+                eval_dataset = raw_datasets["test"]
+        else:
+            eval_dataset = raw_datasets["validation"]
+
+        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))
+
+    if training_args.do_predict or data_args.test_file is not None:
+        if "test" not in raw_datasets:
+            raise ValueError("--do_predict requires a test dataset")
+        predict_dataset = raw_datasets["test"]
+        # remove label column if it exists
+        if data_args.max_predict_samples is not None:
+            max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
+            predict_dataset = predict_dataset.select(range(max_predict_samples))
+
+    # Log a few random samples from the training set:
+    if training_args.do_train:
+        for index in random.sample(range(len(train_dataset)), 3):
+            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
+
+    if data_args.metric_name is not None:
+        metric = (
+            evaluate.load(data_args.metric_name, config_name="multilabel", cache_dir=model_args.cache_dir)
+            if is_multi_label
+            else evaluate.load(data_args.metric_name, cache_dir=model_args.cache_dir)
+        )
+        logger.info(f"Using metric {data_args.metric_name} for evaluation.")
+    else:
+        if is_regression:
+            metric = evaluate.load("mse", cache_dir=model_args.cache_dir)
+            logger.info("Using mean squared error (mse) as regression score, you can use --metric_name to overwrite.")
+        else:
+            if is_multi_label:
+                metric = evaluate.load("f1", config_name="multilabel", cache_dir=model_args.cache_dir)
+                logger.info(
+                    "Using multilabel F1 for multi-label classification task, you can use --metric_name to overwrite."
+                )
+            else:
+                metric = evaluate.load("accuracy", cache_dir=model_args.cache_dir)
+                logger.info("Using accuracy as classification score, you can use --metric_name to overwrite.")
+
+    def compute_metrics(p: EvalPrediction):
+        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
+        if is_regression:
+            preds = np.squeeze(preds)
+            result = metric.compute(predictions=preds, references=p.label_ids)
+        elif is_multi_label:
+            preds = np.array([np.where(p > 0, 1, 0) for p in preds])  # convert logits to multi-hot encoding
+            # Micro F1 is commonly used in multi-label classification
+            result = metric.compute(predictions=preds, references=p.label_ids, average="micro")
+        else:
+            preds = np.argmax(preds, axis=1)
+            result = metric.compute(predictions=preds, references=p.label_ids)
+        if len(result) > 1:
+            result["combined_score"] = np.mean(list(result.values())).item()
+        return result
+
+    # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
+    # we already did the padding.
+    if data_args.pad_to_max_length:
+        data_collator = default_data_collator
+    elif training_args.fp16:
+        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
+    else:
+        data_collator = None
+
+    # Initialize our Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=train_dataset if training_args.do_train else None,
+        eval_dataset=eval_dataset if training_args.do_eval else None,
+        compute_metrics=compute_metrics,
+        tokenizer=tokenizer,
+        data_collator=data_collator,
+    )
+
+    # Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
+        )
+        metrics["train_samples"] = min(max_train_samples, len(train_dataset))
+        trainer.save_model()  # Saves the tokenizer too for easy upload
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # Evaluation
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(eval_dataset=eval_dataset)
+        max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
+        metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    if training_args.do_predict:
+        logger.info("*** Predict ***")
+        # Removing the `label` columns if exists because it might contains -1 and Trainer won't like that.
+        if "label" in predict_dataset.features:
+            predict_dataset = predict_dataset.remove_columns("label")
+        predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
+        if is_regression:
+            predictions = np.squeeze(predictions)
+        elif is_multi_label:
+            # Convert logits to multi-hot encoding. We compare the logits to 0 instead of 0.5, because the sigmoid is not applied.
+            # You can also pass `preprocess_logits_for_metrics=lambda logits, labels: nn.functional.sigmoid(logits)` to the Trainer
+            # and set p > 0.5 below (less efficient in this case)
+            predictions = np.array([np.where(p > 0, 1, 0) for p in predictions])
+        else:
+            predictions = np.argmax(predictions, axis=1)
+        output_predict_file = os.path.join(training_args.output_dir, "predict_results.txt")
+        if trainer.is_world_process_zero():
+            with open(output_predict_file, "w") as writer:
+                logger.info("***** Predict results *****")
+                writer.write("index\tprediction\n")
+                for index, item in enumerate(predictions):
+                    if is_regression:
+                        writer.write(f"{index}\t{item:3.3f}\n")
+                    elif is_multi_label:
+                        # recover from multi-hot encoding
+                        item = [label_list[i] for i in range(len(item)) if item[i] == 1]
+                        writer.write(f"{index}\t{item}\n")
+                    else:
+                        item = label_list[item]
+                        writer.write(f"{index}\t{item}\n")
+        logger.info("Predict results saved at {}".format(output_predict_file))
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+
+def _mp_fn(index):
+    # For xla_spawn (TPUs)
+    main()
+
+
+if __name__ == "__main__":
+    main()