usrbinenv python codingutf8 2020 the huggingface inc team 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 orglicenseslicense2 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 loaddataset import transformers from transformers import autoconfig automodelforsequenceclassification autotokenizer datacollatorwithpadding evalprediction hfargumentparser trainer trainingarguments defaultdatacollator setseed from transformers trainerutils import getlastcheckpoint from transformers utils import checkminversion sendexampletelemetry from transformers utils versions import requireversion will error if the minimal version of transformers is not installed remove at your own risks checkminversion4 36 0 dev0 requireversiondatasets1 8 0 to fix pip install r examplespytorchtextclassificationrequirements txt logger logging getloggername dataclass class datatrainingarguments datasetname optionalstr field defaultnone metadatahelp the name of the dataset to use via the datasets library datasetconfigname optionalstr field defaultnone metadatahelp the configuration name of the dataset to use via the datasets library doregression bool field defaultnone metadata help whether to do regression instead of classification if none will be inferred from the dataset textcolumnnames optionalstr field defaultnone metadata help the name of the text column in the input dataset or a csvjson file if not specified will use the sentence column for singlemultilabel classifcation task textcolumndelimiter optionalstr field default metadatahelp the delimiter to use to join text columns into a single sentence trainsplitname optionalstr field defaultnone metadata help the name of the train split in the input dataset if not specified will use the train split when dotrain is enabled validationsplitname optionalstr field defaultnone metadata help the name of the validation split in the input dataset if not specified will use the validation split when doeval is enabled testsplitname optionalstr field defaultnone metadata help the name of the test split in the input dataset if not specified will use the test split when dopredict is enabled removesplits optionalstr field defaultnone metadatahelp the splits to remove from the dataset multiple splits should be separated by commas removecolumns optionalstr field defaultnone metadatahelp the columns to remove from the dataset multiple columns should be separated by commas labelcolumnname optionalstr field defaultnone metadata help the name of the label column in the input dataset or a csvjson file if not specified will use the label column for singlemultilabel classifcation task maxseqlength int field default128 metadata help the maximum total input sequence length after tokenization sequences longer than this will be truncated sequences shorter will be padded overwritecache bool field defaultfalse metadatahelp overwrite the cached preprocessed datasets or not padtomaxlength bool field defaulttrue metadata help whether to pad all samples to maxseqlength if false will pad the samples dynamically when batching to the maximum length in the batch shuffletraindataset bool field defaultfalse metadatahelp whether to shuffle the train dataset or not shuffleseed int field default42 metadatahelp random seed that will be used to shuffle the train dataset maxtrainsamples optionalint field defaultnone metadata help for debugging purposes or quicker training truncate the number of training examples to this value if set maxevalsamples optionalint field defaultnone metadata help for debugging purposes or quicker training truncate the number of evaluation examples to this value if set maxpredictsamples optionalint field defaultnone metadata help for debugging purposes or quicker training truncate the number of prediction examples to this value if set metricname optionalstr fielddefaultnone metadatahelp the metric to use for evaluation trainfile optionalstr field defaultnone metadatahelp a csv or a json file containing the training data validationfile optionalstr field defaultnone metadatahelp a csv or a json file containing the validation data testfile optionalstr fielddefaultnone metadatahelp a csv or a json file containing the test data def postinitself if self datasetname is none if self trainfile is none or self validationfile is none raise valueerror trainingvalidation file or a dataset name trainextension self trainfile split 1 assert trainextension in csv json trainfile should be a csv or a json file validationextension self validationfile split 1 assert validationextension trainextension validationfile should have the same extension csv or json as trainfile dataclass class modelarguments modelnameorpath str field metadatahelp path to pretrained model or model identifier from huggingface comodels configname optionalstr field defaultnone metadatahelp pretrained config name or path if not the same as modelname tokenizername optionalstr field defaultnone metadatahelp pretrained tokenizer name or path if not the same as modelname cachedir optionalstr field defaultnone metadatahelp where do you want to store the pretrained models downloaded from huggingface co usefasttokenizer bool field defaulttrue metadatahelp whether to use one of the fast tokenizer backed by the tokenizers library or not modelrevision str field defaultmain metadatahelp the specific model version to use can be a branch name tag name or commit id token str field defaultnone metadata help the token to use as http bearer ization for remote files if not specified will use the token generated when running huggingfacecli login stored in huggingface useauthtoken bool field defaultnone metadata help the useauthtoken argument is deprecated and will be removed in v4 34 please use token instead trustremotecode bool field defaultfalse 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 ignoremismatchedsizes bool field defaultfalse metadatahelp will enable to load a pretrained model whose head dimensions are different def getlabellistrawdataset splittrain liststr we will treat the label list as a list of string instead of int consistent with model config label2id see all possible arguments in srctransformerstrainingargs py or by passing the help flag to this script we now keep distinct sets of args for a cleaner separation of concerns 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 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 pythonpytorch versions setup logging the default of trainingargs loglevel is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint set seed before initializing model get the datasets you can either provide your own csvjson training and evaluation files or specify a dataset name to load from huggingfacedatasets in ether case you can specify a the key of the columns containing the text and the key of the column containing the label if multiple columns are specified for the text they will be joined togather for the actual text value in distributed training the loaddataset function guarantee that only one local process can concurrently download the dataset downloading and loading a dataset from the hub try print some info about the dataset loading a dataset from your local files csvjson training and evaluation files are needed get the test dataset you can provide your own csvjson test file loading a dataset from local csv files loading a dataset from local json files see more about loading any type of standard or custom dataset at https huggingface codocsdatasetsloadingdatasets trying to have good defaults here don t hesitate to tweak to your needs regession requires float as label type let s cast it if needed trying to find the number of labels in a multilabel classification task we have to deal with common cases that labels appear in the training set but not in the validationtest set so we build the label list from the union of labels in trainvaltest add the labels that appear in valtest but not in train throw a warning if label is 1 we throw a warning and remove it from the label list load pretrained model and tokenizer in distributed training the frompretrained methods guarantee that only one local process can concurrently download model vocab padding strategy we will pad later dynamically at batch creation to the max sequence length in each batch for training we will update the config with label infos if dotrain is not set we will use the label infos in the config update config with label infos join together text columns into sentence column tokenize the texts running the preprocessing pipeline on all the datasets remove label column if it exists log a few random samples from the training set micro f1 is commonly used in multilabel classification data collator will default to datacollatorwithpadding when the tokenizer is passed to trainer so we change it if we already did the padding initialize our trainer training evaluation removing the label columns if exists because it might contains 1 and trainer won t like that convert logits to multihot encoding we compare the logits to 0 instead of 0 5 because the sigmoid is not applied you can also pass preprocesslogitsformetricslambda logits labels nn functional sigmoidlogits to the trainer and set p 0 5 below less efficient in this case recover from multihot encoding for xlaspawn tpus usr bin env python coding utf 8 2020 the huggingface inc team 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 will error if the minimal version of transformers is not installed remove at your own risks 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 arguments pertaining to which model config tokenizer we are going to fine tune from get the list of labels from a mutli label dataset we will treat the label list as a list of string instead of int consistent with model config label2id 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 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 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 setup logging the default of training_args log_level is passive so we set log level at info here to have that default log on each process the small summary detecting last checkpoint set seed before initializing model 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 togather for the actual text value in distributed training the load_dataset function guarantee that only one local process can concurrently download the dataset downloading and loading a dataset from the hub try print some info about the dataset loading a dataset from your local files csv json training and evaluation files are needed get the test dataset you can provide your own csv json test file loading a dataset from local csv files loading a dataset from local json files see more about loading any type of standard or custom dataset at https huggingface co docs datasets loading_datasets trying to have good defaults here don t hesitate to tweak to your needs regession requires float as label type let s cast it if needed classification 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 add the labels that appear in val test but not in train throw a warning if label is 1 we throw a warning and remove it from the label list load pretrained model and tokenizer in distributed training the from_pretrained methods guarantee that only one local process can concurrently download model vocab padding strategy we will pad later dynamically at batch creation to the max sequence length in each batch 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 classification training update config with label infos classification but not training regression bceloss requires float as target type join together text columns into sentence column tokenize the texts running the preprocessing pipeline on all the datasets remove label column if it exists log a few random samples from the training set convert logits to multi hot encoding micro f1 is commonly used in multi label classification data collator will default to datacollatorwithpadding when the tokenizer is passed to trainer so we change it if we already did the padding initialize our trainer training saves the tokenizer too for easy upload evaluation removing the label columns if exists because it might contains 1 and trainer won t like that 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 recover from multi hot encoding for xla_spawn tpus
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



check_min_version("4.36.0.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:
    

    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 classifcation 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 classifcation 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:
    

    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]:
    

    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")
    
    label_list = [str(label) for label in label_list]
    return label_list


def main():
    
    
    

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        
        
        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

    
    
    send_example_telemetry("run_classification", model_args, data_args)

    
    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:
        
        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()

    
    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}")

    
    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(training_args.seed)

    
    
    
    
    
    
    if data_args.dataset_name is not None:
        
        raw_datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
            token=model_args.token,
        )
        
        logger.info(f"Dataset loaded: {raw_datasets}")
        logger.info(raw_datasets)
    else:
        
        
        data_files = {"train": data_args.train_file, "validation": data_args.validation_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"):
            
            raw_datasets = load_dataset(
                "csv",
                data_files=data_files,
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )
        else:
            
            raw_datasets = load_dataset(
                "json",
                data_files=data_files,
                cache_dir=model_args.cache_dir,
                token=model_args.token,
            )

    
    

    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.validation_split_name} as validation 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")

    

    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
        
        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:  
        if raw_datasets["train"].features["label"].dtype == "list":  
            is_multi_label = True
            logger.info("Label type is list, doing multi-label classification")
        
        
        
        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:
                    
                    logger.warning(
                        f"Labels {diff} in {split} set but not in training set, adding them to the label list"
                    )
                    label_list += list(diff)
        
        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.")

    
    
    
    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,
    )

    
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        
        padding = False

    
    
    if training_args.do_train and not is_regression:  
        label_to_id = {v: i for i, v in enumerate(label_list)}
        
        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 config.label2id.items()}
    elif not is_regression:  
        logger.info("using label infos in the model config")
        logger.info("label2id: {}".format(model.config.label2id))
        label_to_id = model.config.label2id
    else:  
        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)  
        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(",")
            
            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]
        
        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

    
    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"]
        
        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))

    
    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")
            if is_multi_label
            else evaluate.load(data_args.metric_name)
        )
        logger.info(f"Using metric {data_args.metric_name} for evaluation.")
    else:
        if is_regression:
            metric = evaluate.load("mse")
            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")
                logger.info(
                    "Using multilabel F1 for multi-label classification task, you can use --metric_name to overwrite."
                )
            else:
                metric = evaluate.load("accuracy")
                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])  
            
            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

    
    
    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

    
    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,
    )

    
    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()  
        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    
    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 ***")
        
        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:
            
            
            
            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:
                        
                        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):
    
    main()


if __name__ == "__main__":
    main()