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, OurTrainingArguments))
    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 (
        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 if is_main_process(training_args.local_rank) else logging.WARN,
    )

    # 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: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    if is_main_process(training_args.local_rank):
        transformers.utils.logging.set_verbosity_info()
        transformers.utils.logging.enable_default_handler()
        transformers.utils.logging.enable_explicit_format()
    logger.info("Training/evaluation parameters %s", training_args)

    # Set seed before initializing model.
    set_seed(training_args.seed)

    # 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. You can easily tweak this
    # behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    data_files = {}
    if data_args.train_file is not None:
        data_files["train"] = data_args.train_file
    extension = data_args.train_file.split(".")[-1]
    if extension == "txt":
        extension = "text"
    if extension == "csv":
        datasets = load_dataset(extension, data_files=data_files, cache_dir="./data/", delimiter="\t" if "tsv" in data_args.train_file else ",")
    else:
        datasets = load_dataset(extension, data_files=data_files, cache_dir="./data/")

    # 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.

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "use_auth_token": True if model_args.use_auth_token else None,
    }
    if model_args.config_name:
        config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")

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

    if model_args.model_name_or_path:
        if 'roberta' in model_args.model_name_or_path:
            model = RobertaForCL.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,
                use_auth_token=True if model_args.use_auth_token else None,
                model_args=model_args                  
            )
        elif 'bert' in model_args.model_name_or_path:
            model = BertForCL.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,
                use_auth_token=True if model_args.use_auth_token else None,
                model_args=model_args
            )
            if model_args.do_mlm:
                pretrained_model = BertForPreTraining.from_pretrained(model_args.model_name_or_path)
                model.lm_head.load_state_dict(pretrained_model.cls.predictions.state_dict())
        else:
            raise NotImplementedError
    else:
        raise NotImplementedError
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedLM.from_config(config)

    model.resize_token_embeddings(len(tokenizer))

    # Prepare features
    column_names = datasets["train"].column_names
    sent2_cname = None
    if len(column_names) == 2:
        # Pair datasets
        sent0_cname = column_names[0]
        sent1_cname = column_names[1]
    elif len(column_names) == 3:
        # Pair datasets with hard negatives
        sent0_cname = column_names[0]
        sent1_cname = column_names[1]
        sent2_cname = column_names[2]
    elif len(column_names) == 1:
        # Unsupervised datasets
        sent0_cname = column_names[0]
        sent1_cname = column_names[0]
    else:
        raise NotImplementedError

    def prepare_features(examples):
        # padding = longest (default)
        #   If no sentence in the batch exceed the max length, then use
        #   the max sentence length in the batch, otherwise use the 
        #   max sentence length in the argument and truncate those that
        #   exceed the max length.
        # padding = max_length (when pad_to_max_length, for pressure test)
        #   All sentences are padded/truncated to data_args.max_seq_length.
        total = len(examples[sent0_cname])

        # Avoid "None" fields 
        for idx in range(total):
            if examples[sent0_cname][idx] is None:
                examples[sent0_cname][idx] = " "
            if examples[sent1_cname][idx] is None:
                examples[sent1_cname][idx] = " "
        
        sentences = examples[sent0_cname] + examples[sent1_cname]

        # If hard negative exists
        if sent2_cname is not None:
            for idx in range(total):
                if examples[sent2_cname][idx] is None:
                    examples[sent2_cname][idx] = " "
            sentences += examples[sent2_cname]

        sent_features = tokenizer(
            sentences,
            max_length=data_args.max_seq_length,
            truncation=True,
            padding="max_length" if data_args.pad_to_max_length else False,
        )

        features = {}
        if sent2_cname is not None:
            for key in sent_features:
                features[key] = [[sent_features[key][i], sent_features[key][i+total], sent_features[key][i+total*2]] for i in range(total)]
        else:
            for key in sent_features:
                features[key] = [[sent_features[key][i], sent_features[key][i+total]] for i in range(total)]
            
        return features

    if training_args.do_train:
        train_dataset = datasets["train"].map(
            prepare_features,
            batched=True,
            num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=not data_args.overwrite_cache,
        )

    # Data collator
    @dataclass
    class OurDataCollatorWithPadding:

        tokenizer: PreTrainedTokenizerBase
        padding: Union[bool, str, PaddingStrategy] = True
        max_length: Optional[int] = None
        pad_to_multiple_of: Optional[int] = None
        mlm: bool = True
        mlm_probability: float = data_args.mlm_probability

        def __call__(self, features: List[Dict[str, Union[List[int], List[List[int]], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
            special_keys = ['input_ids', 'attention_mask', 'token_type_ids', 'mlm_input_ids', 'mlm_labels']
            bs = len(features)
            if bs > 0:
                num_sent = len(features[0]['input_ids'])
            else:
                return
            flat_features = []
            for feature in features:
                for i in range(num_sent):
                    flat_features.append({k: feature[k][i] if k in special_keys else feature[k] for k in feature})

            batch = self.tokenizer.pad(
                flat_features,
                padding=self.padding,
                max_length=self.max_length,
                pad_to_multiple_of=self.pad_to_multiple_of,
                return_tensors="pt",
            )
            if model_args.do_mlm:
                batch["mlm_input_ids"], batch["mlm_labels"] = self.mask_tokens(batch["input_ids"])

            batch = {k: batch[k].view(bs, num_sent, -1) if k in special_keys else batch[k].view(bs, num_sent, -1)[:, 0] for k in batch}

            if "label" in batch:
                batch["labels"] = batch["label"]
                del batch["label"]
            if "label_ids" in batch:
                batch["labels"] = batch["label_ids"]
                del batch["label_ids"]

            return batch
        
        def mask_tokens(
            self, inputs: torch.Tensor, special_tokens_mask: Optional[torch.Tensor] = None
        ) -> Tuple[torch.Tensor, torch.Tensor]:
            """
            Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
            """
            inputs = inputs.clone()
            labels = inputs.clone()
            # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
            probability_matrix = torch.full(labels.shape, self.mlm_probability)
            if special_tokens_mask is None:
                special_tokens_mask = [
                    self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist()
                ]
                special_tokens_mask = torch.tensor(special_tokens_mask, dtype=torch.bool)
            else:
                special_tokens_mask = special_tokens_mask.bool()

            probability_matrix.masked_fill_(special_tokens_mask, value=0.0)
            masked_indices = torch.bernoulli(probability_matrix).bool()
            labels[~masked_indices] = -100  # We only compute loss on masked tokens

            # 80% of the time, we replace masked input tokens with tokenizer.mask_token ([MASK])
            indices_replaced = torch.bernoulli(torch.full(labels.shape, 0.8)).bool() & masked_indices
            inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token)

            # 10% of the time, we replace masked input tokens with random word
            indices_random = torch.bernoulli(torch.full(labels.shape, 0.5)).bool() & masked_indices & ~indices_replaced
            random_words = torch.randint(len(self.tokenizer), labels.shape, dtype=torch.long)
            inputs[indices_random] = random_words[indices_random]

            # The rest of the time (10% of the time) we keep the masked input tokens unchanged
            return inputs, labels

    data_collator = default_data_collator if data_args.pad_to_max_length else OurDataCollatorWithPadding(tokenizer)

    trainer = CLTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )
    trainer.model_args = model_args

    # Training
    if training_args.do_train:
        model_path = (
            model_args.model_name_or_path
            if (model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path))
            else None
        )
        train_result = trainer.train(model_path=model_path)
        trainer.save_model()  # Saves the tokenizer too for easy upload

        output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
        if trainer.is_world_process_zero():
            with open(output_train_file, "w") as writer:
                logger.info("***** Train results *****")
                for key, value in sorted(train_result.metrics.items()):
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
            trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))

    # Evaluation
    results = {}
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        results = trainer.evaluate(eval_senteval_transfer=True)

        output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
        if trainer.is_world_process_zero():
            with open(output_eval_file, "w") as writer:
                logger.info("***** Eval results *****")
                for key, value in sorted(results.items()):
                    logger.info(f"  {key} = {value}")
                    writer.write(f"{key} = {value}\n")

    return results
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original.
train.py