From 379f649434e8a3477b11f5e04aaf9692ebb65af4 Mon Sep 17 00:00:00 2001 From: Matt Date: Mon, 12 Jul 2021 15:58:38 +0100 Subject: [PATCH] TF summarization example (#12617) * Adding a TF summarization example * Style pass * Style fixes * Updates for review comments * Adding README * Style pass * Remove unused import --- examples/tensorflow/summarization/README.md | 40 ++ .../summarization/run_summarization.py | 663 ++++++++++++++++++ 2 files changed, 703 insertions(+) create mode 100644 examples/tensorflow/summarization/README.md create mode 100644 examples/tensorflow/summarization/run_summarization.py diff --git a/examples/tensorflow/summarization/README.md b/examples/tensorflow/summarization/README.md new file mode 100644 index 0000000000..032af0241c --- /dev/null +++ b/examples/tensorflow/summarization/README.md @@ -0,0 +1,40 @@ + + +# Summarization example + +This script shows an example of training a *summarization* model with the 🤗 Transformers library. +For straightforward use-cases you may be able to use these scripts without modification, although we have also +included comments in the code to indicate areas that you may need to adapt to your own projects. + +### Multi-GPU and TPU usage + +By default, these scripts use a `MirroredStrategy` and will use multiple GPUs effectively if they are available. TPUs +can also be used by passing the name of the TPU resource with the `--tpu` argument. + +### Example command +``` +python run_summarization.py \ +--model_name_or_path facebook/bart-base \ +--dataset_name cnn_dailymail \ +--dataset_config "3.0.0" \ +--output_dir /tmp/tst-summarization \ +--per_device_train_batch_size 8 \ +--per_device_eval_batch_size 16 \ +--num_train_epochs 3 \ +--do_train \ +--do_eval +``` \ No newline at end of file diff --git a/examples/tensorflow/summarization/run_summarization.py b/examples/tensorflow/summarization/run_summarization.py new file mode 100644 index 0000000000..3ce82082d3 --- /dev/null +++ b/examples/tensorflow/summarization/run_summarization.py @@ -0,0 +1,663 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright 2021 The HuggingFace Team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for summarization. +""" +# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments. + +import logging +import os +import sys +from dataclasses import dataclass, field +from functools import partial +from typing import Optional + +import datasets +import nltk # Here to have a nice missing dependency error message early on +import numpy as np +import tensorflow as tf +from datasets import load_dataset, load_metric +from tqdm import tqdm + +import transformers +from filelock import FileLock +from transformers import ( + AutoConfig, + AutoTokenizer, + HfArgumentParser, + TFAutoModelForSeq2SeqLM, + TFTrainingArguments, + create_optimizer, + set_seed, +) +from transformers.file_utils import is_offline_mode +from transformers.trainer_utils import get_last_checkpoint +from transformers.utils import check_min_version +from transformers.utils.versions import require_version + + +# region Checking dependencies +# Will error if the minimal version of Transformers is not installed. Remove at your own risks. +check_min_version("4.9.0.dev0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt") + +logger = logging.getLogger(__name__) + +try: + nltk.data.find("tokenizers/punkt") +except (LookupError, OSError): + if is_offline_mode(): + raise LookupError( + "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files" + ) + with FileLock(".lock") as lock: + nltk.download("punkt", quiet=True) +# endregion + + +# region Arguments +@dataclass +class ModelArguments: + """ + Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. + """ + + model_name_or_path: str = field( + metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} + ) + config_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} + ) + tokenizer_name: Optional[str] = field( + default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} + ) + cache_dir: Optional[str] = field( + default=None, + metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"}, + ) + use_fast_tokenizer: bool = field( + default=True, + metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, + ) + model_revision: str = field( + default="main", + metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, + ) + use_auth_token: bool = field( + default=False, + metadata={ + "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script " + "with private models)." + }, + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + 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)."} + ) + text_column: Optional[str] = field( + default=None, + metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."}, + ) + summary_column: Optional[str] = field( + default=None, + metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."}, + ) + train_file: Optional[str] = field( + default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."} + ) + validation_file: Optional[str] = field( + default=None, + metadata={ + "help": "An optional input evaluation data file to evaluate the metrics (rouge) on " + "(a jsonlines or csv file)." + }, + ) + test_file: Optional[str] = field( + default=None, + metadata={ + "help": "An optional input test data file to evaluate the metrics (rouge) on " "(a jsonlines or csv file)." + }, + ) + overwrite_cache: bool = field( + default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} + ) + preprocessing_num_workers: Optional[int] = field( + default=None, + metadata={"help": "The number of processes to use for the preprocessing."}, + ) + max_source_length: Optional[int] = field( + default=1024, + metadata={ + "help": "The maximum total input sequence length after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded." + }, + ) + max_target_length: Optional[int] = field( + default=128, + metadata={ + "help": "The maximum total sequence length for target text after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded." + }, + ) + val_max_target_length: Optional[int] = field( + default=None, + metadata={ + "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer " + "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`." + "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used " + "during ``evaluate`` and ``predict``." + }, + ) + pad_to_max_length: bool = field( + default=False, + metadata={ + "help": "Whether to pad all samples to model maximum sentence length. " + "If False, will pad the samples dynamically when batching to the maximum length in the batch. More " + "efficient on GPU but very bad for TPU." + }, + ) + max_train_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + }, + ) + max_eval_samples: Optional[int] = field( + default=None, + metadata={ + "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this " + "value if set." + }, + ) + 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." + }, + ) + num_beams: Optional[int] = field( + default=None, + metadata={ + "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, " + "which is used during ``evaluate`` and ``predict``." + }, + ) + ignore_pad_token_for_loss: bool = field( + default=True, + metadata={ + "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not." + }, + ) + source_prefix: Optional[str] = field( + default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."} + ) + + def __post_init__(self): + if self.dataset_name is None and self.train_file is None and self.validation_file is None: + raise ValueError("Need either a dataset name or a training/validation file.") + else: + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in ["csv", "json"], "`train_file` should be a csv or a json file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file." + if self.val_max_target_length is None: + self.val_max_target_length = self.max_target_length + + +# endregion + + +# region Dataset name mappings +summarization_name_mapping = { + "amazon_reviews_multi": ("review_body", "review_title"), + "big_patent": ("description", "abstract"), + "cnn_dailymail": ("article", "highlights"), + "orange_sum": ("text", "summary"), + "pn_summary": ("article", "summary"), + "psc": ("extract_text", "summary_text"), + "samsum": ("dialogue", "summary"), + "thaisum": ("body", "summary"), + "xglue": ("news_body", "news_title"), + "xsum": ("document", "summary"), + "wiki_summary": ("article", "highlights"), +} +# endregion + + +# region Data generator +def sample_generator(dataset, model, tokenizer, shuffle, pad_to_multiple_of=None): + if shuffle: + sample_ordering = np.random.permutation(len(dataset)) + else: + sample_ordering = np.arange(len(dataset)) + for sample_idx in sample_ordering: + example = dataset[int(sample_idx)] + # Handle dicts with proper padding and conversion to tensor. + example = tokenizer.pad(example, return_tensors="np", pad_to_multiple_of=pad_to_multiple_of) + example = {key: tf.convert_to_tensor(arr, dtype_hint=tf.int32) for key, arr in example.items()} + if model is not None and hasattr(model, "prepare_decoder_input_ids_from_labels"): + decoder_input_ids = model.prepare_decoder_input_ids_from_labels( + labels=tf.expand_dims(example["labels"], 0) + ) + example["decoder_input_ids"] = tf.squeeze(decoder_input_ids, 0) + yield example, example["labels"] # TF needs some kind of labels, even if we don't use them + return + + +# endregion + + +# region Helper functions +def dataset_to_tf(dataset, model, tokenizer, total_batch_size, num_epochs, shuffle): + if dataset is None: + return None + train_generator = partial(sample_generator, dataset, model, tokenizer, shuffle=shuffle) + train_signature = { + feature: tf.TensorSpec(shape=(None,), dtype=tf.int32) + for feature in dataset.features + if feature != "special_tokens_mask" + } + if ( + model is not None + and "decoder_input_ids" not in train_signature + and hasattr(model, "prepare_decoder_input_ids_from_labels") + ): + train_signature["decoder_input_ids"] = train_signature["labels"] + # This may need to be changed depending on your particular model or tokenizer! + padding_values = { + key: tf.convert_to_tensor(tokenizer.pad_token_id if tokenizer.pad_token_id is not None else 0, dtype=tf.int32) + for key in train_signature.keys() + } + padding_values["labels"] = tf.convert_to_tensor(-100, dtype=tf.int32) + train_signature["labels"] = train_signature["input_ids"] + train_signature = (train_signature, train_signature["labels"]) + options = tf.data.Options() + options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF + tf_dataset = ( + tf.data.Dataset.from_generator(train_generator, output_signature=train_signature) + .with_options(options) + .padded_batch( + batch_size=total_batch_size, + drop_remainder=True, + padding_values=(padding_values, np.array(-100, dtype=np.int32)), + ) + .repeat(int(num_epochs)) + ) + return tf_dataset + + +# endregion + + +def main(): + # region Argument parsing + # See all possible arguments in src/transformers/training_args.py + # or by passing the --help flag to this script. + # We now keep distinct sets of args, for a cleaner separation of concerns. + + parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)) + if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): + # If we pass only one argument to the script and it's the path to a json file, + # let's parse it to get our arguments. + model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) + else: + model_args, data_args, training_args = parser.parse_args_into_dataclasses() + # endregion + + # region Logging + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + handlers=[logging.StreamHandler(sys.stdout)], + ) + logger.setLevel(logging.INFO) + datasets.utils.logging.set_verbosity(logging.INFO) + transformers.utils.logging.set_verbosity(logging.INFO) + + # Log on each process the small summary: + logger.info(f"Training/evaluation parameters {training_args}") + # endregion + + # region T5 special-casing + if data_args.source_prefix is None and model_args.model_name_or_path in [ + "t5-small", + "t5-base", + "t5-large", + "t5-3b", + "t5-11b", + ]: + logger.warning( + "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with " + "`--source_prefix 'summarize: ' `" + ) + # endregion + + # region 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." + ) + # endregion + + # Set seed before initializing model. + set_seed(training_args.seed) + + # region Load datasets + # Get the datasets: you can either provide your own CSV/JSON 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 first column for the full texts and the second column for the + # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments). + # + # 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 + ) + else: + 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 data_args.validation_file is not None: + data_files["validation"] = data_args.validation_file + extension = data_args.validation_file.split(".")[-1] + if data_args.test_file is not None: + data_files["test"] = data_args.test_file + extension = data_args.test_file.split(".")[-1] + raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir) + # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at + # https://huggingface.co/docs/datasets/loading_datasets.html. + # endregion + + # region Load model config and tokenizer + # + # Distributed training: + # The .from_pretrained methods guarantee that only one local process can concurrently + # download model & vocab. + + config = AutoConfig.from_pretrained( + model_args.config_name if model_args.config_name else model_args.model_name_or_path, + cache_dir=model_args.cache_dir, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + tokenizer = AutoTokenizer.from_pretrained( + model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, + cache_dir=model_args.cache_dir, + use_fast=model_args.use_fast_tokenizer, + revision=model_args.model_revision, + use_auth_token=True if model_args.use_auth_token else None, + ) + + prefix = data_args.source_prefix if data_args.source_prefix is not None else "" + # endregion + + # region Dataset preprocessing + # We need to tokenize inputs and targets. + if training_args.do_train: + column_names = raw_datasets["train"].column_names + elif training_args.do_eval: + column_names = raw_datasets["validation"].column_names + else: + logger.info("There is nothing to do. Please pass `do_train`, and/or `do_eval`.") + return + + # Get the column names for input/target. + dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None) + if data_args.text_column is None: + text_column = dataset_columns[0] if dataset_columns is not None else column_names[0] + else: + text_column = data_args.text_column + if text_column not in column_names: + raise ValueError( + f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}" + ) + if data_args.summary_column is None: + summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1] + else: + summary_column = data_args.summary_column + if summary_column not in column_names: + raise ValueError( + f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}" + ) + + # Temporarily set max_target_length for training. + max_target_length = data_args.max_target_length + padding = "max_length" if data_args.pad_to_max_length else False + + def preprocess_function(examples): + inputs = examples[text_column] + targets = examples[summary_column] + inputs = [prefix + inp for inp in inputs] + model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True) + + # Setup the tokenizer for targets + with tokenizer.as_target_tokenizer(): + labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True) + + # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore + # padding in the loss. + if padding == "max_length" and data_args.ignore_pad_token_for_loss: + labels["input_ids"] = [ + [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"] + ] + + model_inputs["labels"] = labels["input_ids"] + return model_inputs + + 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.max_train_samples is not None: + train_dataset = train_dataset.select(range(data_args.max_train_samples)) + with training_args.main_process_first(desc="train dataset map pre-processing"): + train_dataset = train_dataset.map( + preprocess_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on train dataset", + ) + else: + train_dataset = None + + if training_args.do_eval: + max_target_length = data_args.val_max_target_length + if "validation" not in raw_datasets: + raise ValueError("--do_eval requires a validation dataset") + eval_dataset = raw_datasets["validation"] + if data_args.max_eval_samples is not None: + eval_dataset = eval_dataset.select(range(data_args.max_eval_samples)) + with training_args.main_process_first(desc="validation dataset map pre-processing"): + eval_dataset = eval_dataset.map( + preprocess_function, + batched=True, + num_proc=data_args.preprocessing_num_workers, + remove_columns=column_names, + load_from_cache_file=not data_args.overwrite_cache, + desc="Running tokenizer on validation dataset", + ) + else: + eval_dataset = None + # endregion + + # region Text preprocessing + def postprocess_text(preds, labels): + preds = [pred.strip() for pred in preds] + labels = [label.strip() for label in labels] + + # rougeLSum expects newline after each sentence + preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds] + labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels] + + return preds, labels + + # endregion + + with training_args.strategy.scope(): + # region Prepare model + model = TFAutoModelForSeq2SeqLM.from_pretrained( + 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.resize_token_embeddings(len(tokenizer)) + # endregion + + # region Prepare TF Dataset objects + if model.config.decoder_start_token_id is None: + raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") + + num_replicas = training_args.strategy.num_replicas_in_sync + total_train_batch_size = training_args.per_device_train_batch_size * num_replicas + total_eval_batch_size = training_args.per_device_eval_batch_size * num_replicas + tf_train_dataset = dataset_to_tf( + train_dataset, + model, + tokenizer, + total_batch_size=total_train_batch_size, + num_epochs=training_args.num_train_epochs, + shuffle=True, + ) + tf_eval_dataset = dataset_to_tf( + eval_dataset, + model, + tokenizer, + total_eval_batch_size, + num_epochs=1, + shuffle=False, + ) + # endregion + + # region Optimizer, loss and LR scheduling + # Scheduler and math around the number of training steps. + num_update_steps_per_epoch = len(train_dataset) // training_args.per_device_train_batch_size + num_train_steps = training_args.num_train_epochs * num_update_steps_per_epoch + optimizer, lr_schedule = create_optimizer( + init_lr=training_args.learning_rate, num_train_steps=num_train_steps, num_warmup_steps=0 + ) + + def masked_sparse_categorical_crossentropy(y_true, y_pred): + # We clip the negative labels to 0 to avoid NaNs appearing in the output and + # fouling up everything that comes afterwards. The loss values corresponding to clipped values + # will be masked later anyway, but even masked NaNs seem to cause overflows for some reason. + # 1e6 is chosen as a reasonable upper bound for the number of token indices - in the unlikely + # event that you have more than 1 million tokens in your vocabulary, consider increasing this value. + # More pragmatically, consider redesigning your tokenizer. + losses = tf.keras.losses.sparse_categorical_crossentropy( + tf.clip_by_value(y_true, 0, int(1e6)), y_pred, from_logits=True + ) + # Compute the per-sample loss only over the unmasked tokens + losses = tf.ragged.boolean_mask(losses, y_true != -100) + losses = tf.reduce_mean(losses, axis=-1) + return losses + + # endregion + + # region Metric + metric = load_metric("rouge") + # endregion + + # region Training + model.compile(loss={"logits": masked_sparse_categorical_crossentropy}, optimizer=optimizer) + + if training_args.do_train: + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {training_args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}") + logger.info(f" Total train batch size = {total_train_batch_size}") + logger.info(f" Total optimization steps = {num_train_steps}") + + model.fit( + tf_train_dataset, + epochs=int(training_args.num_train_epochs), + steps_per_epoch=num_update_steps_per_epoch, + ) + # endregion + + # region Validation + if data_args.val_max_target_length is None: + data_args.val_max_target_length = data_args.max_target_length + + gen_kwargs = { + "max_length": data_args.val_max_target_length if data_args is not None else config.max_length, + "num_beams": data_args.num_beams, + } + if training_args.do_eval: + logger.info("Evaluation...") + for batch, labels in tqdm( + tf_eval_dataset, total=len(eval_dataset) // training_args.per_device_eval_batch_size + ): + batch.update(gen_kwargs) + generated_tokens = model.generate(**batch) + if isinstance(generated_tokens, tuple): + generated_tokens = generated_tokens[0] + decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True) + labels = np.where(labels != -100, labels, tokenizer.pad_token_id) + decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) + decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) + + metric.add_batch(predictions=decoded_preds, references=decoded_labels) + + result = metric.compute(use_stemmer=True) + # Extract a few results from ROUGE + result = {key: value.mid.fmeasure * 100 for key, value in result.items()} + + result = {k: round(v, 4) for k, v in result.items()} + + logger.info(result) + # endregion + + if training_args.output_dir is not None: + model.save_pretrained(training_args.output_dir) + + +if __name__ == "__main__": + main()