diff --git a/run_translation_modified.py b/run_translation_modified.py new file mode 100644 index 0000000..3de4e55 --- /dev/null +++ b/run_translation_modified.py @@ -0,0 +1,694 @@ +#!/usr/bin/env python +# coding=utf-8 +# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +""" +Fine-tuning the library models for sequence to sequence. +""" +# 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 collections import defaultdict +from dataclasses import dataclass, field +from typing import Optional + +import datasets +import numpy as np +import torch +from datasets import load_dataset + +import evaluate +import transformers +from transformers import ( + AutoConfig, + AutoModelForSeq2SeqLM, + AutoTokenizer, + DataCollatorForSeq2Seq, + HfArgumentParser, + M2M100Tokenizer, + MBart50Tokenizer, + MBart50TokenizerFast, + MBartTokenizer, + MBartTokenizerFast, + Seq2SeqTrainer, + Seq2SeqTrainingArguments, + 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("4.23.0") + +require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt") + +logger = logging.getLogger(__name__) + +# A list of all multilingual tokenizer which require src_lang and tgt_lang attributes. +MULTILINGUAL_TOKENIZERS = [MBartTokenizer, MBartTokenizerFast, MBart50Tokenizer, MBart50TokenizerFast, M2M100Tokenizer] + + +MAP_CLASSIFICATION_LABEL = {'positive': 1, 'negative': 0} + + +@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 `huggingface-cli login` (necessary to use this script " + "with private models)." + ) + }, + ) + freeze_weights: bool = field( + default=False, + metadata={"help": "Freeze encoder weights"}, + ) + + +@dataclass +class DataTrainingArguments: + """ + Arguments pertaining to what data we are going to input our model for training and eval. + """ + + source_lang: str = field(default=None, metadata={"help": "Source language id for translation."}) + target_lang: str = field(default=None, metadata={"help": "Target language id for translation."}) + + 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)."} + ) + train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a jsonlines)."}) + validation_file: Optional[str] = field( + default=None, + metadata={ + "help": "An optional input evaluation data file to evaluate the metrics (sacrebleu) on a jsonlines file." + }, + ) + test_file: Optional[str] = field( + default=None, + metadata={"help": "An optional input test data file to evaluate the metrics (sacrebleu) on a jsonlines 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)."} + ) + forced_bos_token: Optional[str] = field( + default=None, + metadata={ + "help": ( + "The token to force as the first generated token after the :obj:`decoder_start_token_id`.Useful for" + " multilingual models like :doc:`mBART <../model_doc/mbart>` where the first generated token needs to" + " be the target language token.(Usually it is the target language token)" + ) + }, + ) + + 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.") + elif self.source_lang is None or self.target_lang is None: + raise ValueError("Need to specify the source language and the target language.") + + # accepting both json and jsonl file extensions, as + # many jsonlines files actually have a .json extension + valid_extensions = ["json", "jsonl"] + + if self.train_file is not None: + extension = self.train_file.split(".")[-1] + assert extension in valid_extensions, "`train_file` should be a jsonlines file." + if self.validation_file is not None: + extension = self.validation_file.split(".")[-1] + assert extension in valid_extensions, "`validation_file` should be a jsonlines file." + if self.val_max_target_length is None: + self.val_max_target_length = self.max_target_length + + +def freeze_model_weights(model: torch.nn.Module) -> None: + for param in model.parameters(): + param.requires_grad = False + + +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, Seq2SeqTrainingArguments)) + 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() + + # 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_translation", 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)], + ) + + 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: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}" + ) + logger.info(f"Training/evaluation parameters {training_args}") + + 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 expected, e.g. with " + "`--source_prefix 'translate English to German: ' `" + ) + + # 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 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 translation, only JSON files are supported, with one field named "translation" containing two keys for the + # source and target languages (unless you adapt what follows). + # + # 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, + use_auth_token=True if model_args.use_auth_token else None, + ) + 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, + use_auth_token=True if model_args.use_auth_token else None, + ) + # 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 = 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, + ) + model = AutoModelForSeq2SeqLM.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, + ) + + if model_args.freeze_weights: + logger.info("Freezing encoder weights") + freeze_model_weights(model.encoder) + + model.resize_token_embeddings(len(tokenizer)) + + # Set decoder_start_token_id + if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)): + if isinstance(tokenizer, MBartTokenizer): + model.config.decoder_start_token_id = tokenizer.lang_code_to_id[data_args.target_lang] + else: + model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(data_args.target_lang) + + if model.config.decoder_start_token_id is None: + raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined") + + prefix = data_args.source_prefix if data_args.source_prefix is not None else "" + if 'classification' not in prefix: + raise RuntimeError('Not found "classification" prefix!') + prefix = prefix.strip() + if not prefix.endswith(':'): + prefix += ':' + prefix += ' ' + logger.info(f'Using translation prefix: "{prefix}"') + + # Preprocessing the datasets. + # 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 + elif training_args.do_predict: + column_names = raw_datasets["test"].column_names + else: + logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.") + return + + # For translation we set the codes of our source and target languages (only useful for mBART, the others will + # ignore those attributes). + if isinstance(tokenizer, tuple(MULTILINGUAL_TOKENIZERS)): + assert data_args.target_lang is not None and data_args.source_lang is not None, ( + f"{tokenizer.__class__.__name__} is a multilingual tokenizer which requires --source_lang and " + "--target_lang arguments." + ) + + tokenizer.src_lang = 'text' + tokenizer.tgt_lang = 'label' + + # For multilingual translation models like mBART-50 and M2M100 we need to force the target language token + # as the first generated token. We ask the user to explicitly provide this as --forced_bos_token argument. + forced_bos_token_id = ( + tokenizer.lang_code_to_id[data_args.forced_bos_token] if data_args.forced_bos_token is not None else None + ) + model.config.forced_bos_token_id = forced_bos_token_id + + # Get the language codes for input/target. + source_lang = 'text' + target_lang = 'label' + + # 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 + + if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"): + logger.warning( + "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for" + f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory" + ) + + def preprocess_function(examples): + inputs = [ex for ex in examples[source_lang]] + targets = [ex for ex in examples[target_lang]] + inputs = [prefix + inp for inp in inputs] + model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True) + + # Tokenize targets with the `text_target` keyword argument + labels = tokenizer(text_target=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: + max_train_samples = min(len(train_dataset), data_args.max_train_samples) + train_dataset = train_dataset.select(range(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", + ) + + 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: + max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples) + label_to_indexes = defaultdict(list) + for index, eval_sample in enumerate(eval_dataset): + label_to_indexes[eval_sample['label']].append(index) + max_samples_per_label = int(max_eval_samples / len(label_to_indexes)) + eval_sample_indexes = [] + for label, indexes in label_to_indexes.items(): + eval_sample_indexes.extend(indexes[:max_samples_per_label]) + logger.info(f"Set {max_samples_per_label} samples for {label}-class") + eval_sample_indexes.sort() + eval_dataset = eval_dataset.select(eval_sample_indexes) + 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", + ) + + if training_args.do_predict: + max_target_length = data_args.val_max_target_length + 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)) + with training_args.main_process_first(desc="prediction dataset map pre-processing"): + predict_dataset = predict_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 prediction dataset", + ) + + # Data collator + label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id + if data_args.pad_to_max_length: + data_collator = default_data_collator + else: + data_collator = DataCollatorForSeq2Seq( + tokenizer, + model=model, + label_pad_token_id=label_pad_token_id, + pad_to_multiple_of=8 if training_args.fp16 else None, + ) + + # Metric + metric = evaluate.load("sacrebleu") + metric_accuracy = evaluate.load("accuracy") + + def postprocess_text(preds, labels): + preds = [pred.strip() for pred in preds] + labels = [[label.strip()] for label in labels] + + return preds, labels + + def compute_metrics(eval_preds): + preds, labels = eval_preds + if isinstance(preds, tuple): + preds = preds[0] + decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True) + if data_args.ignore_pad_token_for_loss: + # Replace -100 in the labels as we can't decode them. + labels = np.where(labels != -100, labels, tokenizer.pad_token_id) + decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True) + + # Some simple post-processing + decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels) + decoded_preds_accuracy = [MAP_CLASSIFICATION_LABEL.get(decoded_pred, -1) for decoded_pred in decoded_preds] + decoded_labels_accuracy = [MAP_CLASSIFICATION_LABEL.get(decoded_label[0], -1) for decoded_label in decoded_labels] + + result = metric.compute(predictions=decoded_preds, references=decoded_labels) + result_accuracy = metric_accuracy.compute(predictions=decoded_preds_accuracy, references=decoded_labels_accuracy) + result = {"bleu": result["score"], "accuracy": result_accuracy["accuracy"]} + + prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds] + result["gen_len"] = np.mean(prediction_lens) + result = {k: round(v, 4) for k, v in result.items()} + return result + + # Initialize our Trainer + trainer = Seq2SeqTrainer( + 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, + tokenizer=tokenizer, + data_collator=data_collator, + compute_metrics=compute_metrics if training_args.predict_with_generate else None, + ) + + # 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) + trainer.save_model() # Saves the tokenizer too for easy upload + + 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.log_metrics("train", metrics) + trainer.save_metrics("train", metrics) + trainer.save_state() + + # Evaluation + results = {} + max_length = ( + training_args.generation_max_length + if training_args.generation_max_length is not None + else data_args.val_max_target_length + ) + num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams + if training_args.do_eval: + logger.info("*** Evaluate ***") + + metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval") + 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 ***") + + predict_results = trainer.predict( + predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams + ) + metrics = predict_results.metrics + max_predict_samples = ( + data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset) + ) + metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset)) + + trainer.log_metrics("predict", metrics) + trainer.save_metrics("predict", metrics) + + if trainer.is_world_process_zero(): + if training_args.predict_with_generate: + predictions = tokenizer.batch_decode( + predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True + ) + predictions = [pred.strip() for pred in predictions] + output_prediction_file = os.path.join(training_args.output_dir, "generated_predictions.txt") + with open(output_prediction_file, "w", encoding="utf-8") as writer: + writer.write("\n".join(predictions)) + + kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "translation"} + if data_args.dataset_name is not None: + kwargs["dataset_tags"] = data_args.dataset_name + if data_args.dataset_config_name is not None: + kwargs["dataset_args"] = data_args.dataset_config_name + kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" + else: + kwargs["dataset"] = data_args.dataset_name + + languages = [l for l in [data_args.source_lang, data_args.target_lang] if l is not None] + if len(languages) > 0: + kwargs["language"] = languages + + if training_args.push_to_hub: + trainer.push_to_hub(**kwargs) + else: + trainer.create_model_card(**kwargs) + + return results + + +def _mp_fn(index): + # For xla_spawn (TPUs) + main() + + +if __name__ == "__main__": + main()