680 lines
27 KiB
Python
680 lines
27 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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Fine-tuning a 🤗 Transformers model on text translation.
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"""
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# You can also adapt this script on your own text translation task. Pointers for this are left as comments.
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import argparse
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import logging
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import math
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import os
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import random
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from pathlib import Path
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from typing import Any, Dict, Union
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import datasets
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import numpy as np
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import torch
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from datasets import load_dataset, load_metric, Metric
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from torch.utils.data import DataLoader
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from torch import nn
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from tqdm.auto import tqdm
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import transformers
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from accelerate import Accelerator
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from huggingface_hub import Repository
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from transformers import (
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CONFIG_MAPPING,
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MODEL_MAPPING,
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AdamW,
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AutoConfig,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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DataCollatorForSeq2Seq,
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MBartTokenizer,
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MBartTokenizerFast,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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set_seed,
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T5ForConditionalGeneration, PreTrainedTokenizerFast,
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)
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from transformers.file_utils import get_full_repo_name
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from transformers.utils.versions import require_version
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logger = logging.getLogger(__name__)
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/translation/requirements.txt")
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# You should update this to your particular problem to have better documentation of `model_type`
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MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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MAP_CLASSIFICATION_LABEL = {'positive': 1, 'negative': 0}
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MODEL_TYPE = Union[T5ForConditionalGeneration]
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# Parsing input arguments
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def parse_args():
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parser = argparse.ArgumentParser(description="Finetune a transformers model on a text classification task")
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help="The name of the dataset to use (via the datasets library).",
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)
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parser.add_argument(
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"--predict_with_generate",
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type=bool,
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default=True,
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help="",
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The configuration name of the dataset to use (via the datasets library).",
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)
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parser.add_argument(
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"--train_file", type=str, default=None, help="A csv or a json file containing the training data."
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)
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parser.add_argument(
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"--num_beams",
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type=int,
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default=None,
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help="Number of beams to use for evaluation. This argument will be "
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"passed to ``model.generate``, which is used during ``evaluate`` and ``predict``.",
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)
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parser.add_argument(
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"--max_source_length",
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type=int,
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default=1024,
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help="The maximum total input sequence length after "
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"tokenization.Sequences longer than this will be truncated, sequences shorter will be padded.",
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)
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parser.add_argument(
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"--max_target_length",
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type=int,
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default=128,
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help="The maximum total sequence length for target text after "
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"tokenization. Sequences longer than this will be truncated, sequences shorter will be padded."
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"during ``evaluate`` and ``predict``.",
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)
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parser.add_argument(
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"--val_max_target_length",
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type=int,
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default=None,
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help="The maximum total sequence length for validation "
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"target text after tokenization.Sequences longer than this will be truncated, sequences shorter will be "
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"padded. Will default to `max_target_length`.This argument is also used to override the ``max_length`` "
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"param of ``model.generate``, which is used during ``evaluate`` and ``predict``.",
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)
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parser.add_argument(
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"--pad_to_max_length",
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type=bool,
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default=False,
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help="Whether to pad all samples to model maximum sentence "
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"length. If False, will pad the samples dynamically when batching to the maximum length in the batch. More"
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"efficient on GPU but very bad for TPU.",
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)
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parser.add_argument(
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"--validation_file", type=str, default=None, help="A csv or a json file containing the validation data."
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)
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parser.add_argument(
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"--test_file", type=str, default=None, help="A csv or a json file containing the test data."
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)
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parser.add_argument(
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"--ignore_pad_token_for_loss",
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type=bool,
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default=True,
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help="Whether to ignore the tokens corresponding to " "padded labels in the loss computation or not.",
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)
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parser.add_argument("--source_lang", type=str, default=None, help="Source language id for translation.")
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parser.add_argument("--target_lang", type=str, default=None, help="Target language id for translation.")
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parser.add_argument(
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"--source_prefix",
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type=str,
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default=None,
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help="A prefix to add before every source text " "(useful for T5 models).",
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)
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parser.add_argument(
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"--preprocessing_num_workers",
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type=int,
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default=None,
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help="The number of processes to use for the preprocessing.",
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)
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parser.add_argument(
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"--overwrite_cache", type=bool, default=None, help="Overwrite the cached training and evaluation sets"
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)
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parser.add_argument(
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"--max_length",
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type=int,
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default=128,
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help=(
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"The maximum total input sequence length after tokenization. Sequences longer than this will be truncated,"
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" sequences shorter will be padded if `--pad_to_max_lengh` is passed."
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),
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)
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parser.add_argument(
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"--model_name_or_path",
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type=str,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=True,
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)
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parser.add_argument(
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"--freeze_encoder",
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action="store_true",
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help="If passed, encoder weight will be frozen.",
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)
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parser.add_argument(
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"--config_name",
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type=str,
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default=None,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--use_slow_tokenizer",
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action="store_true",
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help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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)
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
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)
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parser.add_argument(
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"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
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parser.add_argument(
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"--model_type",
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type=str,
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default=None,
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help="Model type to use if training from scratch.",
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choices=MODEL_TYPES,
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)
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument(
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"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
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)
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parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
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args = parser.parse_args()
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# Sanity checks
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if args.dataset_name is None and args.train_file is None and args.validation_file is None:
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raise ValueError("Need either a task name or a training/validation file.")
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if args.train_file is not None:
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extension = args.train_file.split(".")[-1]
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assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
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if args.validation_file is not None:
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extension = args.validation_file.split(".")[-1]
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assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
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if args.push_to_hub:
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assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed."
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return args
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def postprocess_text(preds, labels):
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preds = [pred.strip() for pred in preds]
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labels = [[label.strip()] for label in labels]
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return preds, labels
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def evaluate(
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model: MODEL_TYPE,
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tokenizer: PreTrainedTokenizerFast,
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dataloader: DataLoader,
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accelerator: Accelerator,
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metric: Metric,
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metric_acc: Metric,
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logger_msg: str,
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args: argparse.Namespace,
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generate_kwargs: Dict[str, Any]
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) -> None:
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for step, batch in enumerate(dataloader):
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with torch.no_grad():
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generated_tokens = accelerator.unwrap_model(model).generate(
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batch["input_ids"],
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attention_mask=batch["attention_mask"],
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**generate_kwargs,
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)
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generated_tokens = accelerator.pad_across_processes(
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generated_tokens, dim=1, pad_index=tokenizer.pad_token_id
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)
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labels = batch["labels"]
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if not args.pad_to_max_length:
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# If we did not pad to max length, we need to pad the labels too
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labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
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generated_tokens = accelerator.gather(generated_tokens).cpu().numpy()
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labels = accelerator.gather(labels).cpu().numpy()
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if args.ignore_pad_token_for_loss:
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# Replace -100 in the labels as we can't decode them.
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labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
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decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
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decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
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decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
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decoded_preds_acc = [MAP_CLASSIFICATION_LABEL.get(decoded_pred, -1) for decoded_pred in decoded_preds]
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decoded_labels_acc = [MAP_CLASSIFICATION_LABEL.get(decoded_label[0], -1) for decoded_label in
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decoded_labels]
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metric.add_batch(predictions=decoded_preds, references=decoded_labels)
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metric_acc.add_batch(
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predictions=accelerator.gather(decoded_preds_acc),
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references=accelerator.gather(decoded_labels_acc),
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)
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eval_metric = metric.compute()
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eval_metric_acc = metric_acc.compute()
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logger.info(f'{logger_msg} | bleu: {eval_metric["score"]} | accuracy: {eval_metric_acc["accuracy"]}')
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def freeze_model(model: nn.Module) -> None:
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for param in model.parameters():
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param.requires_grad = False
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def unfreeze_model(model: nn.Module) -> None:
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for param in model.parameters():
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param.requires_grad = True
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def main():
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# Parse the arguments
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args = parse_args()
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# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
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accelerator = Accelerator()
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state)
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# Setup logging, we only want one process per machine to log things on the screen.
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# accelerator.is_local_main_process is only True for one process per machine.
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logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
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if accelerator.is_local_main_process:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.push_to_hub:
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if args.hub_model_id is None:
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repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
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else:
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repo_name = args.hub_model_id
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repo = Repository(args.output_dir, clone_from=repo_name)
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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accelerator.wait_for_everyone()
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(args.dataset_name, args.dataset_config_name)
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else:
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data_files = {}
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if args.train_file is not None:
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data_files["train"] = args.train_file
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if args.validation_file is not None:
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data_files["validation"] = args.validation_file
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if args.test_file is not None:
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data_files["test"] = args.test_file
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extension = args.train_file.split(".")[-1]
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raw_datasets = load_dataset(extension, data_files=data_files)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.html.
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# Load pretrained model and tokenizer
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#
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# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if args.config_name:
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config = AutoConfig.from_pretrained(args.model_name_or_path)
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elif args.model_name_or_path:
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config = AutoConfig.from_pretrained(args.model_name_or_path)
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else:
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config = CONFIG_MAPPING[args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=not args.use_slow_tokenizer)
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elif args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(args.model_name_or_path, use_fast=not args.use_slow_tokenizer)
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else:
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raise ValueError(
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"You are instantiating a new tokenizer from scratch. This is not supported by this script."
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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if args.model_name_or_path:
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model = AutoModelForSeq2SeqLM.from_pretrained(
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args.model_name_or_path,
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from_tf=bool(".ckpt" in args.model_name_or_path),
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config=config,
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)
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else:
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logger.info("Training new model from scratch")
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model = AutoModelForSeq2SeqLM.from_config(config)
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model.resize_token_embeddings(len(tokenizer))
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if args.freeze_encoder:
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logger.info('Freezing model weights')
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if isinstance(model, T5ForConditionalGeneration):
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freeze_model(model.encoder)
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# Uncomment to unfreeze shared embedding layer
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# unfreeze_model(model.shared)
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else:
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raise RuntimeError(f'Cannot freeze weights for {model.__class__.__name__}')
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# Set decoder_start_token_id
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if model.config.decoder_start_token_id is None and isinstance(tokenizer, (MBartTokenizer, MBartTokenizerFast)):
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assert (
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args.target_lang is not None and args.source_lang is not None
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), "mBart requires --target_lang and --source_lang"
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if isinstance(tokenizer, MBartTokenizer):
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model.config.decoder_start_token_id = tokenizer.lang_code_to_id[args.target_lang]
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else:
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model.config.decoder_start_token_id = tokenizer.convert_tokens_to_ids(args.target_lang)
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if model.config.decoder_start_token_id is None:
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raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
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prefix = args.source_prefix if args.source_prefix is not None else ""
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if 'classification' not in prefix:
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raise RuntimeError('Not found "classification" prefix!')
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prefix = prefix.strip()
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if not prefix.endswith(':'):
|
|
prefix += ':'
|
|
prefix += ' '
|
|
logger.info(f'Using translation prefix: "{prefix}"')
|
|
|
|
# Preprocessing the datasets.
|
|
# First we tokenize all the texts.
|
|
column_names = raw_datasets["train"].column_names
|
|
|
|
# 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, (MBartTokenizer, MBartTokenizerFast)):
|
|
if args.source_lang is not None:
|
|
tokenizer.src_lang = args.source_lang
|
|
if args.target_lang is not None:
|
|
tokenizer.tgt_lang = args.target_lang
|
|
|
|
# Get the language codes for input/target.
|
|
source_lang = 'tweet'
|
|
target_lang = 'label'
|
|
|
|
padding = "max_length" if args.pad_to_max_length else False
|
|
|
|
# Temporarily set max_target_length for training.
|
|
max_target_length = args.max_target_length
|
|
padding = "max_length" if args.pad_to_max_length else False
|
|
|
|
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=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 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
|
|
|
|
with accelerator.main_process_first():
|
|
processed_datasets = raw_datasets.map(
|
|
preprocess_function,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
|
|
train_dataset = processed_datasets["train"]
|
|
eval_dataset = processed_datasets["validation"]
|
|
test_dataset = processed_datasets["test"]
|
|
|
|
# Log a few random samples from the training set:
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
|
|
# DataLoaders creation:
|
|
label_pad_token_id = -100 if args.ignore_pad_token_for_loss else tokenizer.pad_token_id
|
|
if args.pad_to_max_length:
|
|
# If padding was already done ot max length, we use the default data collator that will just convert everything
|
|
# to tensors.
|
|
data_collator = default_data_collator
|
|
else:
|
|
# Otherwise, `DataCollatorWithPadding` will apply dynamic padding for us (by padding to the maximum length of
|
|
# the samples passed). When using mixed precision, we add `pad_to_multiple_of=8` to pad all tensors to multiple
|
|
# of 8s, which will enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
|
|
data_collator = DataCollatorForSeq2Seq(
|
|
tokenizer,
|
|
model=model,
|
|
label_pad_token_id=label_pad_token_id,
|
|
pad_to_multiple_of=8 if accelerator.use_fp16 else None,
|
|
)
|
|
|
|
train_dataloader = DataLoader(
|
|
train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size
|
|
)
|
|
eval_dataloader = DataLoader(eval_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
|
|
test_dataloader = DataLoader(test_dataset, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size)
|
|
|
|
# Optimizer
|
|
# Split weights in two groups, one with weight decay and the other not.
|
|
no_decay = ["bias", "LayerNorm.weight"]
|
|
optimizer_grouped_parameters = [
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
|
"weight_decay": args.weight_decay,
|
|
},
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
|
"weight_decay": 0.0,
|
|
},
|
|
]
|
|
optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
model, optimizer, train_dataloader, eval_dataloader, test_dataloader = accelerator.prepare(
|
|
model, optimizer, train_dataloader, eval_dataloader, test_dataloader
|
|
)
|
|
|
|
# Note -> the training dataloader needs to be prepared before we grab his length below (cause its length will be
|
|
# shorter in multiprocess)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
else:
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
lr_scheduler = get_scheduler(
|
|
name=args.lr_scheduler_type,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.num_warmup_steps,
|
|
num_training_steps=args.max_train_steps,
|
|
)
|
|
|
|
metric = load_metric("sacrebleu")
|
|
metric_acc = load_metric("accuracy")
|
|
|
|
def postprocess_text(preds, labels):
|
|
preds = [pred.strip() for pred in preds]
|
|
labels = [[label.strip()] for label in labels]
|
|
|
|
return preds, labels
|
|
|
|
# Train!
|
|
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
completed_steps = 0
|
|
|
|
for epoch in range(args.num_train_epochs):
|
|
model.train()
|
|
for step, batch in enumerate(train_dataloader):
|
|
outputs = model(**batch)
|
|
loss = outputs.loss
|
|
loss = loss / args.gradient_accumulation_steps
|
|
accelerator.backward(loss)
|
|
if step % args.gradient_accumulation_steps == 0 or step == len(train_dataloader) - 1:
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
progress_bar.update(1)
|
|
completed_steps += 1
|
|
|
|
if completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
model.eval()
|
|
|
|
if args.val_max_target_length is None:
|
|
args.val_max_target_length = args.max_target_length
|
|
|
|
gen_kwargs = {
|
|
"max_length": args.val_max_target_length if args is not None else config.max_length,
|
|
"num_beams": args.num_beams,
|
|
}
|
|
evaluate(model, tokenizer, eval_dataloader, accelerator, metric, metric_acc, 'Validation-set', args, gen_kwargs)
|
|
|
|
if args.push_to_hub and epoch < args.num_train_epochs - 1:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
repo.push_to_hub(commit_message=f"Training in progress epoch {epoch}", blocking=False)
|
|
|
|
gen_kwargs = {
|
|
"max_length": args.val_max_target_length if args is not None else config.max_length,
|
|
"num_beams": args.num_beams,
|
|
}
|
|
evaluate(model, tokenizer, test_dataloader, accelerator, metric, metric_acc, 'Test-set', args, gen_kwargs)
|
|
|
|
if args.output_dir is not None:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(args.output_dir, save_function=accelerator.save)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
if args.push_to_hub:
|
|
repo.push_to_hub(commit_message="End of training")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
main() |