780 lines
32 KiB
Python
780 lines
32 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 the library models for sequence to sequence.
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"""
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# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.
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import logging
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import os
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import sys
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from dataclasses import dataclass, field
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os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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from typing import Optional
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import numpy as np
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from datasets import load_dataset
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import transformers
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from transformers import (
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AutoConfig,
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AutoModelForSeq2SeqLM,
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AutoTokenizer,
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DataCollatorForSeq2Seq,
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HfArgumentParser,
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default_data_collator,
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set_seed
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)
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from transformers.trainer_utils import get_last_checkpoint, is_main_process
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from uie.extraction import constants
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from uie.extraction.record_schema import RecordSchema
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from uie.extraction.predict_parser import decoding_format_dict
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from uie.extraction.extraction_metrics import get_extract_metrics
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from uie.extraction.noiser.spot_asoc_noiser import SpotAsocNoiser
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from uie.extraction.dataset_processer import PrefixGenerator
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from uie.seq2seq.constrained_seq2seq import (
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ConstraintSeq2SeqTrainingArguments,
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ConstraintSeq2SeqTrainer,
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OriginalConstraintSeq2SeqTrainer,
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UIEPretrainConstraintSeq2SeqTrainer,
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UIEFinetuneConstraintSeq2SeqTrainer,
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MetaPretrainConstraintSeq2SeqTrainer,
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MetaFinetuneConstraintSeq2SeqTrainer,
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)
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from uie.seq2seq.data_collator import (
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DataCollatorForMetaSeq2Seq,
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DynamicSSIGenerator,
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)
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from uie.seq2seq.features import RecordFeature
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from uie.seq2seq.model import PromptSeq2SeqTransformer
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from uie.seq2seq.noise_record import create_noised_record
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import pdb
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logger = logging.getLogger(__name__)
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
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"""
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model_name_or_path: str = field(
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metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=False,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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use_auth_token: bool = field(
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default=False,
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metadata={
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"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
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"with private models)."
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},
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)
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from_checkpoint: bool = field(
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default=False, metadata={"help": "Whether load from checkpoint to continue learning"}
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)
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load_config_only: bool = field(
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default=False, metadata={"help": "Whether load model config only from checkpoint"}
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)
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use_prompt_tuning_model: bool = field(
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default=False, metadata={"help": "Whether use prompt tuning model"}
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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task: str = field(
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default="summarization",
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metadata={
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"help": "The name of the task, should be summarization (or summarization_{dataset} for evaluating "
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"pegasus) or translation (or translation_{xx}_to_{yy})."
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},
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)
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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text_column: Optional[str] = field(
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default='text',
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metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
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)
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record_column: Optional[str] = field(
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default='record',
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metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
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)
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train_file: Optional[str] = field(
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default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
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)
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validation_file: Optional[str] = field(
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default=None,
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metadata={
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"help": "An optional input evaluation data file to evaluate the metrics (rouge/sacreblue) on "
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"(a jsonlines or csv file)."
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},
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)
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test_file: Optional[str] = field(
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default=None,
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metadata={
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"help": "An optional input test data file to evaluate the metrics (rouge/sacreblue) on "
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"(a jsonlines or csv file)."
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},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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max_source_length: Optional[int] = field(
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default=1024,
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metadata={
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"help": "The maximum total input sequence length after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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max_target_length: Optional[int] = field(
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default=128,
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metadata={
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"help": "The maximum total sequence length for target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded."
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},
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)
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max_prefix_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum prefix length."
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},
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)
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val_max_target_length: Optional[int] = field(
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default=None,
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metadata={
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"help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
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"than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
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"This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
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"during ``evaluate`` and ``predict``."
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},
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)
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pad_to_max_length: bool = field(
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default=False,
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metadata={
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"help": "Whether to pad all samples to model maximum sentence length. "
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"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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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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},
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)
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max_val_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of validation examples to this "
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"value if set."
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},
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)
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max_test_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": "For debugging purposes or quicker training, truncate the number of test examples to this "
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"value if set."
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},
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)
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num_beams: Optional[int] = field(
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default=None,
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metadata={
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"help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
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"which is used during ``evaluate`` and ``predict``."
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},
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)
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ignore_pad_token_for_loss: bool = field(
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default=True,
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metadata={
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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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)
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source_prefix: Optional[str] = field(
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default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
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)
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meta_negative: int = field(
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default=-1, metadata={"help": "Negative Schema Number in Training."}
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)
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ordered_prompt: bool = field(
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default=True,
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metadata={
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"help": "Whether to sort the spot prompt and asoc prompt or not."
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},
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.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 self.validation_file is not None:
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extension = self.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 self.val_max_target_length is None:
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self.val_max_target_length = self.max_target_length
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decoding_format: str = field(
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default='tree',
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metadata={"help": "Decoding Format, valid in %s" % decoding_format_dict.keys()}
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)
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record_schema: str = field(
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default=None, metadata={"help": "The input event schema file."}
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)
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spot_noise: float = field(
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default=0., metadata={"help": "The noise rate of null spot."}
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)
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asoc_noise: float = field(
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default=0., metadata={"help": "The noise rate of null asoc."}
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)
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meta_positive_rate: float = field(
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default=1., metadata={"help": "The keep rate of positive spot."}
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)
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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, ConstraintSeq2SeqTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to overcome."
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)
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elif last_checkpoint is not None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Setup logging
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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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handlers=[logging.StreamHandler(sys.stdout)],
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)
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logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
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logger.info("Options:")
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logger.info(model_args)
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logger.info(data_args)
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logger.info(training_args)
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
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+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
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)
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# Set the verbosity to info of the Transformers logger (on main process only):
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if is_main_process(training_args.local_rank):
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transformers.utils.logging.set_verbosity_info()
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logger.info("Training/evaluation parameters %s", training_args)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Get the datasets: you can either provide your own CSV/JSON 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 in the summarization task, this script will use the first column for the full texts and the
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# second column for the summaries (unless you specify column names for this with the `text_column` and
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# `record_column` arguments).
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# For translation, only JSON files are supported, with one field named "translation" containing two keys for the
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# source and target languages (unless you adapt what follows).
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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 data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name)
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else:
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data_files = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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extension = data_args.train_file.split(".")[-1]
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if training_args.do_eval and data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = data_args.validation_file.split(".")[-1]
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if training_args.do_predict and data_args.test_file is not None:
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data_files["test"] = data_args.test_file
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extension = data_args.test_file.split(".")[-1]
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logger.info(data_files)
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datasets = load_dataset("uie_json.py", data_files=data_files, block_size=(10<<22))
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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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logger.info(datasets)
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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logger.info("Load Config: %s" % model_args.config_name if model_args.config_name else model_args.model_name_or_path)
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config = AutoConfig.from_pretrained(
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model_args.config_name if model_args.config_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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config.max_length = data_args.max_target_length
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
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cache_dir=model_args.cache_dir,
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use_fast=model_args.use_fast_tokenizer,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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)
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to_remove_token_list = list()
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if tokenizer.bos_token:
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to_remove_token_list += [tokenizer.bos_token]
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if tokenizer.eos_token:
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to_remove_token_list += [tokenizer.eos_token]
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if tokenizer.pad_token:
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to_remove_token_list += [tokenizer.pad_token]
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if model_args.use_prompt_tuning_model:
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MODEL = PromptSeq2SeqTransformer
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else:
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MODEL = AutoModelForSeq2SeqLM
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if model_args.load_config_only:
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model = MODEL.from_config(config)
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else:
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model = MODEL.from_pretrained(
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model_args.model_name_or_path,
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from_tf=bool(".ckpt" in model_args.model_name_or_path),
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config=config,
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cache_dir=model_args.cache_dir,
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revision=model_args.model_revision,
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use_auth_token=True if model_args.use_auth_token else None,
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mirror='tuna',
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)
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if training_args.do_train:
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to_add_special_token = list()
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for special_token in [constants.type_start, constants.type_end, constants.text_start, constants.span_start, constants.spot_prompt, constants.asoc_prompt]:
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if special_token not in tokenizer.get_vocab():
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to_add_special_token += [special_token]
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tokenizer.add_special_tokens(
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{"additional_special_tokens": tokenizer.special_tokens_map_extended['additional_special_tokens'] + to_add_special_token}
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)
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model.resize_token_embeddings(len(tokenizer))
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logger.info(tokenizer)
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# Set decoder_start_token_id
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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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if data_args.record_schema and os.path.exists(data_args.record_schema):
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record_schema = RecordSchema.read_from_file(data_args.record_schema)
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else:
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record_schema = None
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if data_args.source_prefix is not None:
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if data_args.source_prefix == 'schema':
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prefix = PrefixGenerator.get_schema_prefix(schema=record_schema)
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elif data_args.source_prefix.startswith('meta'):
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prefix = ""
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else:
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prefix = data_args.source_prefix
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else:
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prefix = ""
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logger.info(f"Prefix: {prefix}")
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logger.info(f"Prefix Length: {len(tokenizer.tokenize(prefix))}")
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# Preprocessing the datasets.
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# We need to tokenize inputs and targets.
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if training_args.do_train:
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column_names = datasets["train"].column_names
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elif training_args.do_eval:
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column_names = datasets["validation"].column_names
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elif training_args.do_predict:
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column_names = datasets["test"].column_names
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else:
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logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
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return
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# To serialize preprocess_function below, each of those four variables needs to be defined (even if we won't use
|
|
# them all).
|
|
|
|
text_column = data_args.text_column
|
|
record_column = data_args.record_column
|
|
logger.info('Using src: %s and tgt: %s' % (text_column, record_column))
|
|
|
|
# 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.error(
|
|
"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 = examples[text_column]
|
|
targets = examples[record_column]
|
|
inputs = [prefix + inp for inp in inputs]
|
|
model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)
|
|
|
|
model_inputs["text"] = inputs
|
|
|
|
# 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"] = [
|
|
[(_label if _label != tokenizer.pad_token_id else -100) for _label in label] for label in labels["input_ids"]
|
|
]
|
|
|
|
model_inputs["labels"] = labels["input_ids"]
|
|
|
|
# set noised record inputs
|
|
noised_record_list = []
|
|
for idx, noised_record in enumerate(examples["noised_record"]):
|
|
if noised_record is None:
|
|
tokens = examples["tokens"][idx]
|
|
entity_list = examples["entity"][idx]
|
|
triple_list = examples["relation"][idx]
|
|
event_list = examples["event"][idx]
|
|
|
|
noised_record = create_noised_record(tokens, entity_list, triple_list, event_list)
|
|
noised_record_list.append(noised_record)
|
|
model_inputs["noised_record"] = noised_record_list
|
|
# model_inputs["noised_record"] = examples["noised_record"]
|
|
|
|
# others
|
|
model_inputs['sample_prompt'] = [False] * len(model_inputs['input_ids'])
|
|
if data_args.source_prefix is not None and data_args.source_prefix.startswith('meta'):
|
|
model_inputs['spots'] = examples['spot']
|
|
model_inputs['asocs'] = examples['asoc']
|
|
model_inputs['spot_asoc'] = examples['spot_asoc']
|
|
# sample_prompt=True for Finetune and Pretrain
|
|
model_inputs['sample_prompt'] = [True] * len(model_inputs['input_ids'])
|
|
|
|
return model_inputs
|
|
|
|
def preprocess_function_eval(examples):
|
|
model_inputs = preprocess_function(examples)
|
|
# sample_prompt=False for evaluation
|
|
model_inputs['sample_prompt'] = [False] * len(model_inputs['input_ids'])
|
|
return model_inputs
|
|
|
|
def postprocess_text(x_str):
|
|
# Clean `bos` `eos` `pad` for cleaned text
|
|
for to_remove_token in to_remove_token_list:
|
|
x_str = x_str.replace(to_remove_token, '')
|
|
|
|
return x_str.strip()
|
|
|
|
logger.info("Start Data Preprocessing ...")
|
|
|
|
if training_args.do_train:
|
|
train_dataset = datasets["train"]
|
|
if data_args.max_train_samples is not None:
|
|
train_dataset = train_dataset.select(range(data_args.max_train_samples))
|
|
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,
|
|
features=RecordFeature,
|
|
)
|
|
|
|
if training_args.do_eval:
|
|
max_target_length = data_args.val_max_target_length
|
|
eval_dataset = datasets["validation"]
|
|
if data_args.max_val_samples is not None:
|
|
eval_dataset = eval_dataset.select(range(data_args.max_val_samples))
|
|
eval_dataset = eval_dataset.map(
|
|
preprocess_function_eval,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
features=RecordFeature,
|
|
)
|
|
|
|
if training_args.do_predict:
|
|
max_target_length = data_args.val_max_target_length
|
|
test_dataset = datasets["test"]
|
|
if data_args.max_test_samples is not None:
|
|
test_dataset = test_dataset.select(range(data_args.max_test_samples))
|
|
test_dataset = test_dataset.map(
|
|
preprocess_function_eval,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
features=RecordFeature,
|
|
)
|
|
|
|
logger.info("End Data Preprocessing ...")
|
|
|
|
# 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
|
|
elif data_args.source_prefix.startswith('meta'):
|
|
|
|
if data_args.spot_noise > 0 or data_args.asoc_noise > 0:
|
|
if data_args.decoding_format == 'spotasoc':
|
|
spot_asoc_nosier = SpotAsocNoiser(
|
|
spot_noise_ratio=data_args.spot_noise,
|
|
asoc_noise_ratio=data_args.asoc_noise,
|
|
null_span=constants.null_span,
|
|
)
|
|
else:
|
|
raise NotImplementedError(
|
|
"decoding_format `spotasoc` is not implemented."
|
|
)
|
|
else:
|
|
spot_asoc_nosier = None
|
|
|
|
data_collator = DataCollatorForMetaSeq2Seq(
|
|
tokenizer,
|
|
model=model,
|
|
label_pad_token_id=label_pad_token_id,
|
|
pad_to_multiple_of=8 if training_args.fp16 else None,
|
|
max_length=data_args.max_source_length,
|
|
max_prefix_length=data_args.max_prefix_length,
|
|
max_target_length=data_args.max_target_length,
|
|
negative_sampler=DynamicSSIGenerator(
|
|
tokenizer=tokenizer,
|
|
schema=record_schema,
|
|
positive_rate=data_args.meta_positive_rate,
|
|
negative=data_args.meta_negative,
|
|
ordered_prompt=data_args.ordered_prompt,
|
|
),
|
|
spot_asoc_nosier=spot_asoc_nosier,
|
|
decoding_format=data_args.decoding_format,
|
|
)
|
|
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,
|
|
)
|
|
|
|
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=False, clean_up_tokenization_spaces=False)
|
|
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=False, clean_up_tokenization_spaces=False)
|
|
|
|
decoded_preds = [postprocess_text(x) for x in decoded_preds]
|
|
decoded_labels = [postprocess_text(x) for x in decoded_labels]
|
|
|
|
result = get_extract_metrics(
|
|
pred_lns=decoded_preds,
|
|
tgt_lns=decoded_labels,
|
|
label_constraint=record_schema,
|
|
decoding_format=data_args.decoding_format,
|
|
)
|
|
|
|
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
|
|
if training_args.trainer_type == "uie_pretrain":
|
|
TRAINER = UIEPretrainConstraintSeq2SeqTrainer
|
|
elif training_args.trainer_type == "uie_finetune":
|
|
TRAINER = UIEFinetuneConstraintSeq2SeqTrainer
|
|
elif training_args.trainer_type == "meta_pretrain":
|
|
TRAINER = MetaPretrainConstraintSeq2SeqTrainer
|
|
elif training_args.trainer_type == "meta_finetune":
|
|
TRAINER = MetaFinetuneConstraintSeq2SeqTrainer
|
|
else:
|
|
TRAINER = OriginalConstraintSeq2SeqTrainer
|
|
|
|
trainer = TRAINER(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=train_dataset if training_args.do_train else None,
|
|
eval_dataset=eval_dataset if training_args.do_eval else None,
|
|
tokenizer=tokenizer,
|
|
data_collator=data_collator,
|
|
compute_metrics=compute_metrics if training_args.predict_with_generate else None,
|
|
decoding_type_schema=record_schema,
|
|
decoding_format=data_args.decoding_format,
|
|
source_prefix=prefix,
|
|
task=data_args.task,
|
|
)
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
if model_args.from_checkpoint:
|
|
if last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
elif os.path.isdir(model_args.model_name_or_path):
|
|
checkpoint = model_args.model_name_or_path
|
|
else:
|
|
checkpoint = None
|
|
else:
|
|
checkpoint = None
|
|
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
trainer.save_model() # Saves the tokenizer too for easy upload
|
|
|
|
output_train_file = os.path.join(training_args.output_dir, "train_results.txt")
|
|
if trainer.is_world_process_zero():
|
|
with open(output_train_file, "w") as writer:
|
|
logger.info("***** Train results *****")
|
|
for key, value in sorted(train_result.metrics.items()):
|
|
logger.info(f" {key} = {value}")
|
|
writer.write(f"{key} = {value}\n")
|
|
|
|
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
|
|
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json"))
|
|
|
|
# Evaluation
|
|
results = {}
|
|
if training_args.do_eval:
|
|
logger.info("*** Evaluate ***")
|
|
|
|
results = trainer.evaluate(max_length=data_args.val_max_target_length, num_beams=data_args.num_beams)
|
|
results = {k: round(v, 4) for k, v in results.items()}
|
|
|
|
eval_results = trainer.predict(
|
|
eval_dataset,
|
|
metric_key_prefix="eval",
|
|
max_length=data_args.val_max_target_length,
|
|
num_beams=data_args.num_beams,
|
|
)
|
|
|
|
output_eval_file = os.path.join(training_args.output_dir, "eval_results_seq2seq.txt")
|
|
if trainer.is_world_process_zero():
|
|
with open(output_eval_file, "w") as writer:
|
|
logger.info("***** Eval results *****")
|
|
for key, value in sorted(results.items()):
|
|
logger.info(f" {key} = {value}")
|
|
writer.write(f"{key} = {value}\n")
|
|
|
|
if training_args.predict_with_generate:
|
|
eval_preds = tokenizer.batch_decode(
|
|
eval_results.predictions, skip_special_tokens=False, clean_up_tokenization_spaces=False
|
|
)
|
|
eval_preds = [postprocess_text(pred) for pred in eval_preds]
|
|
output_test_preds_file = os.path.join(training_args.output_dir, "eval_preds_seq2seq.txt")
|
|
with open(output_test_preds_file, "w") as writer:
|
|
writer.write("\n".join(eval_preds))
|
|
|
|
if training_args.do_predict:
|
|
logger.info("*** Test ***")
|
|
|
|
test_results = trainer.predict(
|
|
test_dataset,
|
|
metric_key_prefix="test",
|
|
max_length=data_args.val_max_target_length,
|
|
num_beams=data_args.num_beams,
|
|
)
|
|
test_metrics = test_results.metrics
|
|
test_metrics["test_loss"] = round(test_metrics["test_loss"], 4)
|
|
|
|
output_test_result_file = os.path.join(training_args.output_dir, "test_results_seq2seq.txt")
|
|
if trainer.is_world_process_zero():
|
|
with open(output_test_result_file, "w") as writer:
|
|
logger.info("***** Test results *****")
|
|
for key, value in sorted(test_metrics.items()):
|
|
logger.info(f" {key} = {value}")
|
|
writer.write(f"{key} = {value}\n")
|
|
|
|
if training_args.predict_with_generate:
|
|
test_preds = tokenizer.batch_decode(
|
|
test_results.predictions, skip_special_tokens=False, clean_up_tokenization_spaces=False
|
|
)
|
|
test_preds = [postprocess_text(pred) for pred in test_preds]
|
|
output_test_preds_file = os.path.join(training_args.output_dir, "test_preds_seq2seq.txt")
|
|
with open(output_test_preds_file, "w") as writer:
|
|
writer.write("\n".join(test_preds))
|
|
|
|
return results
|
|
|
|
|
|
def _mp_fn(index):
|
|
# For xla_spawn (TPUs)
|
|
main()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|