from huggingface_hub import Repository
from transformers import (
CONFIG_MAPPING,
MODEL_MAPPING,
AutoConfig,
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
SchedulerType,
get_scheduler,
)
from transformers.utils import check_min_version, get_full_repo_name, is_offline_mode, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.22.0.dev0")
logger = get_logger(__name__)
require_version(
"datasets>=1.8.0",
"To fix: pip install -r examples/pytorch/summarization/requirements.txt")
# You should update this to your particular problem to have better documentation of `model_type`
MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizerFast,
TFAutoModelForQuestionAnswering,
TFTrainingArguments,
set_seed,
)
from transformers.file_utils import CONFIG_NAME, TF2_WEIGHTS_NAME
from transformers.utils import check_min_version
from utils_qa import postprocess_qa_predictions
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.16.0.dev0")
logger = logging.getLogger(__name__)
# region Arguments
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
model_name_or_path: str = field(
metadata={
"help":
"Path to pretrained model or model identifier from huggingface.co/models"
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
PretrainedConfig,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.5.0")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
logger = logging.getLogger(__name__)
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForLanguageModeling,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.10.0")
require_version(
"datasets>=1.8.0",
"To fix: pip install -r examples/pytorch/language-modeling/requirements.txt",
)
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
# added these lines
import config_train
import configuration
config_repository = configuration.Config()
# end lines
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.6.0")
logger = logging.getLogger(__name__)
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
import transformers
from transformers import ( # Trainer,; TrainingArguments,
AutoConfig, AutoModelForSequenceClassification, AutoTokenizer,
DataCollatorWithPadding, EvalPrediction, HfArgumentParser,
PretrainedConfig, default_data_collator, set_seed,
)
# Will import SageMaker Model parallelism specific Trainer
from transformers.sagemaker import SageMakerTrainer as Trainer
from transformers.sagemaker import SageMakerTrainingArguments as TrainingArguments
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.4.2")
task_to_keys = {
"cola": ("sentence", None),
"mnli": ("premise", "hypothesis"),
"mrpc": ("sentence1", "sentence2"),
"qnli": ("question", "sentence"),
"qqp": ("question1", "question2"),
"rte": ("sentence1", "sentence2"),
"sst2": ("sentence", None),
"stsb": ("sentence1", "sentence2"),
"wnli": ("sentence1", "sentence2"),
}
logger = logging.getLogger(__name__)
AutoModelForSeq2SeqLM,
AutoTokenizer,
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainer,
Seq2SeqTrainingArguments,
default_data_collator,
set_seed,
)
from transformers.file_utils import is_offline_mode
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.4.0")
logger = logging.getLogger(__name__)
try:
nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
if is_offline_mode():
raise LookupError(
"Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
)
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
@dataclass
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizerFast,
QDQBertConfig,
QDQBertForQuestionAnswering,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import SchedulerType, get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from utils_qa import postprocess_qa_predictions
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.9.0")
require_version(
"datasets>=1.8.0",
"To fix: pip install -r examples/pytorch/question-answering/requirements.txt"
)
logger = logging.getLogger(__name__)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
"""
PreTrainedTokenizerBase,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
import transformers
from datasets import Dataset, load_dataset
from enum import Enum
import dataclasses
from dataclasses import dataclass
import logging
import os
import sys
from typing import Union, Optional, Any
check_min_version('4.7.0')
class SpecialToken(Enum):
SIMSEP = '<SIMSEP>'
class SBert(nn.Module):
"""[Sentence-Bert]
:param nn: [description]
:type nn: [type]
"""
def __init__(self,
bert_pretrained_name='bert-base-chinese',