Python check_min_version Examples

Example #1

File: run_summarization_no_trainer.py Project: LSinev/transformers

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(

Example #2

File: run_qa.py Project: peteriz/transformers

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"

Example #3

File: train_full_classifier.py Project: TalSchuster/CATs

    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__)

Example #4

File: run_mlm.py Project: dhawalkp/amazon-sagemaker-examples

    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.

Example #5

    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={

Example #6

File: run_glue_model_parallelism.py Project: madlag/transformers

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__)

Example #7

File: run_summarization.py Project: rmroczkowski/transformers

    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

Example #8

    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.
    """

Example #9

    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',