<reponame>aicanhelp/ai-transformers
from ..transformersx_base import *
from . import ALL_TASK_MODEL_PATHS, ModelTaskType, ModelMode


@configclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """
    model_finetuned_dir: str = field("/app/models/finetuning",
                                     "The 'model_base_dir' generally is for the finetuned models."
                                     "Generally, the model is loaded from 'model_finetuned_dir' firstly."
                                     " If the model cannot be found, it will be loaded from the model_pretrained_dir.")

    model_pretrained_dir: str = field("/app/models/pretrained",
                                      "This folder is for the pretrained models downloaded from Internet.")

    not_use_pretrained: bool = field(False,
                                     "Whether use the model in model_pretrained_dir. Generally, for training, it should be False")

    model_name: str = field("bert-base-chinese", "the name of model: " + str(ALL_TASK_MODEL_PATHS))

    model_task_type: str = field("seq_cls",
                                 "the task type of model:{}, model_task_type decides the Task".format(
                                     str(ModelTaskType.names())))

    model_mode: str = field("classification", "the model of model: " + str(ModelMode.names()))

    framework: str = field("pt", "the name of framework: pt--Pytorch,tf--Tensorflow")

    language: str = field("cn", "the language of model: cn, en")

    freeze_parameters: str = field("bert", "The parameters list for freeze")
    freeze_main: bool = field(False, 'whether freeze main parameter')

    num_labels: int = field(2, "the number of label")

    unit_test: bool = field(False, "For unit test of the code, without the load of the model")

from ..transformersx_base import *
from . import ALL_TASK_MODEL_PATHS, ModelTaskType, ModelMode


@configclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """
    model_finetuned_dir: str = field("/app/models/finetuning",
                                     "The 'model_base_dir' generally is for the finetuned models."
                                     "Generally, the model is loaded from 'model_finetuned_dir' firstly."
                                     " If the model cannot be found, it will be loaded from the model_pretrained_dir.")

    model_pretrained_dir: str = field("/app/models/pretrained",
                                      "This folder is for the pretrained models downloaded from Internet.")

    not_use_pretrained: bool = field(False,
                                     "Whether use the model in model_pretrained_dir. Generally, for training, it should be False")

    model_name: str = field("bert-base-chinese", "the name of model: " + str(ALL_TASK_MODEL_PATHS))

    model_task_type: str = field("seq_cls",
                                 "the task type of model:{}, model_task_type decides the Task".format(
                                     str(ModelTaskType.names())))

    model_mode: str = field("classification", "the model of model: " + str(ModelMode.names()))

    framework: str = field("pt", "the name of framework: pt--Pytorch,tf--Tensorflow")

    language: str = field("cn", "the language of model: cn, en")

    freeze_parameters: str = field("bert", "The parameters list for freeze")
    freeze_main: bool = field(False, 'whether freeze main parameter')

    num_labels: int = field(2, "the number of label")

    unit_test: bool = field(False, "For unit test of the code, without the load of the model")
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.