codingutf8 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache orglicenseslicense2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license convbert model configuration from collections import ordereddict from typing import mapping from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername convbertpretrainedconfigarchivemap yitutechconvbertbase https huggingface coyitutechconvbertbaseresolvemainconfig json yitutechconvbertmediumsmall https huggingface coyitutechconvbertmediumsmallresolvemainconfig json yitutechconvbertsmall https huggingface coyitutechconvbertsmallresolvemainconfig json see all convbert models at https huggingface comodels filterconvbert class convbertconfigpretrainedconfig r this is the configuration class to store the configuration of a convbertmodel it is used to instantiate an convbert model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the convbert yitutechconvbertbasehttps huggingface coyitutechconvbertbase architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 30522 vocabulary size of the convbert model defines the number of different tokens that can be represented by the inputsids passed when calling convbertmodel or tfconvbertmodel hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder hiddenact str or function optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew are supported hiddendropoutprob float optional defaults to 0 1 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 1 the dropout ratio for the attention probabilities maxpositionembeddings int optional defaults to 512 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling convbertmodel or tfconvbertmodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers headratio int optional defaults to 2 ratio gamma to reduce the number of attention heads numgroups int optional defaults to 1 the number of groups for grouped linear layers for convbert model convkernelsize int optional defaults to 9 the size of the convolutional kernel classifierdropout float optional the dropout ratio for the classification head example python from transformers import convbertconfig convbertmodel initializing a convbert convbertbaseuncased style configuration configuration convbertconfig initializing a model with random weights from the convbertbaseuncased style configuration model convbertmodelconfiguration accessing the model configuration configuration model config modeltype convbert def init self vocabsize30522 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 padtokenid1 bostokenid0 eostokenid2 embeddingsize768 headratio2 convkernelsize9 numgroups1 classifierdropoutnone kwargs super init padtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self embeddingsize embeddingsize self headratio headratio self convkernelsize convkernelsize self numgroups numgroups self classifierdropout classifierdropout copied from transformers models bert configurationbert bertonnxconfig class convbertonnxconfigonnxconfig property def inputsself mappingstr mappingint str if self task multiplechoice dynamicaxis 0 batch 1 choice 2 sequence else dynamicaxis 0 batch 1 sequence return ordereddict inputids dynamicaxis attentionmask dynamicaxis tokentypeids dynamicaxis coding utf 8 the huggingface team licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license convbert model configuration see all convbert models at https huggingface co models filter convbert this is the configuration class to store the configuration of a convbertmodel it is used to instantiate an convbert model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the convbert yitutech conv bert base https huggingface co yitutech conv bert base architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocab_size int optional defaults to 30522 vocabulary size of the convbert model defines the number of different tokens that can be represented by the inputs_ids passed when calling convbertmodel or tfconvbertmodel hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 12 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder hidden_act str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new are supported hidden_dropout_prob float optional defaults to 0 1 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler attention_probs_dropout_prob float optional defaults to 0 1 the dropout ratio for the attention probabilities max_position_embeddings int optional defaults to 512 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 type_vocab_size int optional defaults to 2 the vocabulary size of the token_type_ids passed when calling convbertmodel or tfconvbertmodel initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices layer_norm_eps float optional defaults to 1e 12 the epsilon used by the layer normalization layers head_ratio int optional defaults to 2 ratio gamma to reduce the number of attention heads num_groups int optional defaults to 1 the number of groups for grouped linear layers for convbert model conv_kernel_size int optional defaults to 9 the size of the convolutional kernel classifier_dropout float optional the dropout ratio for the classification head example python from transformers import convbertconfig convbertmodel initializing a convbert convbert base uncased style configuration configuration convbertconfig initializing a model with random weights from the convbert base uncased style configuration model convbertmodel configuration accessing the model configuration configuration model config copied from transformers models bert configuration_bert bertonnxconfig
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
    "YituTech/conv-bert-medium-small": (
        "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
    ),
    "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
    
}


class ConvBertConfig(PretrainedConfig):
    r

    model_type = "convbert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        embedding_size=768,
        head_ratio=2,
        conv_kernel_size=9,
        num_groups=1,
        classifier_dropout=None,
        **kwargs,
    ):
        super().__init__(
            pad_token_id=pad_token_id,
            bos_token_id=bos_token_id,
            eos_token_id=eos_token_id,
            **kwargs,
        )

        self.vocab_size = vocab_size
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.embedding_size = embedding_size
        self.head_ratio = head_ratio
        self.conv_kernel_size = conv_kernel_size
        self.num_groups = num_groups
        self.classifier_dropout = classifier_dropout



class ConvBertOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
            dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"}
        else:
            dynamic_axis = {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]
        )