codingutf8 2019present the huggingface inc team the google ai language team and facebook inc 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 distilbert 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 distilbertpretrainedconfigarchivemap distilbertbaseuncased https huggingface codistilbertbaseuncasedresolvemainconfig json distilbertbaseuncaseddistilledsquad https huggingface codistilbertbaseuncaseddistilledsquadresolvemainconfig json distilbertbasecased https huggingface codistilbertbasecasedresolvemainconfig json distilbertbasecaseddistilledsquad https huggingface codistilbertbasecaseddistilledsquadresolvemainconfig json distilbertbasegermancased https huggingface codistilbertbasegermancasedresolvemainconfig json distilbertbasemultilingualcased https huggingface codistilbertbasemultilingualcasedresolvemainconfig json distilbertbaseuncasedfinetunedsst2english https huggingface codistilbertbaseuncasedfinetunedsst2englishresolvemainconfig json class distilbertconfigpretrainedconfig r this is the configuration class to store the configuration of a distilbertmodel or a tfdistilbertmodel it is used to instantiate a distilbert 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 distilbert distilbertbaseuncasedhttps huggingface codistilbertbaseuncased 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 distilbert model defines the number of different tokens that can be represented by the inputsids passed when calling distilbertmodel or tfdistilbertmodel 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 sinusoidalposembds boolean optional defaults to false whether to use sinusoidal positional embeddings nlayers int optional defaults to 6 number of hidden layers in the transformer encoder nheads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder dim int optional defaults to 768 dimensionality of the encoder layers and the pooler layer hiddendim int optional defaults to 3072 the size of the intermediate often named feedforward layer in the transformer encoder dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentiondropout float optional defaults to 0 1 the dropout ratio for the attention probabilities activation str or callable optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices qadropout float optional defaults to 0 1 the dropout probabilities used in the question answering model distilbertforquestionanswering seqclassifdropout float optional defaults to 0 2 the dropout probabilities used in the sequence classification and the multiple choice model distilbertforsequenceclassification examples python from transformers import distilbertconfig distilbertmodel initializing a distilbert configuration configuration distilbertconfig initializing a model with random weights from the configuration model distilbertmodelconfiguration accessing the model configuration configuration model config modeltype distilbert attributemap hiddensize dim numattentionheads nheads numhiddenlayers nlayers def init self vocabsize30522 maxpositionembeddings512 sinusoidalposembdsfalse nlayers6 nheads12 dim768 hiddendim4 768 dropout0 1 attentiondropout0 1 activationgelu initializerrange0 02 qadropout0 1 seqclassifdropout0 2 padtokenid0 kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self sinusoidalposembds sinusoidalposembds self nlayers nlayers self nheads nheads self dim dim self hiddendim hiddendim self dropout dropout self attentiondropout attentiondropout self activation activation self initializerrange initializerrange self qadropout qadropout self seqclassifdropout seqclassifdropout super initkwargs padtokenidpadtokenid class distilbertonnxconfigonnxconfig 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 coding utf 8 2019 present the huggingface inc team the google ai language team and facebook inc 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 distilbert model configuration this is the configuration class to store the configuration of a distilbertmodel or a tfdistilbertmodel it is used to instantiate a distilbert 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 distilbert distilbert base uncased https huggingface co distilbert base uncased 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 distilbert model defines the number of different tokens that can be represented by the inputs_ids passed when calling distilbertmodel or tfdistilbertmodel 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 sinusoidal_pos_embds boolean optional defaults to false whether to use sinusoidal positional embeddings n_layers int optional defaults to 6 number of hidden layers in the transformer encoder n_heads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder dim int optional defaults to 768 dimensionality of the encoder layers and the pooler layer hidden_dim int optional defaults to 3072 the size of the intermediate often named feed forward layer in the transformer encoder dropout float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_dropout float optional defaults to 0 1 the dropout ratio for the attention probabilities activation str or callable optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu silu and gelu_new are supported initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices qa_dropout float optional defaults to 0 1 the dropout probabilities used in the question answering model distilbertforquestionanswering seq_classif_dropout float optional defaults to 0 2 the dropout probabilities used in the sequence classification and the multiple choice model distilbertforsequenceclassification examples python from transformers import distilbertconfig distilbertmodel initializing a distilbert configuration configuration distilbertconfig initializing a model with random weights from the configuration model distilbertmodel configuration accessing the model configuration configuration model config
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__)

DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}


class DistilBertConfig(PretrainedConfig):
    r

    model_type = "distilbert"
    attribute_map = {
        "hidden_size": "dim",
        "num_attention_heads": "n_heads",
        "num_hidden_layers": "n_layers",
    }

    def __init__(
        self,
        vocab_size=30522,
        max_position_embeddings=512,
        sinusoidal_pos_embds=False,
        n_layers=6,
        n_heads=12,
        dim=768,
        hidden_dim=4 * 768,
        dropout=0.1,
        attention_dropout=0.1,
        activation="gelu",
        initializer_range=0.02,
        qa_dropout=0.1,
        seq_classif_dropout=0.2,
        pad_token_id=0,
        **kwargs,
    ):
        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.sinusoidal_pos_embds = sinusoidal_pos_embds
        self.n_layers = n_layers
        self.n_heads = n_heads
        self.dim = dim
        self.hidden_dim = hidden_dim
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.activation = activation
        self.initializer_range = initializer_range
        self.qa_dropout = qa_dropout
        self.seq_classif_dropout = seq_classif_dropout
        super().__init__(**kwargs, pad_token_id=pad_token_id)


class DistilBertOnnxConfig(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),
            ]
        )