codingutf8 2018 the huggingface inc team microsoft corporation c 2018 nvidia corporation 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 mpnet model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername mpnetpretrainedconfigarchivemap microsoftmpnetbase https huggingface comicrosoftmpnetbaseresolvemainconfig json class mpnetconfigpretrainedconfig r this is the configuration class to store the configuration of a mpnetmodel or a tfmpnetmodel it is used to instantiate a mpnet 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 mpnet microsoftmpnetbasehttps huggingface comicrosoftmpnetbase 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 30527 vocabulary size of the mpnet model defines the number of different tokens that can be represented by the inputsids passed when calling mpnetmodel or tfmpnetmodel 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 often named feedforward layer in the transformer encoder hiddenact 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 hiddendropoutprob float optional defaults to 0 1 the dropout probability 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 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 relativeattentionnumbuckets int optional defaults to 32 the number of buckets to use for each attention layer examples python from transformers import mpnetmodel mpnetconfig initializing a mpnet mpnetbase style configuration configuration mpnetconfig initializing a model from the mpnetbase style configuration model mpnetmodelconfiguration accessing the model configuration configuration model config modeltype mpnet def init self vocabsize30527 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 initializerrange0 02 layernormeps1e12 relativeattentionnumbuckets32 padtokenid1 bostokenid0 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self hiddenact hiddenact self intermediatesize intermediatesize self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self initializerrange initializerrange self layernormeps layernormeps self relativeattentionnumbuckets relativeattentionnumbuckets coding utf 8 2018 the huggingface inc team microsoft corporation c 2018 nvidia corporation 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 mpnet model configuration this is the configuration class to store the configuration of a mpnetmodel or a tfmpnetmodel it is used to instantiate a mpnet 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 mpnet microsoft mpnet base https huggingface co microsoft mpnet 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 30527 vocabulary size of the mpnet model defines the number of different tokens that can be represented by the inputs_ids passed when calling mpnetmodel or tfmpnetmodel 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 often named feed forward layer in the transformer encoder hidden_act 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 hidden_dropout_prob float optional defaults to 0 1 the dropout probability 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 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 relative_attention_num_buckets int optional defaults to 32 the number of buckets to use for each attention layer examples python from transformers import mpnetmodel mpnetconfig initializing a mpnet mpnet base style configuration configuration mpnetconfig initializing a model from the mpnet base style configuration model mpnetmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/mpnet-base": "https://huggingface.co/microsoft/mpnet-base/resolve/main/config.json",
}


class MPNetConfig(PretrainedConfig):
    r

    model_type = "mpnet"

    def __init__(
        self,
        vocab_size=30527,
        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,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        relative_attention_num_buckets=32,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **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.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.relative_attention_num_buckets = relative_attention_num_buckets