codingutf8 2020 the google ai language team s and the huggingface inc 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 bertgeneration model configuration from configurationutils import pretrainedconfig class bertgenerationconfigpretrainedconfig r this is the configuration class to store the configuration of a bertgenerationpretrainedmodel it is used to instantiate a bertgeneration 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 bertgeneration googlebertforseqgenerationl24bbcencoderhttps huggingface cogooglebertforseqgenerationl24bbcencoder 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 50358 vocabulary size of the bert model defines the number of different tokens that can be represented by the inputsids passed when calling bertgeneration hiddensize int optional defaults to 1024 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 24 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 4096 dimensionality of the intermediate often called 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 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 padtokenid int optional defaults to 0 padding token id bostokenid int optional defaults to 2 beginning of stream token id eostokenid int optional defaults to 1 end of stream token id positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery for positional embeddings use absolute for more information on relativekey please refer to selfattention with relative position representations shaw et al https arxiv orgabs1803 02155 for more information on relativekeyquery please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv orgabs2009 13658 usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue examples python from transformers import bertgenerationconfig bertgenerationencoder initializing a bertgeneration config configuration bertgenerationconfig initializing a model with random weights from the config model bertgenerationencoderconfiguration accessing the model configuration configuration model config modeltype bertgeneration def init self vocabsize50358 hiddensize1024 numhiddenlayers24 numattentionheads16 intermediatesize4096 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 initializerrange0 02 layernormeps1e12 padtokenid0 bostokenid2 eostokenid1 positionembeddingtypeabsolute usecachetrue 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 positionembeddingtype positionembeddingtype self usecache usecache coding utf 8 2020 the google ai language team s and the huggingface inc 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 bertgeneration model configuration this is the configuration class to store the configuration of a bertgenerationpretrainedmodel it is used to instantiate a bertgeneration 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 bertgeneration google bert_for_seq_generation_l 24_bbc_encoder https huggingface co google bert_for_seq_generation_l 24_bbc_encoder 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 50358 vocabulary size of the bert model defines the number of different tokens that can be represented by the inputs_ids passed when calling bertgeneration hidden_size int optional defaults to 1024 dimensionality of the encoder layers and the pooler layer num_hidden_layers int optional defaults to 24 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder intermediate_size int optional defaults to 4096 dimensionality of the intermediate often called 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 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 pad_token_id int optional defaults to 0 padding token id bos_token_id int optional defaults to 2 beginning of stream token id eos_token_id int optional defaults to 1 end of stream token id position_embedding_type str optional defaults to absolute type of position embedding choose one of absolute relative_key relative_key_query for positional embeddings use absolute for more information on relative_key please refer to self attention with relative position representations shaw et al https arxiv org abs 1803 02155 for more information on relative_key_query please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv org abs 2009 13658 use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models only relevant if config is_decoder true examples python from transformers import bertgenerationconfig bertgenerationencoder initializing a bertgeneration config configuration bertgenerationconfig initializing a model with random weights from the config model bertgenerationencoder configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig


class BertGenerationConfig(PretrainedConfig):
    r

    model_type = "bert-generation"

    def __init__(
        self,
        vocab_size=50358,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        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,
        pad_token_id=0,
        bos_token_id=2,
        eos_token_id=1,
        position_embedding_type="absolute",
        use_cache=True,
        **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.position_embedding_type = position_embedding_type
        self.use_cache = use_cache