codingutf8 2021 the microsoft research asia markuplm team s 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 markuplm model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername markuplmpretrainedconfigarchivemap microsoftmarkuplmbase https huggingface comicrosoftmarkuplmbaseresolvemainconfig json microsoftmarkuplmlarge https huggingface comicrosoftmarkuplmlargeresolvemainconfig json class markuplmconfigpretrainedconfig r this is the configuration class to store the configuration of a markuplmmodel it is used to instantiate a markuplm 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 markuplm microsoftmarkuplmbasehttps huggingface comicrosoftmarkuplmbase architecture configuration objects inherit from bertconfig and can be used to control the model outputs read the documentation from bertconfig for more information args vocabsize int optional defaults to 30522 vocabulary size of the markuplm model defines the different tokens that can be represented by the inputsids passed to the forward method of markuplmmodel 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 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 typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed into markuplmmodel 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 maxtreeidunitembeddings int optional defaults to 1024 the maximum value that the tree id unit embedding might ever use typically set this to something large just in case e g 1024 maxxpathtagunitembeddings int optional defaults to 256 the maximum value that the xpath tag unit embedding might ever use typically set this to something large just in case e g 256 maxxpathsubsunitembeddings int optional defaults to 1024 the maximum value that the xpath subscript unit embedding might ever use typically set this to something large just in case e g 1024 tagpadid int optional defaults to 216 the id of the padding token in the xpath tags subspadid int optional defaults to 1001 the id of the padding token in the xpath subscripts xpathtagunithiddensize int optional defaults to 32 the hidden size of each tree id unit one complete tree index will have 50xpathtagunithiddensizedim maxdepth int optional defaults to 50 the maximum depth in xpath examples python from transformers import markuplmmodel markuplmconfig initializing a markuplm microsoftmarkuplmbase style configuration configuration markuplmconfig initializing a model from the microsoftmarkuplmbase style configuration model markuplmmodelconfiguration accessing the model configuration configuration model config modeltype markuplm def init self vocabsize30522 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 padtokenid0 bostokenid0 eostokenid2 maxxpathtagunitembeddings256 maxxpathsubsunitembeddings1024 tagpadid216 subspadid1001 xpathunithiddensize32 maxdepth50 positionembeddingtypeabsolute usecachetrue classifierdropoutnone kwargs super init padtokenidpadtokenid 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 typevocabsize typevocabsize self initializerrange initializerrange self layernormeps layernormeps self positionembeddingtype positionembeddingtype self usecache usecache self classifierdropout classifierdropout additional properties self maxdepth maxdepth self maxxpathtagunitembeddings maxxpathtagunitembeddings self maxxpathsubsunitembeddings maxxpathsubsunitembeddings self tagpadid tagpadid self subspadid subspadid self xpathunithiddensize xpathunithiddensize coding utf 8 2021 the microsoft research asia markuplm team s 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 markuplm model configuration this is the configuration class to store the configuration of a markuplmmodel it is used to instantiate a markuplm 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 markuplm microsoft markuplm base https huggingface co microsoft markuplm base architecture configuration objects inherit from bertconfig and can be used to control the model outputs read the documentation from bertconfig for more information args vocab_size int optional defaults to 30522 vocabulary size of the markuplm model defines the different tokens that can be represented by the inputs_ids passed to the forward method of markuplmmodel 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 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 type_vocab_size int optional defaults to 2 the vocabulary size of the token_type_ids passed into markuplmmodel 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 max_tree_id_unit_embeddings int optional defaults to 1024 the maximum value that the tree id unit embedding might ever use typically set this to something large just in case e g 1024 max_xpath_tag_unit_embeddings int optional defaults to 256 the maximum value that the xpath tag unit embedding might ever use typically set this to something large just in case e g 256 max_xpath_subs_unit_embeddings int optional defaults to 1024 the maximum value that the xpath subscript unit embedding might ever use typically set this to something large just in case e g 1024 tag_pad_id int optional defaults to 216 the id of the padding token in the xpath tags subs_pad_id int optional defaults to 1001 the id of the padding token in the xpath subscripts xpath_tag_unit_hidden_size int optional defaults to 32 the hidden size of each tree id unit one complete tree index will have 50 xpath_tag_unit_hidden_size dim max_depth int optional defaults to 50 the maximum depth in xpath examples python from transformers import markuplmmodel markuplmconfig initializing a markuplm microsoft markuplm base style configuration configuration markuplmconfig initializing a model from the microsoft markuplm base style configuration model markuplmmodel configuration accessing the model configuration configuration model config additional properties
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}


class MarkupLMConfig(PretrainedConfig):
    r

    model_type = "markuplm"

    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=0,
        bos_token_id=0,
        eos_token_id=2,
        max_xpath_tag_unit_embeddings=256,
        max_xpath_subs_unit_embeddings=1024,
        tag_pad_id=216,
        subs_pad_id=1001,
        xpath_unit_hidden_size=32,
        max_depth=50,
        position_embedding_type="absolute",
        use_cache=True,
        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.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.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.classifier_dropout = classifier_dropout
        
        self.max_depth = max_depth
        self.max_xpath_tag_unit_embeddings = max_xpath_tag_unit_embeddings
        self.max_xpath_subs_unit_embeddings = max_xpath_subs_unit_embeddings
        self.tag_pad_id = tag_pad_id
        self.subs_pad_id = subs_pad_id
        self.xpath_unit_hidden_size = xpath_unit_hidden_size