codingutf8 2019 the huggingface inc team 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 bertabs configuration import logging from transformers import pretrainedconfig logger logging getloggername bertabsfinetunedconfigmap bertabsfinetunedcnndm https huggingface coremibertabsfinetunedcnndmextractiveabstractivesummarizationresolvemainconfig json class bertabsconfigpretrainedconfig rclass to store the configuration of the bertabs model arguments vocabsize int number of tokens in the vocabulary maxpos int the maximum sequence length that this model will be used with enclayer int the numner of hidden layers in the transformer encoder enchiddensize int the size of the encoder s layers encheads int the number of attention heads for each attention layer in the encoder encffsize int the size of the encoder s feedforward layers encdropout int the dropout probability for all fully connected layers in the embeddings layers pooler and also the attention probabilities in the encoder declayer int the numner of hidden layers in the decoder dechiddensize int the size of the decoder s layers decheads int the number of attention heads for each attention layer in the decoder decffsize int the size of the decoder s feedforward layers decdropout int the dropout probability for all fully connected layers in the embeddings layers pooler and also the attention probabilities in the decoder coding utf 8 2019 the huggingface inc team 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 bertabs configuration class to store the configuration of the bertabs model arguments vocab_size int number of tokens in the vocabulary max_pos int the maximum sequence length that this model will be used with enc_layer int the numner of hidden layers in the transformer encoder enc_hidden_size int the size of the encoder s layers enc_heads int the number of attention heads for each attention layer in the encoder enc_ff_size int the size of the encoder s feed forward layers enc_dropout int the dropout probability for all fully connected layers in the embeddings layers pooler and also the attention probabilities in the encoder dec_layer int the numner of hidden layers in the decoder dec_hidden_size int the size of the decoder s layers dec_heads int the number of attention heads for each attention layer in the decoder dec_ff_size int the size of the decoder s feed forward layers dec_dropout int the dropout probability for all fully connected layers in the embeddings layers pooler and also the attention probabilities in the decoder
import logging

from transformers import PretrainedConfig


logger = logging.getLogger(__name__)


BERTABS_FINETUNED_CONFIG_MAP = {
    "bertabs-finetuned-cnndm": "https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json",
}


class BertAbsConfig(PretrainedConfig):
    r

    model_type = "bertabs"

    def __init__(
        self,
        vocab_size=30522,
        max_pos=512,
        enc_layers=6,
        enc_hidden_size=512,
        enc_heads=8,
        enc_ff_size=512,
        enc_dropout=0.2,
        dec_layers=6,
        dec_hidden_size=768,
        dec_heads=8,
        dec_ff_size=2048,
        dec_dropout=0.2,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.vocab_size = vocab_size
        self.max_pos = max_pos

        self.enc_layers = enc_layers
        self.enc_hidden_size = enc_hidden_size
        self.enc_heads = enc_heads
        self.enc_ff_size = enc_ff_size
        self.enc_dropout = enc_dropout

        self.dec_layers = dec_layers
        self.dec_hidden_size = dec_hidden_size
        self.dec_heads = dec_heads
        self.dec_ff_size = dec_ff_size
        self.dec_dropout = dec_dropout