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120 lines
4.2 KiB
Python
120 lines
4.2 KiB
Python
# coding=utf-8
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# Copyright 2019 The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" BertAbs configuration """
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import json
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import logging
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import sys
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from transformers import PretrainedConfig
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logger = logging.getLogger(__name__)
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BERTABS_FINETUNED_CONFIG_MAP = {
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"bertabs-finetuned-cnndm": "https://s3.amazonaws.com/models.huggingface.co/bert/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization-config.json",
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}
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class BertAbsConfig(PretrainedConfig):
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r""" Class to store the configuration of the BertAbs model.
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Arguments:
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max_pos: int
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The maximum sequence length that this model will be used with.
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enc_layer: int
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The numner of hidden layers in the Transformer encoder.
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enc_hidden_size: int
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The size of the encoder's layers.
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enc_heads: int
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The number of attention heads for each attention layer in the encoder.
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enc_ff_size: int
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The size of the encoder's feed-forward layers.
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enc_dropout: int
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The dropout probabilitiy for all fully connected layers in the
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embeddings, layers, pooler and also the attention probabilities in
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the encoder.
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dec_layer: int
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The numner of hidden layers in the decoder.
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dec_hidden_size: int
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The size of the decoder's layers.
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dec_heads: int
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The number of attention heads for each attention layer in the decoder.
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dec_ff_size: int
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The size of the decoder's feed-forward layers.
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dec_dropout: int
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The dropout probabilitiy for all fully connected layers in the
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embeddings, layers, pooler and also the attention probabilities in
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the decoder.
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"""
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pretrained_config_archive_map = BERTABS_FINETUNED_CONFIG_MAP
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def __init__(
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self,
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vocab_size=30522,
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max_pos=512,
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enc_layers=6,
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enc_hidden_size=512,
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enc_heads=8,
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enc_ff_size=512,
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enc_dropout=0.2,
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dec_layers=6,
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dec_hidden_size=768,
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dec_heads=8,
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dec_ff_size=2048,
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dec_dropout=0.2,
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**kwargs,
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):
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super(BertAbsConfig, self).__init__(**kwargs)
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if self._input_is_path_to_json(vocab_size):
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path_to_json = vocab_size
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with open(path_to_json, "r", encoding="utf-8") as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size, int):
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self.vocab_size = vocab_size
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self.max_pos = max_pos
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self.enc_layers = enc_layers
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self.enc_hidden_size = enc_hidden_size
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self.enc_heads = enc_heads
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self.enc_ff_size = enc_ff_size
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self.enc_dropout = enc_dropout
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self.dec_layers = dec_layers
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self.dec_hidden_size = dec_hidden_size
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self.dec_heads = dec_heads
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self.dec_ff_size = dec_ff_size
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self.dec_dropout = dec_dropout
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else:
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raise ValueError(
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"First argument must be either a vocabulary size (int)"
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"or the path to a pretrained model config file (str)"
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)
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def _input_is_path_to_json(self, first_argument):
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""" Checks whether the first argument passed to config
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is the path to a JSON file that contains the config.
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"""
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is_python_2 = sys.version_info[0] == 2
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if is_python_2:
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return isinstance(first_argument, unicode)
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else:
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return isinstance(first_argument, str)
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