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configuration_bertabs.py
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configuration_bertabs.py
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# coding=utf-8
# Copyright 2019 The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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 """
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"""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.
"""
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
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