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# coding=utf-8
# Copyright 2020 The Fairseq Authors 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.
""" BART configuration """
import logging
from .configuration_utils import PretrainedConfig
logger = logging.getLogger(__name__)
BART_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"bart-large": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large/config.json",
"bart-large-mnli": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-mnli/config.json",
"bart-large-cnn": "https://s3.amazonaws.com/models.huggingface.co/bert/facebook/bart-large-cnn/config.json",
}
class BartConfig(PretrainedConfig):
r"""
Configuration class for Bart. Parameters are renamed from the fairseq implementation
"""
model_type = "bart"
pretrained_config_archive_map = BART_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
activation_dropout=0.0,
vocab_size=50265,
pad_token_id=1,
eos_token_id=2,
d_model=1024,
encoder_ffn_dim=4096,
encoder_layers=12,
encoder_attention_heads=16,
decoder_ffn_dim=4096,
decoder_layers=12,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
attention_dropout=0.0,
dropout=0.1,
max_position_embeddings=1024,
init_std=0.02,
classifier_dropout=0.0,
output_past=False,
num_labels=3,
bos_token_id=0,
**common_kwargs
):
r"""
:class:`~transformers.BartConfig` is the configuration class for `BartModel`.
Examples:
config = BartConfig.from_pretrained('bart-large')
model = BartModel(config)
"""
super().__init__(
num_labels=num_labels,
output_past=output_past,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
**common_kwargs,
)
self.vocab_size = vocab_size
self.d_model = d_model # encoder_embed_dim and decoder_embed_dim
self.eos_token_id = eos_token_id
self.encoder_ffn_dim = encoder_ffn_dim
self.encoder_layers = self.num_hidden_layers = encoder_layers
self.encoder_attention_heads = encoder_attention_heads
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.decoder_ffn_dim = decoder_ffn_dim
self.decoder_layers = decoder_layers
self.decoder_attention_heads = decoder_attention_heads
self.max_position_embeddings = max_position_embeddings
self.init_std = init_std # Normal(0, this parameter)
# 3 Types of Dropout
self.attention_dropout = attention_dropout
self.activation_dropout = activation_dropout
self.dropout = dropout
# Classifier stuff
self.classif_dropout = classifier_dropout
@property
def num_attention_heads(self):
return self.encoder_attention_heads
@property
def hidden_size(self):
return self.d_model
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