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