145 lines
6.9 KiB
Python
145 lines
6.9 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
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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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""" DistilBERT model 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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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"distilbert-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
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"distilbert-base-uncased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json",
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"distilbert-base-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-cased-config.json",
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"distilbert-base-cased-distilled-squad": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-cased-distilled-squad-config.json",
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"distilbert-base-german-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-german-cased-config.json",
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"distilbert-base-multilingual-cased": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-multilingual-cased-config.json",
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"distilbert-base-uncased-finetuned-sst-2-english": "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-finetuned-sst-2-english-config.json",
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}
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class DistilBertConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel`.
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It is used to instantiate a DistilBERT model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the DistilBERT `distilbert-base-uncased <https://huggingface.co/distilbert-base-uncased>`__ architecture.
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Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used
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to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig`
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for more information.
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Args:
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vocab_size (:obj:`int`, optional, defaults to 30522):
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Vocabulary size of the DistilBERT model. Defines the different tokens that
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can be represented by the `inputs_ids` passed to the forward method of :class:`~transformers.BertModel`.
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max_position_embeddings (:obj:`int`, optional, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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sinusoidal_pos_embds (:obj:`boolean`, optional, defaults to :obj:`False`):
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Whether to use sinusoidal positional embeddings.
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n_layers (:obj:`int`, optional, defaults to 6):
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Number of hidden layers in the Transformer encoder.
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n_heads (:obj:`int`, optional, defaults to 12):
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Number of attention heads for each attention layer in the Transformer encoder.
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dim (:obj:`int`, optional, defaults to 768):
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Dimensionality of the encoder layers and the pooler layer.
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hidden_dim (:obj:`int`, optional, defaults to 3072):
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The size of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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dropout (:obj:`float`, optional, defaults to 0.1):
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The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
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attention_dropout (:obj:`float`, optional, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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activation (:obj:`str` or :obj:`function`, optional, defaults to "gelu"):
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The non-linear activation function (function or string) in the encoder and pooler.
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If string, "gelu", "relu", "swish" and "gelu_new" are supported.
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initializer_range (:obj:`float`, optional, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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qa_dropout (:obj:`float`, optional, defaults to 0.1):
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The dropout probabilities used in the question answering model
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:class:`~transformers.DistilBertForQuestionAnswering`.
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seq_classif_dropout (:obj:`float`, optional, defaults to 0.2):
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The dropout probabilities used in the sequence classification model
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:class:`~transformers.DistilBertForSequenceClassification`.
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Example::
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from transformers import DistilBertModel, DistilBertConfig
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# Initializing a DistilBERT configuration
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configuration = DistilBertConfig()
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# Initializing a model from the configuration
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model = DistilBertModel(configuration)
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# Accessing the model configuration
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configuration = model.config
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Attributes:
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pretrained_config_archive_map (Dict[str, str]):
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A dictionary containing all the available pre-trained checkpoints.
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"""
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pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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model_type = "distilbert"
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def __init__(
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self,
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vocab_size=30522,
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max_position_embeddings=512,
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sinusoidal_pos_embds=False,
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n_layers=6,
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n_heads=12,
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dim=768,
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hidden_dim=4 * 768,
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dropout=0.1,
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attention_dropout=0.1,
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activation="gelu",
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initializer_range=0.02,
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qa_dropout=0.1,
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seq_classif_dropout=0.2,
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pad_token_id=0,
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**kwargs
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):
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super().__init__(**kwargs, pad_token_id=pad_token_id)
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.sinusoidal_pos_embds = sinusoidal_pos_embds
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dim = dim
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self.hidden_dim = hidden_dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation = activation
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self.initializer_range = initializer_range
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self.qa_dropout = qa_dropout
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self.seq_classif_dropout = seq_classif_dropout
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@property
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def hidden_size(self):
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return self.dim
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@property
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def num_attention_heads(self):
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return self.n_heads
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@property
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def num_hidden_layers(self):
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return self.n_layers
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