# coding=utf-8 # Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc. # # 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. """ DistilBERT model configuration """ from collections import OrderedDict from typing import Mapping from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging logger = logging.get_logger(__name__) DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json", "distilbert-base-uncased-distilled-squad": "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json", "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json", "distilbert-base-cased-distilled-squad": "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json", "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json", "distilbert-base-multilingual-cased": "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json", "distilbert-base-uncased-finetuned-sst-2-english": "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json", } class DistilBertConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a :class:`~transformers.DistilBertModel` or a :class:`~transformers.TFDistilBertModel`. It is used to instantiate a DistilBERT model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the DistilBERT `distilbert-base-uncased `__ architecture. Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. Args: vocab_size (:obj:`int`, `optional`, defaults to 30522): Vocabulary size of the DistilBERT model. Defines the number of different tokens that can be represented by the :obj:`inputs_ids` passed when calling :class:`~transformers.DistilBertModel` or :class:`~transformers.TFDistilBertModel`. max_position_embeddings (:obj:`int`, `optional`, defaults to 512): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). sinusoidal_pos_embds (:obj:`boolean`, `optional`, defaults to :obj:`False`): Whether to use sinusoidal positional embeddings. n_layers (:obj:`int`, `optional`, defaults to 6): Number of hidden layers in the Transformer encoder. n_heads (:obj:`int`, `optional`, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. dim (:obj:`int`, `optional`, defaults to 768): Dimensionality of the encoder layers and the pooler layer. hidden_dim (:obj:`int`, `optional`, defaults to 3072): The size of the "intermediate" (often named feed-forward) layer in the Transformer encoder. dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout ratio for the attention probabilities. activation (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"` and :obj:`"gelu_new"` are supported. initializer_range (:obj:`float`, `optional`, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. qa_dropout (:obj:`float`, `optional`, defaults to 0.1): The dropout probabilities used in the question answering model :class:`~transformers.DistilBertForQuestionAnswering`. seq_classif_dropout (:obj:`float`, `optional`, defaults to 0.2): The dropout probabilities used in the sequence classification and the multiple choice model :class:`~transformers.DistilBertForSequenceClassification`. Examples:: >>> from transformers import DistilBertModel, DistilBertConfig >>> # Initializing a DistilBERT configuration >>> configuration = DistilBertConfig() >>> # Initializing a model from the configuration >>> model = DistilBertModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config """ model_type = "distilbert" def __init__( self, vocab_size=30522, max_position_embeddings=512, sinusoidal_pos_embds=False, n_layers=6, n_heads=12, dim=768, hidden_dim=4 * 768, dropout=0.1, attention_dropout=0.1, activation="gelu", initializer_range=0.02, qa_dropout=0.1, seq_classif_dropout=0.2, pad_token_id=0, **kwargs ): super().__init__(**kwargs, pad_token_id=pad_token_id) self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.sinusoidal_pos_embds = sinusoidal_pos_embds self.n_layers = n_layers self.n_heads = n_heads self.dim = dim self.hidden_dim = hidden_dim self.dropout = dropout self.attention_dropout = attention_dropout self.activation = activation self.initializer_range = initializer_range self.qa_dropout = qa_dropout self.seq_classif_dropout = seq_classif_dropout @property def hidden_size(self): return self.dim @property def num_attention_heads(self): return self.n_heads @property def num_hidden_layers(self): return self.n_layers class DistilBertOnnxConfig(OnnxConfig): @property def inputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict( [ ("input_ids", {0: "batch", 1: "sequence"}), ("attention_mask", {0: "batch", 1: "sequence"}), ] ) @property def outputs(self) -> Mapping[str, Mapping[int, str]]: return OrderedDict([("last_hidden_state", {0: "batch", 1: "sequence"})])