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| # coding=utf-8 | |
| # Copyright 2018 The Google AI Language Team Authors and 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. | |
| """ BERT 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__) | |
| BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/config.json", | |
| "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/config.json", | |
| "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/config.json", | |
| "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/config.json", | |
| "bert-base-multilingual-uncased": "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json", | |
| "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json", | |
| "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/config.json", | |
| "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/config.json", | |
| "bert-large-uncased-whole-word-masking": ( | |
| "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json" | |
| ), | |
| "bert-large-cased-whole-word-masking": ( | |
| "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json" | |
| ), | |
| "bert-large-uncased-whole-word-masking-finetuned-squad": ( | |
| "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json" | |
| ), | |
| "bert-large-cased-whole-word-masking-finetuned-squad": ( | |
| "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json" | |
| ), | |
| "bert-base-cased-finetuned-mrpc": "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json", | |
| "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json", | |
| "bert-base-german-dbmdz-uncased": "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json", | |
| "cl-tohoku/bert-base-japanese": "https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json", | |
| "cl-tohoku/bert-base-japanese-whole-word-masking": ( | |
| "https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json" | |
| ), | |
| "cl-tohoku/bert-base-japanese-char": ( | |
| "https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json" | |
| ), | |
| "cl-tohoku/bert-base-japanese-char-whole-word-masking": ( | |
| "https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json" | |
| ), | |
| "TurkuNLP/bert-base-finnish-cased-v1": ( | |
| "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json" | |
| ), | |
| "TurkuNLP/bert-base-finnish-uncased-v1": ( | |
| "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json" | |
| ), | |
| "wietsedv/bert-base-dutch-cased": "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json", | |
| # See all BERT models at https://huggingface.co/models?filter=bert | |
| } | |
| class BertConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BertModel`] or a [`TFBertModel`]. It is used to | |
| instantiate a BERT 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 BERT | |
| [bert-base-uncased](https://huggingface.co/bert-base-uncased) architecture. | |
| Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the | |
| documentation from [`PretrainedConfig`] for more information. | |
| Args: | |
| vocab_size (`int`, *optional*, defaults to 30522): | |
| Vocabulary size of the BERT model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`BertModel`] or [`TFBertModel`]. | |
| hidden_size (`int`, *optional*, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (`int`, *optional*, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"silu"` and `"gelu_new"` are supported. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (`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). | |
| type_vocab_size (`int`, *optional*, defaults to 2): | |
| The vocabulary size of the `token_type_ids` passed when calling [`BertModel`] or [`TFBertModel`]. | |
| initializer_range (`float`, *optional*, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (`float`, *optional*, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| position_embedding_type (`str`, *optional*, defaults to `"absolute"`): | |
| Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For | |
| positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to | |
| [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155). | |
| For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models | |
| with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658). | |
| is_decoder (`bool`, *optional*, defaults to `False`): | |
| Whether the model is used as a decoder or not. If `False`, the model is used as an encoder. | |
| use_cache (`bool`, *optional*, defaults to `True`): | |
| Whether or not the model should return the last key/values attentions (not used by all models). Only | |
| relevant if `config.is_decoder=True`. | |
| classifier_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| Examples: | |
| ```python | |
| >>> from transformers import BertConfig, BertModel | |
| >>> # Initializing a BERT bert-base-uncased style configuration | |
| >>> configuration = BertConfig() | |
| >>> # Initializing a model (with random weights) from the bert-base-uncased style configuration | |
| >>> model = BertModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "bert" | |
| def __init__( | |
| self, | |
| vocab_size=30522, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=512, | |
| type_vocab_size=2, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| pad_token_id=0, | |
| position_embedding_type="absolute", | |
| use_cache=True, | |
| classifier_dropout=None, | |
| **kwargs, | |
| ): | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.intermediate_size = intermediate_size | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.position_embedding_type = position_embedding_type | |
| self.use_cache = use_cache | |
| self.classifier_dropout = classifier_dropout | |
| class BertOnnxConfig(OnnxConfig): | |
| def inputs(self) -> Mapping[str, Mapping[int, str]]: | |
| if self.task == "multiple-choice": | |
| dynamic_axis = {0: "batch", 1: "choice", 2: "sequence"} | |
| else: | |
| dynamic_axis = {0: "batch", 1: "sequence"} | |
| return OrderedDict( | |
| [ | |
| ("input_ids", dynamic_axis), | |
| ("attention_mask", dynamic_axis), | |
| ("token_type_ids", dynamic_axis), | |
| ] | |
| ) | |