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| # coding=utf-8 | |
| # Copyright The HuggingFace team. 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. | |
| """ ConvBERT 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__) | |
| CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json", | |
| "YituTech/conv-bert-medium-small": ( | |
| "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json" | |
| ), | |
| "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json", | |
| # See all ConvBERT models at https://huggingface.co/models?filter=convbert | |
| } | |
| class ConvBertConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`ConvBertModel`]. It is used to instantiate an | |
| ConvBERT 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 ConvBERT | |
| [YituTech/conv-bert-base](https://huggingface.co/YituTech/conv-bert-base) 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 ConvBERT model. Defines the number of different tokens that can be represented by | |
| the `inputs_ids` passed when calling [`ConvBertModel`] or [`TFConvBertModel`]. | |
| 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" (i.e., feed-forward) layer in the Transformer encoder. | |
| hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, | |
| `"relu"`, `"selu"` and `"gelu_new"` are supported. | |
| hidden_dropout_prob (`float`, *optional*, defaults to 0.1): | |
| The dropout probabilitiy 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 [`ConvBertModel`] or [`TFConvBertModel`]. | |
| 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. | |
| head_ratio (`int`, *optional*, defaults to 2): | |
| Ratio gamma to reduce the number of attention heads. | |
| num_groups (`int`, *optional*, defaults to 1): | |
| The number of groups for grouped linear layers for ConvBert model | |
| conv_kernel_size (`int`, *optional*, defaults to 9): | |
| The size of the convolutional kernel. | |
| classifier_dropout (`float`, *optional*): | |
| The dropout ratio for the classification head. | |
| Example: | |
| ```python | |
| >>> from transformers import ConvBertConfig, ConvBertModel | |
| >>> # Initializing a ConvBERT convbert-base-uncased style configuration | |
| >>> configuration = ConvBertConfig() | |
| >>> # Initializing a model (with random weights) from the convbert-base-uncased style configuration | |
| >>> model = ConvBertModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "convbert" | |
| 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=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| embedding_size=768, | |
| head_ratio=2, | |
| conv_kernel_size=9, | |
| num_groups=1, | |
| classifier_dropout=None, | |
| **kwargs, | |
| ): | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_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.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| 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.embedding_size = embedding_size | |
| self.head_ratio = head_ratio | |
| self.conv_kernel_size = conv_kernel_size | |
| self.num_groups = num_groups | |
| self.classifier_dropout = classifier_dropout | |
| # Copied from transformers.models.bert.configuration_bert.BertOnnxConfig | |
| class ConvBertOnnxConfig(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), | |
| ] | |
| ) | |