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| # coding=utf-8 | |
| # Copyright 2022 The HuggingFace Team and Microsoft Research AI4Science 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. | |
| """ BioGPT model configuration""" | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "microsoft/biogpt": "https://huggingface.co./microsoft/biogpt/resolve/main/config.json", | |
| # See all BioGPT models at https://huggingface.co./models?filter=biogpt | |
| } | |
| class BioGptConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a [`BioGptModel`]. It is used to instantiate an | |
| BioGPT 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 BioGPT | |
| [microsoft/biogpt](https://huggingface.co./microsoft/biogpt) 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 42384): | |
| Vocabulary size of the BioGPT model. Defines the number of different tokens that can be represented by the | |
| `inputs_ids` passed when calling [`BioGptModel`]. | |
| hidden_size (`int`, *optional*, defaults to 1024): | |
| Dimension of the encoder layers and the pooler layer. | |
| num_hidden_layers (`int`, *optional*, defaults to 24): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (`int`, *optional*, defaults to 16): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (`int`, *optional*, defaults to 4096): | |
| Dimension 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 1024): | |
| 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). | |
| 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. | |
| scale_embedding (`bool`, *optional*, defaults to `True`): | |
| Scale embeddings by diving by sqrt(d_model). | |
| 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`. | |
| layerdrop (`float`, *optional*, defaults to 0.0): | |
| Please refer to the paper about LayerDrop: https://arxiv.org/abs/1909.11556 for further details | |
| activation_dropout (`float`, *optional*, defaults to 0.0): | |
| The dropout ratio for activations inside the fully connected layer. | |
| pad_token_id (`int`, *optional*, defaults to 1): | |
| Padding token id. | |
| bos_token_id (`int`, *optional*, defaults to 0): | |
| Beginning of stream token id. | |
| eos_token_id (`int`, *optional*, defaults to 2): | |
| End of stream token id. | |
| Example: | |
| ```python | |
| >>> from transformers import BioGptModel, BioGptConfig | |
| >>> # Initializing a BioGPT microsoft/biogpt style configuration | |
| >>> configuration = BioGptConfig() | |
| >>> # Initializing a model from the microsoft/biogpt style configuration | |
| >>> model = BioGptModel(configuration) | |
| >>> # Accessing the model configuration | |
| >>> configuration = model.config | |
| ```""" | |
| model_type = "biogpt" | |
| def __init__( | |
| self, | |
| vocab_size=42384, | |
| hidden_size=1024, | |
| num_hidden_layers=24, | |
| num_attention_heads=16, | |
| intermediate_size=4096, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=1024, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-12, | |
| scale_embedding=True, | |
| use_cache=True, | |
| layerdrop=0.0, | |
| activation_dropout=0.0, | |
| pad_token_id=1, | |
| bos_token_id=0, | |
| eos_token_id=2, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.max_position_embeddings = max_position_embeddings | |
| 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.initializer_range = initializer_range | |
| self.layer_norm_eps = layer_norm_eps | |
| self.scale_embedding = scale_embedding | |
| self.use_cache = use_cache | |
| self.layerdrop = layerdrop | |
| self.activation_dropout = activation_dropout | |
| super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |