7fd1febf38
* add conversion script * improve conversion script * make style * add tryout files * fix * update * add causal bert * better names * add tokenizer file as well * finish causal_bert * fix small bugs * improve generate * change naming * renaming * renaming * renaming * remove leftover files * clean files * add fix tokenizer * finalize * correct slow test * update docs * small fixes * fix link * adapt check repo * apply sams and sylvains recommendations * fix import * implement Lysandres recommendations * fix logger warn
107 lines
5.1 KiB
Python
107 lines
5.1 KiB
Python
# coding=utf-8
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# Copyright 2020 The Google AI Language Team Authors and The HuggingFace Inc. team.
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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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""" BertForSeqGeneration model configuration """
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from .configuration_utils import PretrainedConfig
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class BertGenerationConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a :class:`~transformers.BertGenerationPreTrainedModel`.
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It is used to instantiate a BertGenerationConfig model according to the specified arguments, defining the model 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 50358):
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Vocabulary size of the BertForSeqGeneration 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.BertForSeqGeneration`.
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hidden_size (:obj:`int`, `optional`, defaults to 1024):
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Dimensionality of the encoder layers and the pooler layer.
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num_hidden_layers (:obj:`int`, `optional`, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (:obj:`int`, `optional`, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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intermediate_size (:obj:`int`, `optional`, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
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The non-linear activation function (function or string) in the encoder and pooler.
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If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"swish"` and :obj:`"gelu_new"` are supported.
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hidden_dropout_prob (: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_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1):
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The dropout ratio for the attention probabilities.
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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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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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layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
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The epsilon used by the layer normalization layers.
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gradient_checkpointing (:obj:`bool`, `optional`, defaults to :obj:`False`):
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If :obj:`True`, use gradient checkpointing to save memory at the expense of slower backward pass.
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Example::
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>>> from transformers import BertGenerationConfig, BertGenerationEncoder
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>>> # Initializing a BertForSeqGeneration config
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>>> configuration = BertGenerationConfig()
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>>> # Initializing a modelfrom the config
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>>> model = BertGenerationEncoder(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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"""
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model_type = "bert-for-seq-generation"
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def __init__(
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self,
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vocab_size=50358,
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hidden_size=1024,
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num_hidden_layers=24,
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num_attention_heads=16,
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intermediate_size=4096,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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bos_token_id=2,
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eos_token_id=1,
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gradient_checkpointing=False,
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**kwargs
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):
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.intermediate_size = intermediate_size
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.gradient_checkpointing = gradient_checkpointing
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