mirror of
https://github.com/huggingface/transformers.git
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115 lines
4.1 KiB
Python
115 lines
4.1 KiB
Python
# coding=utf-8
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# Copyright 2010, XXX authors
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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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""" XXX model configuration """
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import logging
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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XXX_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"xxx-base-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-base-uncased-config.json",
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"xxx-large-uncased": "https://s3.amazonaws.com/models.huggingface.co/bert/xxx-large-uncased-config.json",
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}
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class XxxConfig(PretrainedConfig):
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r"""
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:class:`~transformers.XxxConfig` is the configuration class to store the configuration of a
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`XxxModel`.
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Arguments:
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vocab_size: Vocabulary size of `inputs_ids` in `XxxModel`.
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hidden_size: Size of the encoder layers and the pooler layer.
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num_hidden_layers: Number of hidden layers in the Transformer encoder.
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num_attention_heads: Number of attention heads for each attention layer in
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the Transformer encoder.
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intermediate_size: The size of the "intermediate" (i.e., feed-forward)
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layer in the Transformer encoder.
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hidden_act: The non-linear activation function (function or string) in the
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encoder and pooler. If string, "gelu", "relu", "swish" and "gelu_new" are supported.
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hidden_dropout_prob: The dropout probabilitiy for all fully connected
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layers in the embeddings, encoder, and pooler.
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attention_probs_dropout_prob: The dropout ratio for the attention
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probabilities.
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max_position_embeddings: The maximum sequence length that this model might
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ever be used with. Typically set this to something large just in case
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(e.g., 512 or 1024 or 2048).
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type_vocab_size: The vocabulary size of the `token_type_ids` passed into
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`XxxModel`.
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initializer_range: The sttdev of the truncated_normal_initializer for
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initializing all weight matrices.
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layer_norm_eps: The epsilon used by LayerNorm.
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"""
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pretrained_config_archive_map = XXX_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(
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self,
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vocab_size=50257,
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n_positions=1024,
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n_ctx=1024,
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n_embd=768,
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n_layer=12,
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n_head=12,
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attn_pdrop=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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summary_type="cls_index",
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summary_use_proj=True,
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summary_activation=None,
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summary_proj_to_labels=True,
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summary_first_dropout=0.1,
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**kwargs
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):
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super(XxxConfig, self).__init__(**kwargs)
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self.vocab_size = vocab_size
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self.n_ctx = n_ctx
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.summary_type = summary_type
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self.summary_use_proj = summary_use_proj
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self.summary_activation = summary_activation
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self.summary_first_dropout = summary_first_dropout
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self.summary_proj_to_labels = summary_proj_to_labels
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@property
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def max_position_embeddings(self):
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return self.n_positions
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@property
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def hidden_size(self):
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return self.n_embd
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@property
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def num_attention_heads(self):
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return self.n_head
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@property
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def num_hidden_layers(self):
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return self.n_layer
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