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90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# coding=utf-8
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# Copyright 2019-present, the HuggingFace Inc. team, The Google AI Language Team and Facebook, Inc.
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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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""" DistilBERT model configuration """
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from __future__ import (absolute_import, division, print_function,
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unicode_literals)
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import sys
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import json
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import logging
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from io import open
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from .configuration_utils import PretrainedConfig
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logger = logging.getLogger(__name__)
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DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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'distilbert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-config.json",
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'distilbert-base-uncased-distilled-squad': "https://s3.amazonaws.com/models.huggingface.co/bert/distilbert-base-uncased-distilled-squad-config.json"
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}
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class DistilBertConfig(PretrainedConfig):
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pretrained_config_archive_map = DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
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def __init__(self,
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vocab_size_or_config_json_file=30522,
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max_position_embeddings=512,
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sinusoidal_pos_embds=True,
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n_layers=6,
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n_heads=12,
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dim=768,
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hidden_dim=4*768,
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dropout=0.1,
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attention_dropout=0.1,
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activation='gelu',
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initializer_range=0.02,
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tie_weights_=True,
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qa_dropout=0.1,
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seq_classif_dropout=0.2,
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**kwargs):
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super(DistilBertConfig, self).__init__(**kwargs)
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if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
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and isinstance(vocab_size_or_config_json_file, unicode)):
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with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
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json_config = json.loads(reader.read())
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for key, value in json_config.items():
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self.__dict__[key] = value
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elif isinstance(vocab_size_or_config_json_file, int):
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self.vocab_size = vocab_size_or_config_json_file
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self.max_position_embeddings = max_position_embeddings
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self.sinusoidal_pos_embds = sinusoidal_pos_embds
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self.n_layers = n_layers
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self.n_heads = n_heads
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self.dim = dim
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self.hidden_dim = hidden_dim
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.activation = activation
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self.initializer_range = initializer_range
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self.tie_weights_ = tie_weights_
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self.qa_dropout = qa_dropout
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self.seq_classif_dropout = seq_classif_dropout
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else:
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raise ValueError("First argument must be either a vocabulary size (int)"
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" or the path to a pretrained model config file (str)")
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@property
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def hidden_size(self):
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return self.dim
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@property
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def num_attention_heads(self):
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return self.n_heads
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@property
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def num_hidden_layers(self):
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return self.n_layers
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