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- # coding=utf-8
- # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
- # Copyright (c) 2018, NVIDIA CORPORATION. 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.
- """ RoBERTa configuration """
-
- from __future__ import (absolute_import, division, print_function,
- unicode_literals)
-
- import logging, json, sys
- from io import open
-
- from .configuration_utils import PretrainedConfig
- from .configuration_bert import BertConfig
-
- logger = logging.getLogger(__name__)
-
- ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = {
- 'roberta-base': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-base-config.json",
- 'roberta-large': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-config.json",
- 'roberta-large-mnli': "https://s3.amazonaws.com/models.huggingface.co/bert/roberta-large-mnli-config.json",
- }
-
- class RobertaConfig(BertConfig):
- pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
-
- # class RobertaConfig(PretrainedConfig):
- # r"""
- # :class:`~pytorch_transformers.BertConfig` is the configuration class to store the configuration of a
- # `BertModel`.
-
-
- # Arguments:
- # vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `BertModel`.
- # hidden_size: Size of the encoder layers and the pooler layer.
- # num_hidden_layers: Number of hidden layers in the Transformer encoder.
- # num_attention_heads: Number of attention heads for each attention layer in
- # the Transformer encoder.
- # intermediate_size: The size of the "intermediate" (i.e., feed-forward)
- # layer in the Transformer encoder.
- # hidden_act: The non-linear activation function (function or string) in the
- # encoder and pooler. If string, "gelu", "relu" and "swish" are supported.
- # hidden_dropout_prob: The dropout probabilitiy for all fully connected
- # layers in the embeddings, encoder, and pooler.
- # attention_probs_dropout_prob: The dropout ratio for the attention
- # probabilities.
- # max_position_embeddings: 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: The vocabulary size of the `token_type_ids` passed into
- # `BertModel`.
- # initializer_range: The sttdev of the truncated_normal_initializer for
- # initializing all weight matrices.
- # layer_norm_eps: The epsilon used by LayerNorm.
- # """
- # pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP
-
- # def __init__(self,
- # vocab_size_or_config_json_file=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,
- # padding_idx=1,
- # **kwargs):
- # super(RobertaConfig, self).__init__(**kwargs)
- # if isinstance(vocab_size_or_config_json_file, str) or (sys.version_info[0] == 2
- # and isinstance(vocab_size_or_config_json_file, unicode)):
- # with open(vocab_size_or_config_json_file, "r", encoding='utf-8') as reader:
- # json_config = json.loads(reader.read())
- # for key, value in json_config.items():
- # self.__dict__[key] = value
- # elif isinstance(vocab_size_or_config_json_file, int):
- # self.vocab_size = vocab_size_or_config_json_file
- # self.hidden_size = hidden_size
- # self.num_hidden_layers = num_hidden_layers
- # self.num_attention_heads = num_attention_heads
- # self.hidden_act = hidden_act
- # self.intermediate_size = intermediate_size
- # 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.padding_idx = padding_idx
- # else:
- # raise ValueError("First argument must be either a vocabulary size (int)"
- # " or the path to a pretrained model config file (str)")
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