12/11/2023 0 Comments Pytorch cross entropy loss![]() Check out the :meth:`~pytorch_pretrained` method to load the model weights. Initializing with a config file does not load the weights associated with the model, only the configuration. _`torch.nn.Module`: Parameters: config (:class:`~pytorch_transformers.RobertaConfig`): Model configuration class with all the parameters of the model. _`RoBERTa: A Robustly Optimized BERT Pretraining Approach`. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. This model is a PyTorch `torch.nn.Module`_ sub-class. This implementation is the same as BertModel with a tiny embeddings tweak as well as a setup for Roberta pretrained models. It builds on BERT and modifies key hyperparameters, removing the next-sentence pretraining objective and training with much larger mini-batches and learning rates. It is based on Google's BERT model released in 2018. class RobertaConfig ( BertConfig ): pretrained_config_archive_map = ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP ROBERTA_START_DOCSTRING = r """ The RoBERTa model was proposed in `RoBERTa: A Robustly Optimized BERT Pretraining Approach`_ by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov. forward ( input_ids, token_type_ids = token_type_ids, position_ids = position_ids ) expand_as ( input_ids ) return super ( RobertaEmbeddings, self ). fairseq's `utils.make_positions` position_ids = torch. size ( 1 ) if position_ids is None : # Position numbers begin at padding_idx+1. padding_idx = 1 def forward ( self, input_ids, token_type_ids = None, position_ids = None ): seq_length = input_ids. """ def _init_ ( self, config ): super ( RobertaEmbeddings, self ). getLogger ( _name_ ) ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP = class RobertaEmbeddings ( BertEmbeddings ): """ Same as BertEmbeddings with a tiny tweak for positional embeddings indexing. """ from _future_ import ( absolute_import, division, print_function, unicode_literals ) import logging import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import CrossEntropyLoss, MSELoss from pytorch_transformers.modeling_bert import ( BertConfig, BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu ) from pytorch_transformers.modeling_utils import add_start_docstrings logger = logging. ![]() # See the License for the specific language governing permissions and # limitations under the License. # You may obtain a copy of the License at # 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. # Licensed under the Apache License, Version 2.0 (the "License") # you may not use this file except in compliance with the License. # Copyright (c) 2018, NVIDIA CORPORATION. # coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. ![]()
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