This subclass of `argparse.ArgumentParser` uses type hints on dataclasses to generate arguments. The class is designed to play well with the

Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Args: model (:class:`~tra

Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.). Args: do_lower_case (:obj:

Implements Adam algorithm with weight decay fix as introduced in `Decoupled Weight Decay Regularization <https://arxiv.org/abs/1711.05101>`__

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (:obj:`torch.FloatTensor` of sh

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added betwee

A single training/test example for simple sequence classification. Args: guid: Unique id for the example. text_a: string. Th

AddedToken represents a token to be added to a Tokenizer An AddedToken can have special options defining the way it should behave.

TrainingArguments is the subset of the arguments we use in our example scripts **which relate to the training loop itself**. Using :clas

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (:obj:`tf.Tensor` of shape :obj

Holds the output of the :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.encode_plus` and :meth:`~transformers.tokenizati

Base class for outputs of sentence classification models. Args: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, ret

`Memory` NamedTuple have a single field `bytes` and you can get a human readable str of the number of mega bytes by calling `__repr__`

r""" This is the configuration class to store the configuration of a :class:`~transformers.BartModel`. It is used to instantiate a BART model

Utility class containing a conversation and its history. This class is meant to be used as an input to the :class:`~transformers.Conversation

Base class for masked language models outputs. Args: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned when

r""" Construct shared token embeddings. The weights of the embedding layer is usually shared with the weights of the linear decoder when doin

Base class for outputs of token classification models. Args: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, return

1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). Basically works like a linear layer but the wei

Base class for outputs of question answering models. Args: loss (:obj:`torch.FloatTensor` of shape :obj:`(1,)`, `optional`, returned

Retriever used to get documents from vector queries. It retrieves the documents embeddings as well as the documents contents, and it formats

r""" This is the configuration class to store the configuration of a :class:`~transformers.ReformerModel`. It is used to instantiate a Reforme

r""" :class:`~transformers.DPRConfig` is the configuration class to store the configuration of a `DPRModel`. This is the configuration class

Base class for outputs of multiple choice models. Args: loss (:obj:`torch.FloatTensor` of shape `(1,)`, `optional`, returned when :o

Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they are not all of the same leng

https://arxiv.org/pdf/2004.02984.pdf

The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added betwee

Base class for masked language models outputs. Args: loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :obj:`la

Base class for outputs of multiple choice models. Args: loss (:obj:`tf.Tensor` of shape `(1,)`, `optional`, returned when :obj:`labe

Base class for outputs of question answering models. Args: loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when :o

Base class for outputs of sentence classification models. Args: loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned wh

Base class for outputs of token classification models. Args: loss (:obj:`tf.Tensor` of shape :obj:`(1,)`, `optional`, returned when

This class copied code from :class:`~transformers.RobertaModel` and overwrote standard self-attention with longformer self-attention to provi

Runs basic tokenization with MeCab morphological parser.

1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2). Basically works like a linear layer but the wei

r""" Construct a BERT tokenizer. Based on WordPiece. This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains m

A dataset that calls prepare_seq2seq_batch.

r""" Compute a single vector summary of a sequence hidden states. Args: config (:class:`~transformers.PretrainedConfig`):

Optimized dropout module for stabilizing the training Args: drop_prob (float): the dropout probabilities

Compute a single vector summary of a sequence hidden states. Args: config (:class:`~transformers.PretrainedConfig`): The

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (:obj:`torch.FloatTen

Fully Connected layer with on the fly adaptive mask. If needed, a score matrix is created to store the importance of each associated weight.

This module produces sinusoidal positional embeddings of any length.

This module produces sinusoidal positional embeddings of any length. We don't want to save the weight of this embedding since it's not train

Constructs a SquadResult which can be used to evaluate a model's output on the SQuAD dataset. Args: unique_id: The unique identifier

Runs WordPiece tokenization.

Base class for model's outputs, with potential hidden states and attentions. Args: last_hidden_state (:obj:`torch.FloatTensor` of sh

r""" :class:`transformers.BeamScorer` implementing standard beam search decoding. Adapted in part from `Facebook's XLM beam search code <

r""" This is the configuration class to store the configuration of a :class:`~transformers.BertModel` or a :class:`~transformers.TFBertModel`.

Data collator that will dynamically pad the inputs received, as well as the labels. Args: tokenizer (:class:`~transformers.PreTraine

A class responsible for properly gathering tensors (or nested list/tuple of tensors) on the CPU by chunks. If our dataset has 16 samples wit

Base class for model's outputs that also contains a pooling of the last hidden states. Args: last_hidden_state (:obj:`tf.Tensor` of