r""" Instantiate a :class:`~transformers.EncoderDecoderConfig` (or a derived class) from a pre-trained encoder model configuration and

Gaussian Error Linear Unit. Original Implementation of the gelu activation function in Google Bert repo when initially created. For informati

See base class.

Returns :obj:`True` if index is already initialized.

projection

Split `text` in the biggest lines possible with the constraint of `max_len` using `prefix` on the first line and then indenting with the same

Collate `examples` into a batch, using the information in `tokenizer` for padding if necessary.

Creates examples for the training, dev and test sets.

Parse arguments and tokenize

Add a callback to the current list of :class:`~transformer.TrainerCallback`. Args: callback (:obj:`type` or :class:`~tran

Runs through the specific directory, looking for the files identified with `identifier`. Executes the doctests in those files

This method shows how to compute: - head attention entropy - head importance scores according to http://arxiv.org/abs/1905.10650

Loads a data file into a list of `InputBatch`s.

Create the code for the dummy object corresponding to `name`.

Run evaluation and returns metrics. The calling script will be responsible for providing a method to compute metrics, as they are ta

Return the properly gathered arrays and truncate to the number of samples (since the sampler added some extras to get each process a

r""" Instantiates an question encoder and a generator from one or two base classes of the library from pretrained model checkpoints.

From a ``pretrained_model_name_or_path``, resolve to a dictionary of parameters, to be used for instantiating a :class:`~transformers

Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the

Returns the training :class:`~torch.utils.data.DataLoader`. Will use no sampler if :obj:`self.train_dataset` does not implement :obj

Get the public list of all the models on huggingface, including the community models

Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It adds special tokens, tr

Call HF API to get a presigned url to upload `filename` to S3.

Print a 2D tensor

Prune a Conv1D layer to keep only entries in index. A Conv1D work as a Linear layer (see e.g. BERT) but the weights are transposed. Used

r""" Generates sequences for models with a language modeling head using multinomial sampling. Parameters: input_ids (:ob

Will save the model, so you can reload it using :obj:`from_pretrained()`.

Set the vebosity level for the 🤗 Transformers's root logger. Args: verbosity (:obj:`int`): Logging level, e.g., one of:

Train method to train the model.

Update config with summarization specific params.

Args: model: (:obj:`Union[torch.nn.DataParallel, torch.nn.parallel.DistributedDataParallel, torch.nn.modules.Module]`):

If tensors not close, or a and b arent both tensors, raise a nice Assertion error.

force one of token_ids to be generated by setting prob of all other tokens to 0 (logprob=-float("inf"))

Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy and pad_to_max_length

Build a resized Embedding Module from a provided token Embedding Module. Increasing the size will add newly initialized vectors at th

Tie or clone module weights depending of whether we are using TorchScript or not

Add a user input to the conversation for the next round. This populates the internal :obj:`new_user_input` field. Args:

r""" Generates sequences for models with a language modeling head using beam search decoding. Parameters: input_ids (:ob

Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and adding special tokens