Args: inputs (`torch.FloatTensor`) The input matrix from which the binarizer computes the binary mask.

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

Makes broadcastable attention mask and causal mask so that future and maked tokens are ignored. Arguments: attention_mask: torch.

See base class.

Conv1D layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2) Basically works like a Linear layer but the weights are

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

See base class.

list(map(f, x))

Just EOS

Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size. Take care of tying weights embeddings afterwards if

Saving best-practices: if you use default names for the model, you can reload it using from_pretrained(). Will only save fro

Serializes this instance to a Python dictionary.

Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see WordPieceTokenizer. Args:

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

Small helper to check pseudo-equality of parsed arguments on `ArgumentParser` instances.

Context Manager allowing tensor allocation on the user-specified device in framework agnostic way. example: # Explicitly

Constructs a `ModelCard` from a Python dictionary of parameters.

r""" Instantiates one of the base model classes of the library from a pre-trained model configuration. The `from_pretrained()` method

See base class.

Get token or None if not existent.

Resolve a model identifier, and a file name, to a HF-hosted url on either S3 or Cloudfront (a Content Delivery Network, or CDN). Cloudfr

Run prediction and return predictions and potential metrics. Depending on the dataset and your use case, your test dataset may conta

Creates examples for the training, dev and test sets.

Parse arguments and tokenize

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

Loads a data file into a list of `InputFeatures` `cls_token_at_end` define the location of the CLS token: - False (Default, BERT/

Ensure PyTorch tensors are on the specified device. :param inputs: :return:

r""" Instantiates an encoder and a decoder from one or two base classes of the library from pre-trained model checkpoints. The model is set

type: torch.Tensor -> torch.Tensor

Load datasets. Called after prepare data.

Get number of (optionally, trainable) parameters in the module.

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

Converts a list of examples into a list of features that can be directly given as input to a model. It is model-dependant and takes advantage

Tokenizes a piece of text.

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