r""" Instantiate a [`ModelCard`] from a pre-trained model model card. Parameters: pretrained_model_name_or_path: either:

r""" # Using torch.hub ! import torch model = torch.hub.load('huggingface/transformers', 'model', 'google-bert/be

Number of byte required for this data type Returns: Integer > 0

separate heads

Will look for the words added to the trie within `text`. Output is the original string splitted along the boundaries of the words fou

Creates a random int32 tensor of the shape within the vocab size.

Concatenates transformations along a new dimension. Args: ts: A list of T objects dim:

Analogous to torch.unsqueeze. The dimension is relative to the shared dimensions of the rotation/translation. Args: dim:

Called when a proxy object is has the keys() method called. This is what happens when ** is called on a proxy. This should return an iterator

Uses the `pre_processor` to prepare the inputs for the `model`.

Save a model card object to the directory or file `save_directory_or_file`.

r""" Safety checker that arguments are correct - also replaces some NoneType arguments with their default values.

r""" # Using torch.hub ! import torch tokenizer = torch.hub.load('huggingface/transformers', 'tokenizer', 'google-bert/bert-b

Uses the `post_processor` to decode the model output.

Loads the dictionary from a text file with the format: ``` <symbol0> <count0> <symbol1> <count1> ... ```

Creates a `keras.initializers.TruncatedNormal` with the given range. Args: initializer_range (*float*, defaults to 0.02): Standard d

This method forwards all its arguments to BertTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of

Returns a copy of the Rotation whose underlying Tensor has been detached from its torch graph. Returns: A copy of the Ro

Expands 2-dimensional `image` to 3 dimensions. Args: image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):

Prune a linear layer to keep only entries in index. Used to remove heads. Args: layer (`torch.nn.Linear`): The layer to prune.

Tokenize the provided input and eventually returns corresponding tokens id: - **text_input**: String to tokenize - **return_ids**: Bo

Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`. Parameters: key_states (`torch

Returns the mean of the distribution.

Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the vocabulary. Args:

Prepare a table and a string for the model. Args: table (`pd.DataFrame`): Table containing tabular data.

Creates a random float32 tensor

Main training entry point. Args: resume_from_checkpoint (`str` or `bool`, *optional*): If a `str`, local

Utility factory method to build a [`Pipeline`]. Pipelines are made of: - A [tokenizer](tokenizer) in charge of mapping raw textual

Log `logs` on the various objects watching training. Subclass and override this method to inject custom behavior. Args:

Convert self to a tuple containing all the attributes/keys that are not `None`.

Prepare a table and a list of strings for the model. <Tip warning={true}> This method is deprecated, `__call__` should be u

Overwrites the `parse` method to incorporate a skip for CUDA tests, and remove logs and dataset prints before calling `super().parse`

Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length in the batch. Padding s

Simplifies an object by rounding float numbers, and downcasting tensors/numpy arrays to get simple equality test within tests.

Serializes this instance to a Python dictionary.

Converts the old size parameter in the config into the new dict expected in the config. This is to ensure backwards compatibility with the ol

Creates a new instance of this constraint. Args: stateful(`bool`): Whether to not only copy the constraint for new insta

This is a thin wrapper that sets the model's loss output head as the loss if the user does not specify a loss function themselves.

Map inputs to attention experts according to routing decision and compute query projection inside each experts.

Saves the relevant code files for your tool so it can be pushed to the Hub. This will copy the code of your tool in `output_dir` as w

Flatten a nested dict into a single level dict.

Applies the transformation to a coordinate tensor. Args: pts: A [*, 3] coordinate tensor. Returns: T

Upload the tool to the Hub. For this method to work properly, your tool must have been defined in a separate module (not `__main__`)

Serializes this instance to a JSON string.

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

Returns the (height, width) dimensions of the image. Args: image (`np.ndarray`): The image to get the dimensions of.

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