allennlp.data.dataset

A Batch represents a collection of Instance s to be fed through a model.

class allennlp.data.dataset.Batch(instances: typing.Iterable[allennlp.data.instance.Instance]) → None[source]

Bases: typing.Iterable

A batch of Instances. In addition to containing the instances themselves, it contains helper functions for converting the data into tensors.

as_tensor_dict(padding_lengths: typing.Dict[str, typing.Dict[str, int]] = None, cuda_device: int = -1, verbose: bool = False) → typing.Dict[str, typing.Union[torch.Tensor, typing.Dict[str, torch.Tensor]]][source]

This method converts this Batch into a set of pytorch Tensors that can be passed through a model. In order for the tensors to be valid tensors, all Instances in this batch need to be padded to the same lengths wherever padding is necessary, so we do that first, then we combine all of the tensors for each field in each instance into a set of batched tensors for each field.

Parameters:
padding_lengths : Dict[str, Dict[str, int]]

If a key is present in this dictionary with a non-None value, we will pad to that length instead of the length calculated from the data. This lets you, e.g., set a maximum value for sentence length if you want to throw out long sequences.

Entries in this dictionary are keyed first by field name (e.g., “question”), then by padding key (e.g., “num_tokens”).

cuda_device : int

If cuda_device >= 0, GPUs are available and Pytorch was compiled with CUDA support, the tensor will be copied to the cuda_device specified.

verbose : bool, optional (default=``False``)

Should we output logging information when we’re doing this padding? If the batch is large, this is nice to have, because padding a large batch could take a long time. But if you’re doing this inside of a data generator, having all of this output per batch is a bit obnoxious (and really slow).

Returns:
tensors : Dict[str, DataArray]

A dictionary of tensors, keyed by field name, suitable for passing as input to a model. This is a batch of instances, so, e.g., if the instances have a “question” field and an “answer” field, the “question” fields for all of the instances will be grouped together into a single tensor, and the “answer” fields for all instances will be similarly grouped in a parallel set of tensors, for batched computation. Additionally, for complex Fields, the value of the dictionary key is not necessarily a single tensor. For example, with the TextField, the output is a dictionary mapping TokenIndexer keys to tensors. The number of elements in this sub-dictionary therefore corresponds to the number of TokenIndexers used to index the TextField. Each Field class is responsible for batching its own output.

get_padding_lengths() → typing.Dict[str, typing.Dict[str, int]][source]

Gets the maximum padding lengths from all Instances in this batch. Each Instance has multiple Fields, and each Field could have multiple things that need padding. We look at all fields in all instances, and find the max values for each (field_name, padding_key) pair, returning them in a dictionary.

This can then be used to convert this batch into arrays of consistent length, or to set model parameters, etc.

index_instances(vocab: allennlp.data.vocabulary.Vocabulary) → None[source]
print_statistics() → None[source]