A TextField represents a string of text, the kind that you might want to represent with standard word vectors, or pass through an LSTM.


    tokens: List[],
    token_indexers: Dict[str,],
) -> None

This Field represents a list of string tokens. Before constructing this object, you need to tokenize raw strings using a

Because string tokens can be represented as indexed arrays in a number of ways, we also take a dictionary of objects that will be used to convert the tokens into indices. Each TokenIndexer could represent each token as a single ID, or a list of character IDs, or something else.

This field will get converted into a dictionary of arrays, one for each TokenIndexer. A SingleIdTokenIndexer produces an array of shape (num_tokens,), while a TokenCharactersIndexer produces an array of shape (num_tokens, num_characters).


    padding_lengths: Dict[str, int],
) -> Dict[str, torch.Tensor]

Given a set of specified padding lengths, actually pad the data in this field and return a torch Tensor (or a more complex data structure) of the correct shape. We also take a couple of parameters that are important when constructing torch Tensors.


  • padding_lengths : Dict[str, int] This dictionary will have the same keys that were produced in
  • :func:get_padding_lengths. The values specify the lengths to use when padding each relevant dimension, aggregated across all instances in a batch.


    tensor_list: List[Dict[str, Dict[str, torch.Tensor]]],
) -> Dict[str, Dict[str, torch.Tensor]]

Takes the output of Field.as_tensor() from a list of Instances and merges it into one batched tensor for this Field. The default implementation here in the base class handles cases where as_tensor returns a single torch tensor per instance. If your subclass returns something other than this, you need to override this method.

This operation does not modify self, but in some cases we need the information contained in self in order to perform the batching, so this is an instance method, not a class method.


TextField.count_vocab_items(self, counter:Dict[str, Dict[str, int]])

If there are strings in this field that need to be converted into integers through a :class:Vocabulary, here is where we count them, to determine which tokens are in or out of the vocabulary.

If your Field does not have any strings that need to be converted into indices, you do not need to implement this method.

A note on this counter: because Fields can represent conceptually different things, we separate the vocabulary items by namespaces. This way, we can use a single shared mechanism to handle all mappings from strings to integers in all fields, while keeping words in a TextField from sharing the same ids with labels in a LabelField (e.g., "entailment" or "contradiction" are labels in an entailment task)

Additionally, a single Field might want to use multiple namespaces - TextFields can be represented as a combination of word ids and character ids, and you don't want words and characters to share the same vocabulary - "a" as a word should get a different id from "a" as a character, and the vocabulary sizes of words and characters are very different.

Because of this, the first key in the counter object is a namespace, like "tokens", "token_characters", "tags", or "labels", and the second key is the actual vocabulary item.


TextField.get_padding_lengths(self) -> Dict[str, int]

The TextField has a list of Tokens, and each Token gets converted into arrays by (potentially) several TokenIndexers. This method gets the max length (over tokens) associated with each of these arrays.



Given a :class:Vocabulary, converts all strings in this field into (typically) integers. This modifies the Field object, it does not return anything.

If your Field does not have any strings that need to be converted into indices, you do not need to implement this method.


TextField.sequence_length(self) -> int

How many elements are there in this sequence?