class typing.Dict[str,] = None, tag_label: str = 'chunk', feature_labels: typing.Sequence[str] = (), lazy: bool = False, coding_scheme: str = 'BIO', label_namespace: str = 'labels') → None[source]


Reads instances from a pretokenised file where each line is in the following format:


with a blank line indicating the end of each sentence and converts it into a Dataset suitable for sequence tagging.

Each Instance contains the words in the "tokens" TextField. The values corresponding to the tag_label values will get loaded into the "tags" SequenceLabelField. And if you specify any feature_labels (you probably shouldn’t), the corresponding values will get loaded into their own SequenceLabelField s.

token_indexers : Dict[str, TokenIndexer], optional (default=``{“tokens”: SingleIdTokenIndexer()}``)

We use this to define the input representation for the text. See TokenIndexer.

tag_label: ``str``, optional (default=``chunk``)

Specify pos, or chunk to have that tag loaded into the instance field tag.

feature_labels: ``Sequence[str]``, optional (default=``()``)

These labels will be loaded as features into the corresponding instance fields: pos -> pos_tags or chunk -> chunk_tags. Each will have its own namespace: pos_tags or chunk_tags. If you want to use one of the tags as a feature in your model, it should be specified here.

coding_scheme: ``str``, optional (default=``BIO``)

Specifies the coding scheme for chunk_labels. Valid options are BIO and BIOUL. The BIO default maintains the original BIO scheme in the CoNLL 2000 chunking data. In the BIO scheme, B is a token starting a span, I is a token continuing a span, and O is a token outside of a span.

label_namespace: ``str``, optional (default=``labels``)

Specifies the namespace for the chosen tag_label.

text_to_instance(tokens: typing.List[], pos_tags: typing.List[str] = None, chunk_tags: typing.List[str] = None) →[source]

We take pre-tokenized input here, because we don’t have a tokenizer in this class.