token_indexers: Dict[str,] = None,
    tag_label: str = 'chunk',
    feature_labels: Sequence[str] = (),
    coding_scheme: str = 'BIO',
    label_namespace: str = 'labels',
) -> None

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.

Registered as a DatasetReader with name "conll2000".


  • token_indexers : Dict[str, TokenIndexer], optional (default={"tokens": SingleIdTokenIndexer()})
  • We use this to define the input representation for the text. See :class: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.


    tokens: List[],
    pos_tags: List[str] = None,
    chunk_tags: List[str] = None,
) ->

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