class allennlp.data.dataset_readers.conll2000.Conll2000DatasetReader(token_indexers: typing.Dict[str, allennlp.data.token_indexers.token_indexer.TokenIndexer] = 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:

WORD POS-TAG CHUNK-TAG

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.

Parameters: 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[allennlp.data.tokenizers.token.Token], pos_tags: typing.List[str] = None, chunk_tags: typing.List[str] = None) → allennlp.data.instance.Instance[source]

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