Add docstrings for simple_ner
@@ -14,6 +14,20 @@ from ...util import registry
def BiluoTagger(
tok2vec: Model[List[Doc], List[Floats2d]]
) -> Model[List[Doc], List[Floats2d]]:
+ """Construct a simple NER tagger, that predicts BILUO tag scores for each
+ token and uses greedy decoding with transition-constraints to return a valid
+ BILUO tag sequence.
+
+ A BILUO tag sequence encodes a sequence of non-overlapping labelled spans
+ into tags assigned to each token. The first token of a span is given the
+ tag B-LABEL, the last token of the span is given the tag L-LABEL, and tokens
+ within the span are given the tag U-LABEL. Single-token spans are given
+ the tag U-LABEL. All other tokens are assigned the tag O.
+
+ The BILUO tag scheme generally results in better linear separation between
+ classes, especially for non-CRF models, because there are more distinct classes
+ for the different situations (Ratinov et al., 2009).
+ """
biluo = BILUO()
linear = Linear(
nO=None, nI=tok2vec.get_dim("nO"), init_W=configure_normal_init(mean=0.02)
@@ -41,6 +55,15 @@ def BiluoTagger(
def IOBTagger(
tok2vec: Model[List[Doc], List[Floats2d]]
) -> Model[List[Doc], List[Floats2d]]:
+ """Construct a simple NER tagger, that predicts IOB tag scores for each
+ token and uses greedy decoding with transition-constraints to return a valid
+ IOB tag sequence.
+
+ A IOB tag sequence encodes a sequence of non-overlapping labelled spans
+ into tags assigned to each token. The first token of a span is given the
+ tag B-LABEL, and subsequent tokens are given the tag I-LABEL.
+ All other tokens are assigned the tag O.
+ """
biluo = IOB()
linear = Linear(nO=None, nI=tok2vec.get_dim("nO"))
model = chain(
