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