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	throw custom error when state_type is invalid
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					@ -480,6 +480,8 @@ class Errors:
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    E201 = ("Span index out of range.")
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					    E201 = ("Span index out of range.")
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    # TODO: fix numbering after merging develop into master
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					    # TODO: fix numbering after merging develop into master
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					    E917 = ("Received invalid value {value} for 'state_type' in "
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					            "TransitionBasedParser: only 'parser' or 'ner' are valid options.")
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    E918 = ("Received invalid value for vocab: {vocab} ({vocab_type}). Valid "
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					    E918 = ("Received invalid value for vocab: {vocab} ({vocab_type}). Valid "
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            "values are an instance of spacy.vocab.Vocab or True to create one"
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					            "values are an instance of spacy.vocab.Vocab or True to create one"
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            " (default).")
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					            " (default).")
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					@ -2,6 +2,7 @@ from typing import Optional, List
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from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
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					from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
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from thinc.types import Floats2d
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					from thinc.types import Floats2d
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					from ... import Errors
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from ...util import registry
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					from ...util import registry
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from .._precomputable_affine import PrecomputableAffine
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					from .._precomputable_affine import PrecomputableAffine
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from ..tb_framework import TransitionModel
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					from ..tb_framework import TransitionModel
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					@ -66,7 +67,7 @@ def build_tb_parser_model(
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    elif state_type == "ner":
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					    elif state_type == "ner":
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        nr_feature_tokens = 6 if extra_state_tokens else 3
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					        nr_feature_tokens = 6 if extra_state_tokens else 3
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    else:
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					    else:
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        raise ValueError(f"unknown state type {state_type}")  # TODO error
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					        raise ValueError(Errors.E917.format(value=state_type))
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    t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
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					    t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
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    tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width))
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					    tok2vec = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width))
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    tok2vec.set_dim("nO", hidden_width)
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					    tok2vec.set_dim("nO", hidden_width)
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