Given a sequence corresponding to BIO tags, extracts spans.
    Spans are inclusive and can be of zero length, representing a single word span.
    Ill-formed spans are also included (i.e those which do not start with a "B-LABEL"),
    as otherwise it is possible to get a perfect precision score whilst still predicting
    ill-formed spans in addition to the correct spans. This function works properly when
    the spans are unlabeled (i.e., your labels are simply "B", "I", and "O").

    Parameters
    ----------
    tag_sequence : List[str], required.
        The integer class labels for a sequence.
    classes_to_ignore : List[str], optional (default = None).
        A list of string class labels `excluding` the bio tag
        which should be ignored when extracting spans.

    Returns
    -------
    spans : List[TypedStringSpan]
        The typed, extracted spans from the sequence, in the format (label, (span_start, span_end)).
        Note that the label `does not` contain any BIO tag prefixes.

def bio_tags_to_spans(tag_sequence: List[str],
                      classes_to_ignore: List[str] = None) -> List[TypedStringSpan]:
    """
    Given a sequence corresponding to BIO tags, extracts spans.
    Spans are inclusive and can be of zero length, representing a single word span.
    Ill-formed spans are also included (i.e those which do not start with a "B-LABEL"),
    as otherwise it is possible to get a perfect precision score whilst still predicting
    ill-formed spans in addition to the correct spans. This function works properly when
    the spans are unlabeled (i.e., your labels are simply "B", "I", and "O").

    Parameters
    ----------
    tag_sequence : List[str], required.
        The integer class labels for a sequence.
    classes_to_ignore : List[str], optional (default = None).
        A list of string class labels `excluding` the bio tag
        which should be ignored when extracting spans.

    Returns
    -------
    spans : List[TypedStringSpan]
        The typed, extracted spans from the sequence, in the format (label, (span_start, span_end)).
        Note that the label `does not` contain any BIO tag prefixes.
    """
    classes_to_ignore = classes_to_ignore or []
    spans: Set[Tuple[str, Tuple[int, int]]] = set()
    span_start = 0
    span_end = 0
    active_conll_tag = None
    for index, string_tag in enumerate(tag_sequence):
        # Actual BIO tag.
        bio_tag = string_tag[0]
        if bio_tag not in ["B", "I", "O"]:
            raise InvalidTagSequence(tag_sequence)
        conll_tag = string_tag[2:]
        if bio_tag == "O" or conll_tag in classes_to_ignore:
            # The span has ended.
            if active_conll_tag is not None:
                spans.add((active_conll_tag, (span_start, span_end)))
            active_conll_tag = None
            # We don't care about tags we are
            # told to ignore, so we do nothing.
            continue
        elif bio_tag == "B":
            # We are entering a new span; reset indices
            # and active tag to new span.
            if active_conll_tag is not None:
                spans.add((active_conll_tag, (span_start, span_end)))
            active_conll_tag = conll_tag
            span_start = index
            span_end = index
        elif bio_tag == "I" and conll_tag == active_conll_tag:
            # We're inside a span.
            span_end += 1
        else:
            # This is the case the bio label is an "I", but either:
            # 1) the span hasn't started - i.e. an ill formed span.
            # 2) The span is an I tag for a different conll annotation.
            # We'll process the previous span if it exists, but also
            # include this span. This is important, because otherwise,
            # a model may get a perfect F1 score whilst still including
            # false positive ill-formed spans.
            if active_conll_tag is not None:
                spans.add((active_conll_tag, (span_start, span_end)))
            active_conll_tag = conll_tag
            span_start = index
            span_end = index
    # Last token might have been a part of a valid span.
    if active_conll_tag is not None:
        spans.add((active_conll_tag, (span_start, span_end)))
    return list(spans)